multiobjective path planning has Increasing demand in military missions, rescue operations, construction job-sites.
There is Lack of robotic path planning algorithm that compromises multiple
objectives. Commonly no solution that optimizes all the objective functions. Here we modify RRT, RRT* sampling based algorithm.
Probabilistic Matrix Factorization (PMF)
Bayesian Probabilistic Matrix Factorization (BPMF) using
Markov Chain Monte Carlo (MCMC)
BPMF using MCMC – Overall Model
BPMF using MCMC – Gibbs Sampling
Here a Review of the Combination of Machine Learning models from Bayesian Averaging, Committees to Boosting... Specifically An statistical analysis of Boosting is done
In this work, we propose to apply trust region optimization to deep reinforcement
learning using a recently proposed Kronecker-factored approximation to
the curvature. We extend the framework of natural policy gradient and propose
to optimize both the actor and the critic using Kronecker-factored approximate
curvature (K-FAC) with trust region; hence we call our method Actor Critic using
Kronecker-Factored Trust Region (ACKTR). To the best of our knowledge, this
is the first scalable trust region natural gradient method for actor-critic methods.
It is also a method that learns non-trivial tasks in continuous control as well as
discrete control policies directly from raw pixel inputs. We tested our approach
across discrete domains in Atari games as well as continuous domains in the MuJoCo
environment. With the proposed methods, we are able to achieve higher
rewards and a 2- to 3-fold improvement in sample efficiency on average, compared
to previous state-of-the-art on-policy actor-critic methods. Code is available at
https://github.com/openai/baselines.
Probabilistic Matrix Factorization (PMF)
Bayesian Probabilistic Matrix Factorization (BPMF) using
Markov Chain Monte Carlo (MCMC)
BPMF using MCMC – Overall Model
BPMF using MCMC – Gibbs Sampling
Here a Review of the Combination of Machine Learning models from Bayesian Averaging, Committees to Boosting... Specifically An statistical analysis of Boosting is done
In this work, we propose to apply trust region optimization to deep reinforcement
learning using a recently proposed Kronecker-factored approximation to
the curvature. We extend the framework of natural policy gradient and propose
to optimize both the actor and the critic using Kronecker-factored approximate
curvature (K-FAC) with trust region; hence we call our method Actor Critic using
Kronecker-Factored Trust Region (ACKTR). To the best of our knowledge, this
is the first scalable trust region natural gradient method for actor-critic methods.
It is also a method that learns non-trivial tasks in continuous control as well as
discrete control policies directly from raw pixel inputs. We tested our approach
across discrete domains in Atari games as well as continuous domains in the MuJoCo
environment. With the proposed methods, we are able to achieve higher
rewards and a 2- to 3-fold improvement in sample efficiency on average, compared
to previous state-of-the-art on-policy actor-critic methods. Code is available at
https://github.com/openai/baselines.
The retrieval algorithms in remote sensing generally involve complex physical forward models that are nonlinear and computationally expensive to evaluate. Statistical emulation provides an alternative with cheap computation and can be used to calibrate model parameters and to improve computational efficiency of the retrieval algorithms. We introduce a framework of combining dimension reduction of input and output spaces and Gaussian process emulation technique. The functional principal component analysis (FPCA) is chosen to reduce to the output space of thousands of dimensions by orders of magnitude. In addition, instead of making restrictive assumptions regarding the correlation structure of the high-dimensional input space, we identity and exploit the most important directions of this space and thus construct a Gaussian process emulator with feasible computation. We will present preliminary results obtained from applying our method to OCO-2 data, and discuss how our framework can be generalized in distributed systems.
A Minimum Spanning Tree Approach of Solving a Transportation Probleminventionjournals
: This work centered on the transportation problem in the shipment of cable troughs for an underground cable installation from three supply ends to four locations at a construction site where they are needed; in which case, we sought to minimize the cost of shipment. The problem was modeled into a bipartite network representation and solved using the Kruskal method of minimum spanning tree; after which the solution was confirmed with TORA Optimization software version 2.00. The result showed that the cost obtained in shipping the cable troughs under the application of the method, which was AED 2,022,000 (in the United Arab Emirate Dollar), was more effective than that obtained from mere heuristics when compared.
Abstract : Motivated by the recovery and prediction of electricity consumption time series, we extend Nonnegative Matrix Factorization to take into account external features as side information. We consider general linear measurement settings, and propose a framework which models non-linear relationships between external features and the response variable. We extend previous theoretical results to obtain a sufficient condition on the identifiability of NMF with side information. Based on the classical Hierarchical Alternating Least Squares (HALS) algorithm, we propose a new algorithm (HALSX, or Hierarchical Alternating Least Squares with eXogeneous variables) which estimates NMF in this setting. The algorithm is validated on both simulated and real electricity consumption datasets as well as a recommendation system dataset, to show its performance in matrix recovery and prediction for new rows and columns.
We give a modified version of a heuristic, available in the relevant literature, of the capacitated facility
location problem. A numerical experiment is performed to compare the two heuristics. The study would
help to design heuristics for different generalizations of the problem.
Andres hernandez ai_machine_learning_london_nov2017Andres Hernandez
My slides from the AI & Machine Learning in Quantitative Finance conference in London. I train a neural network to train another neural network to optimize particular black boxes
An improved spfa algorithm for single source shortest path problem using forw...IJMIT JOURNAL
We present an improved SPFA algorithm for the single source shortest path problem. For a random graph,
the empirical average time complexity is O(|E|), where |E| is the number of edges of the input network.
SPFA maintains a queue of candidate vertices and add a vertex to the queue only if that vertex is relaxed.
In the improved SPFA, MinPoP principle is employed to improve the quality of the queue. We theoretically
analyse the advantage of this new algorithm and experimentally demonstrate that the algorithm is efficient.
International Journal of Managing Information Technology (IJMIT)IJMIT JOURNAL
We present an improved SPFA algorithm for the single source shortest path problem. For a random graph, the empirical average time complexity is O(|E|), where |E| is the number of edges of the input network. SPFA maintains a queue of candidate vertices and add a vertex to the queue only if that vertex is relaxed. In the improved SPFA, MinPoP principle is employed to improve the quality of the queue. We theoretically analyse the advantage of this new algorithm and experimentally demonstrate that the algorithm is efficient
An improved spfa algorithm for single source shortest path problem using forw...IJMIT JOURNAL
We present an improved SPFA algorithm for the single source shortest path problem. For a random graph,
the empirical average time complexity is O(|E|), where |E| is the number of edges of the input network.
SPFA maintains a queue of candidate vertices and add a vertex to the queue only if that vertex is relaxed.
In the improved SPFA, MinPoP principle is employed to improve the quality of the queue. We theoretically
analyse the advantage of this new algorithm and experimentally demonstrate that the algorithm is efficient.
Simulators play a major role in analyzing multi-modal transportation networks. As complexity of simulators increases, development of calibration procedures is becoming an increasingly challenging task. Current calibration procedures often rely on heuristics, rules of thumb and sometimes on brute-force search. In this talk we consider a statistical framework for calibration that relies on Bayesian optimization. Bayesian optimization treats the simulator as a sample from a Gaussian process (GP). Tractability and sample efficiency of Gaussian processes enable computationally efficient algorithms for calibration problems. We show how the choice of prior and inference algorithm effect the outcome of our optimization procedure. We develop dimensionality reduction techniques that allow for our optimization techniques to be applicable for real-life problems. We develop a distributed, Gaussian Process Bayesian regression and active learning models. We demonstrate those to calibrate ground transportation simulation models.
We compute a low-rank surrogate (response surface) approximation to the solution of stochastic PDE. This is a Karhunen-Loeve/polynomial chaos approximation. After that, to compute required statistics, we sample this cheap surrogate, avoiding very expensive solution of the deterministic problem.
The Shortest Path Tour Problem is an extension to the normal Shortest Path Problem and appeared in the scientific literature in Bertsekas's dynamic programming and optimal control book in 2005, for the first time. This paper gives a description of the problem, two algorithms to solve it. Results to the numeric experimentation are given in terms of graphs. Finally, conclusion and discussions are made.
A Minimum Spanning Tree Approach of Solving a Transportation Probleminventionjournals
: This work centered on the transportation problem in the shipment of cable troughs for an underground cable installation from three supply ends to four locations at a construction site where they are needed; in which case, we sought to minimize the cost of shipment. The problem was modeled into a bipartite network representation and solved using the Kruskal method of minimum spanning tree; after which the solution was confirmed with TORA Optimization software version 2.00. The result showed that the cost obtained in shipping the cable troughs under the application of the method, which was AED 2,022,000 (in the United Arab Emirate Dollar), was more effective than that obtained from mere heuristics when compared
A Hough Transform Based On a Map-Reduce AlgorithmIJERA Editor
This paper presents a method that proposes the composition of the Map-Reduce algorithm and the Hough
Transform method to research particular features of shape in the Big Data of images. We introduce the first
formal translation of the Hough Transform method into the Map-Reduce pattern. The Hough transform is
applied to one image or to several images in parallel. The context of the application of this method concerns Big
Data that requires Map-Reduce functions to improve the processing time and the need of object detection in
noisy pictures with the Hough Transform method.
The retrieval algorithms in remote sensing generally involve complex physical forward models that are nonlinear and computationally expensive to evaluate. Statistical emulation provides an alternative with cheap computation and can be used to calibrate model parameters and to improve computational efficiency of the retrieval algorithms. We introduce a framework of combining dimension reduction of input and output spaces and Gaussian process emulation technique. The functional principal component analysis (FPCA) is chosen to reduce to the output space of thousands of dimensions by orders of magnitude. In addition, instead of making restrictive assumptions regarding the correlation structure of the high-dimensional input space, we identity and exploit the most important directions of this space and thus construct a Gaussian process emulator with feasible computation. We will present preliminary results obtained from applying our method to OCO-2 data, and discuss how our framework can be generalized in distributed systems.
A Minimum Spanning Tree Approach of Solving a Transportation Probleminventionjournals
: This work centered on the transportation problem in the shipment of cable troughs for an underground cable installation from three supply ends to four locations at a construction site where they are needed; in which case, we sought to minimize the cost of shipment. The problem was modeled into a bipartite network representation and solved using the Kruskal method of minimum spanning tree; after which the solution was confirmed with TORA Optimization software version 2.00. The result showed that the cost obtained in shipping the cable troughs under the application of the method, which was AED 2,022,000 (in the United Arab Emirate Dollar), was more effective than that obtained from mere heuristics when compared.
Abstract : Motivated by the recovery and prediction of electricity consumption time series, we extend Nonnegative Matrix Factorization to take into account external features as side information. We consider general linear measurement settings, and propose a framework which models non-linear relationships between external features and the response variable. We extend previous theoretical results to obtain a sufficient condition on the identifiability of NMF with side information. Based on the classical Hierarchical Alternating Least Squares (HALS) algorithm, we propose a new algorithm (HALSX, or Hierarchical Alternating Least Squares with eXogeneous variables) which estimates NMF in this setting. The algorithm is validated on both simulated and real electricity consumption datasets as well as a recommendation system dataset, to show its performance in matrix recovery and prediction for new rows and columns.
We give a modified version of a heuristic, available in the relevant literature, of the capacitated facility
location problem. A numerical experiment is performed to compare the two heuristics. The study would
help to design heuristics for different generalizations of the problem.
Andres hernandez ai_machine_learning_london_nov2017Andres Hernandez
My slides from the AI & Machine Learning in Quantitative Finance conference in London. I train a neural network to train another neural network to optimize particular black boxes
An improved spfa algorithm for single source shortest path problem using forw...IJMIT JOURNAL
We present an improved SPFA algorithm for the single source shortest path problem. For a random graph,
the empirical average time complexity is O(|E|), where |E| is the number of edges of the input network.
SPFA maintains a queue of candidate vertices and add a vertex to the queue only if that vertex is relaxed.
In the improved SPFA, MinPoP principle is employed to improve the quality of the queue. We theoretically
analyse the advantage of this new algorithm and experimentally demonstrate that the algorithm is efficient.
International Journal of Managing Information Technology (IJMIT)IJMIT JOURNAL
We present an improved SPFA algorithm for the single source shortest path problem. For a random graph, the empirical average time complexity is O(|E|), where |E| is the number of edges of the input network. SPFA maintains a queue of candidate vertices and add a vertex to the queue only if that vertex is relaxed. In the improved SPFA, MinPoP principle is employed to improve the quality of the queue. We theoretically analyse the advantage of this new algorithm and experimentally demonstrate that the algorithm is efficient
An improved spfa algorithm for single source shortest path problem using forw...IJMIT JOURNAL
We present an improved SPFA algorithm for the single source shortest path problem. For a random graph,
the empirical average time complexity is O(|E|), where |E| is the number of edges of the input network.
SPFA maintains a queue of candidate vertices and add a vertex to the queue only if that vertex is relaxed.
In the improved SPFA, MinPoP principle is employed to improve the quality of the queue. We theoretically
analyse the advantage of this new algorithm and experimentally demonstrate that the algorithm is efficient.
Simulators play a major role in analyzing multi-modal transportation networks. As complexity of simulators increases, development of calibration procedures is becoming an increasingly challenging task. Current calibration procedures often rely on heuristics, rules of thumb and sometimes on brute-force search. In this talk we consider a statistical framework for calibration that relies on Bayesian optimization. Bayesian optimization treats the simulator as a sample from a Gaussian process (GP). Tractability and sample efficiency of Gaussian processes enable computationally efficient algorithms for calibration problems. We show how the choice of prior and inference algorithm effect the outcome of our optimization procedure. We develop dimensionality reduction techniques that allow for our optimization techniques to be applicable for real-life problems. We develop a distributed, Gaussian Process Bayesian regression and active learning models. We demonstrate those to calibrate ground transportation simulation models.
We compute a low-rank surrogate (response surface) approximation to the solution of stochastic PDE. This is a Karhunen-Loeve/polynomial chaos approximation. After that, to compute required statistics, we sample this cheap surrogate, avoiding very expensive solution of the deterministic problem.
The Shortest Path Tour Problem is an extension to the normal Shortest Path Problem and appeared in the scientific literature in Bertsekas's dynamic programming and optimal control book in 2005, for the first time. This paper gives a description of the problem, two algorithms to solve it. Results to the numeric experimentation are given in terms of graphs. Finally, conclusion and discussions are made.
A Minimum Spanning Tree Approach of Solving a Transportation Probleminventionjournals
: This work centered on the transportation problem in the shipment of cable troughs for an underground cable installation from three supply ends to four locations at a construction site where they are needed; in which case, we sought to minimize the cost of shipment. The problem was modeled into a bipartite network representation and solved using the Kruskal method of minimum spanning tree; after which the solution was confirmed with TORA Optimization software version 2.00. The result showed that the cost obtained in shipping the cable troughs under the application of the method, which was AED 2,022,000 (in the United Arab Emirate Dollar), was more effective than that obtained from mere heuristics when compared
A Hough Transform Based On a Map-Reduce AlgorithmIJERA Editor
This paper presents a method that proposes the composition of the Map-Reduce algorithm and the Hough
Transform method to research particular features of shape in the Big Data of images. We introduce the first
formal translation of the Hough Transform method into the Map-Reduce pattern. The Hough transform is
applied to one image or to several images in parallel. The context of the application of this method concerns Big
Data that requires Map-Reduce functions to improve the processing time and the need of object detection in
noisy pictures with the Hough Transform method.
The International Journal of Engineering and Science (The IJES)theijes
The International Journal of Engineering & Science is aimed at providing a platform for researchers, engineers, scientists, or educators to publish their original research results, to exchange new ideas, to disseminate information in innovative designs, engineering experiences and technological skills. It is also the Journal's objective to promote engineering and technology education. All papers submitted to the Journal will be blind peer-reviewed. Only original articles will be published.
Soft Computing Technique Based Enhancement of Transmission System Lodability ...IJERA Editor
Due to the growth of electricity demands and transactions in power markets, existing power networks need to be enhanced in order to increase their loadability. The problem of determining the best locations for network reinforcement can be formulated as a mixed discrete-continuous nonlinear optimization problem (MDCP). The complexity of the problem makes extensive simulations necessary and the computational requirement is high. This paper compares the effectiveness of Evolutionary Programming (EP) and an ordinal optimization (OO) technique is proposed in this paper to solve the MDCP involving two types of flexible ac transmission systems (FACTS) devices, namely static var compensator (SVC) and thyristor controlled series compensator (TCSC), for system loadability enhancement. In this approach, crude models are proposed to cope with the complexity of the problem and speed up the simulations with high alignment confidence. The test and Validation of the proposed algorithm are conducted on IEEE 14–bus system and 22-bus Indian system.Simulation results shows that the proposed models permit the use of OO-based approach for finding good enough solutions with less computational efforts.
International Journal of Engineering Research and Applications (IJERA) is an open access online peer reviewed international journal that publishes research and review articles in the fields of Computer Science, Neural Networks, Electrical Engineering, Software Engineering, Information Technology, Mechanical Engineering, Chemical Engineering, Plastic Engineering, Food Technology, Textile Engineering, Nano Technology & science, Power Electronics, Electronics & Communication Engineering, Computational mathematics, Image processing, Civil Engineering, Structural Engineering, Environmental Engineering, VLSI Testing & Low Power VLSI Design etc.
IJERA (International journal of Engineering Research and Applications) is International online, ... peer reviewed journal. For more detail or submit your article, please visit www.ijera.com
Hybrid Particle Swarm Optimization for Solving Multi-Area Economic Dispatch P...ijsc
We consider the Multi-Area Economic Dispatch problem (MAEDP) in deregulated power system environment for practical multi-area cases with tie line constraints. Our objective is to generate allocation to the power generators in such a manner that the total fuel cost is minimized while all operating constraints are satisfied. This problem is NP-hard. In this paper, we propose Hybrid Particle Swarm Optimization (HGAPSO) to solve MAEDP. The experimental results are reported to show the efficiency of proposed algorithms compared to Particle Swarm Optimization with Time-Varying Acceleration Coefficients (PSO-TVAC) and RCGA.
HYBRID PARTICLE SWARM OPTIMIZATION FOR SOLVING MULTI-AREA ECONOMIC DISPATCH P...ijsc
We consider the Multi-Area Economic Dispatch problem (MAEDP) in deregulated power system
environment for practical multi-area cases with tie line constraints. Our objective is to generate allocation
to the power generators in such a manner that the total fuel cost is minimized while all operating
constraints are satisfied. This problem is NP-hard. In this paper, we propose Hybrid Particle Swarm
Optimization (HGAPSO) to solve MAEDP. The experimental results are reported to show the efficiency of
proposed algorithms compared to Particle Swarm Optimization with Time-Varying Acceleration
Coefficients (PSO-TVAC) and RCGA.
Dynamic Economic Dispatch Assessment Using Particle Swarm Optimization TechniquejournalBEEI
This paper presents the application of particle swarm optimization (PSO) technique for solving the dynamic economic dispatch (DED) problem. The DED is one of the main functions in power system planning in order to obtain optimum power system operation and control. It determines the optimal operation of generating units at every predicted load demands over a certain period of time. The optimum operation of generating units is obtained by referring to the minimum total generation cost while the system is operating within its limits. The DED based PSO technique is tested on a 9-bus system containing of three generator bus, six load bus and twelve transmission lines.
Security constrained optimal load dispatch using hpso technique for thermal s...eSAT Publishing House
IJRET : International Journal of Research in Engineering and Technology is an international peer reviewed, online journal published by eSAT Publishing House for the enhancement of research in various disciplines of Engineering and Technology. The aim and scope of the journal is to provide an academic medium and an important reference for the advancement and dissemination of research results that support high-level learning, teaching and research in the fields of Engineering and Technology. We bring together Scientists, Academician, Field Engineers, Scholars and Students of related fields of Engineering and Technology.
Security constrained optimal load dispatch using hpso technique for thermal s...eSAT Journals
Abstract This paper presents Hybrid Particle Swarm Optimization (HPSO) technique to solve the Optimal Load Dispatch (OLD) problems with line flow constrain, bus voltage limits and generator operating constraints. In the proposed HPSO method both features of EP and PSO are incorporated, so the combined HPSO algorithm may become more effective to find the optimal solutions. In this paper, the proposed Hybrid PSO, PSO and EP techniques have been tested on IEEE14, 30 bus systems. Numerical simulation results show that the Hybrid PSO algorithm outperformed standard PSO algorithm and Evolution Programming method on the same problem and can save considerable cost of Optimal Load Dispatch.
Algorithm Finding Maximum Concurrent Multicommodity Linear Flow with Limited ...IJCNCJournal
Graphs and extended networks are is powerful mathematical tools applied in many fields as transportation,
communication, informatics, economy, … Algorithms to find Maximum Concurrent Multicommodity Flow
with Limited Cost on extended traffic networks are introduced in the works we did. However, with those
algorithms, capacities of two-sided lines are shared fully for two directions. This work studies the more
general and practical case, where flows are limited to use two-sided lines with a single parameter called
regulating coefficient. The algorithm is presented in the programming language Java. The algorithm is
coded in programming language Java with extended network database in database management system
MySQL and offers exact results.
International Journal of Engineering Research and Applications (IJERA) is an open access online peer reviewed international journal that publishes research and review articles in the fields of Computer Science, Neural Networks, Electrical Engineering, Software Engineering, Information Technology, Mechanical Engineering, Chemical Engineering, Plastic Engineering, Food Technology, Textile Engineering, Nano Technology & science, Power Electronics, Electronics & Communication Engineering, Computational mathematics, Image processing, Civil Engineering, Structural Engineering, Environmental Engineering, VLSI Testing & Low Power VLSI Design etc.
Similar to Sampling-Based Planning Algorithms for Multi-Objective Missions (20)
Establishing Line-of-Sight Communication Via Autonomous Relay VehiclesMd Mahbubur Rahman
This is the presentation of our paper,
"Establishing Line-of-Sight Communication Via Autonomous Relay Vehicles, IEEE Military Communication Conference, Baltimore, MD, 2016"
Line-of-sight(LoS) communication (by infrared or visible light) becomes a reliable ways to send information between mobile units in communication-denied environments.
This form of communication is more difficult to intercept or jam, as an attacker would require to be located directly on that LoS.
Mission-related movements may break a fully connected military mission by losing LoS to the Service Vehicles.
Autonomous ground vehicle can recover the LoS based connectivity by moving from place to place as required.
Relay Vehicle Formations for Optimizing Communication Quality in Robot NetworksMd Mahbubur Rahman
Communication relay has vital importance in military, mining,
surveillance and rescue missions, where robots are remotely
controlled by an operator (e.g, drone) who stays in a safe location.
Problem: Wireless signal over distance degrades. Obstacles, terrain,
weather condition further attenuates the signal.
Solution: Relay robots are placed in between the operator and
remotely placed robotic units.
A Coupled Discrete-Event and Motion Planning Methodology for Automated Safety...Md Mahbubur Rahman
Struck-by accidents are one of the most deadly hazards found on
construction jobsites. But there is very few automated and proactive
system to calculate the hazardous zones.
Motion planning techniques can be used to estimate the safe
trajectories.
Discrete event system specification is useful to simulate the
construction job in virtual environment.
Ex-Ante Assessment of Struck-by Safety Hazards in Construction Projects: A Mo...Md Mahbubur Rahman
Struck-by accidents are one of the most deadly hazards found on
construction jobsites. But there is very few automated system to
calculate the hazardous zones.
Our target is to apply motion planning techniques to generate a safe
roadmap and produce a set of directions to the worker to avoid
collisions.
Introduction:
RNA interference (RNAi) or Post-Transcriptional Gene Silencing (PTGS) is an important biological process for modulating eukaryotic gene expression.
It is highly conserved process of posttranscriptional gene silencing by which double stranded RNA (dsRNA) causes sequence-specific degradation of mRNA sequences.
dsRNA-induced gene silencing (RNAi) is reported in a wide range of eukaryotes ranging from worms, insects, mammals and plants.
This process mediates resistance to both endogenous parasitic and exogenous pathogenic nucleic acids, and regulates the expression of protein-coding genes.
What are small ncRNAs?
micro RNA (miRNA)
short interfering RNA (siRNA)
Properties of small non-coding RNA:
Involved in silencing mRNA transcripts.
Called “small” because they are usually only about 21-24 nucleotides long.
Synthesized by first cutting up longer precursor sequences (like the 61nt one that Lee discovered).
Silence an mRNA by base pairing with some sequence on the mRNA.
Discovery of siRNA?
The first small RNA:
In 1993 Rosalind Lee (Victor Ambros lab) was studying a non- coding gene in C. elegans, lin-4, that was involved in silencing of another gene, lin-14, at the appropriate time in the
development of the worm C. elegans.
Two small transcripts of lin-4 (22nt and 61nt) were found to be complementary to a sequence in the 3' UTR of lin-14.
Because lin-4 encoded no protein, she deduced that it must be these transcripts that are causing the silencing by RNA-RNA interactions.
Types of RNAi ( non coding RNA)
MiRNA
Length (23-25 nt)
Trans acting
Binds with target MRNA in mismatch
Translation inhibition
Si RNA
Length 21 nt.
Cis acting
Bind with target Mrna in perfect complementary sequence
Piwi-RNA
Length ; 25 to 36 nt.
Expressed in Germ Cells
Regulates trnasposomes activity
MECHANISM OF RNAI:
First the double-stranded RNA teams up with a protein complex named Dicer, which cuts the long RNA into short pieces.
Then another protein complex called RISC (RNA-induced silencing complex) discards one of the two RNA strands.
The RISC-docked, single-stranded RNA then pairs with the homologous mRNA and destroys it.
THE RISC COMPLEX:
RISC is large(>500kD) RNA multi- protein Binding complex which triggers MRNA degradation in response to MRNA
Unwinding of double stranded Si RNA by ATP independent Helicase
Active component of RISC is Ago proteins( ENDONUCLEASE) which cleave target MRNA.
DICER: endonuclease (RNase Family III)
Argonaute: Central Component of the RNA-Induced Silencing Complex (RISC)
One strand of the dsRNA produced by Dicer is retained in the RISC complex in association with Argonaute
ARGONAUTE PROTEIN :
1.PAZ(PIWI/Argonaute/ Zwille)- Recognition of target MRNA
2.PIWI (p-element induced wimpy Testis)- breaks Phosphodiester bond of mRNA.)RNAse H activity.
MiRNA:
The Double-stranded RNAs are naturally produced in eukaryotic cells during development, and they have a key role in regulating gene expression .
Cancer cell metabolism: special Reference to Lactate PathwayAADYARAJPANDEY1
Normal Cell Metabolism:
Cellular respiration describes the series of steps that cells use to break down sugar and other chemicals to get the energy we need to function.
Energy is stored in the bonds of glucose and when glucose is broken down, much of that energy is released.
Cell utilize energy in the form of ATP.
The first step of respiration is called glycolysis. In a series of steps, glycolysis breaks glucose into two smaller molecules - a chemical called pyruvate. A small amount of ATP is formed during this process.
Most healthy cells continue the breakdown in a second process, called the Kreb's cycle. The Kreb's cycle allows cells to “burn” the pyruvates made in glycolysis to get more ATP.
The last step in the breakdown of glucose is called oxidative phosphorylation (Ox-Phos).
It takes place in specialized cell structures called mitochondria. This process produces a large amount of ATP. Importantly, cells need oxygen to complete oxidative phosphorylation.
If a cell completes only glycolysis, only 2 molecules of ATP are made per glucose. However, if the cell completes the entire respiration process (glycolysis - Kreb's - oxidative phosphorylation), about 36 molecules of ATP are created, giving it much more energy to use.
IN CANCER CELL:
Unlike healthy cells that "burn" the entire molecule of sugar to capture a large amount of energy as ATP, cancer cells are wasteful.
Cancer cells only partially break down sugar molecules. They overuse the first step of respiration, glycolysis. They frequently do not complete the second step, oxidative phosphorylation.
This results in only 2 molecules of ATP per each glucose molecule instead of the 36 or so ATPs healthy cells gain. As a result, cancer cells need to use a lot more sugar molecules to get enough energy to survive.
Unlike healthy cells that "burn" the entire molecule of sugar to capture a large amount of energy as ATP, cancer cells are wasteful.
Cancer cells only partially break down sugar molecules. They overuse the first step of respiration, glycolysis. They frequently do not complete the second step, oxidative phosphorylation.
This results in only 2 molecules of ATP per each glucose molecule instead of the 36 or so ATPs healthy cells gain. As a result, cancer cells need to use a lot more sugar molecules to get enough energy to survive.
introduction to WARBERG PHENOMENA:
WARBURG EFFECT Usually, cancer cells are highly glycolytic (glucose addiction) and take up more glucose than do normal cells from outside.
Otto Heinrich Warburg (; 8 October 1883 – 1 August 1970) In 1931 was awarded the Nobel Prize in Physiology for his "discovery of the nature and mode of action of the respiratory enzyme.
WARNBURG EFFECT : cancer cells under aerobic (well-oxygenated) conditions to metabolize glucose to lactate (aerobic glycolysis) is known as the Warburg effect. Warburg made the observation that tumor slices consume glucose and secrete lactate at a higher rate than normal tissues.
Richard's entangled aventures in wonderlandRichard Gill
Since the loophole-free Bell experiments of 2020 and the Nobel prizes in physics of 2022, critics of Bell's work have retreated to the fortress of super-determinism. Now, super-determinism is a derogatory word - it just means "determinism". Palmer, Hance and Hossenfelder argue that quantum mechanics and determinism are not incompatible, using a sophisticated mathematical construction based on a subtle thinning of allowed states and measurements in quantum mechanics, such that what is left appears to make Bell's argument fail, without altering the empirical predictions of quantum mechanics. I think however that it is a smoke screen, and the slogan "lost in math" comes to my mind. I will discuss some other recent disproofs of Bell's theorem using the language of causality based on causal graphs. Causal thinking is also central to law and justice. I will mention surprising connections to my work on serial killer nurse cases, in particular the Dutch case of Lucia de Berk and the current UK case of Lucy Letby.
Richard's aventures in two entangled wonderlandsRichard Gill
Since the loophole-free Bell experiments of 2020 and the Nobel prizes in physics of 2022, critics of Bell's work have retreated to the fortress of super-determinism. Now, super-determinism is a derogatory word - it just means "determinism". Palmer, Hance and Hossenfelder argue that quantum mechanics and determinism are not incompatible, using a sophisticated mathematical construction based on a subtle thinning of allowed states and measurements in quantum mechanics, such that what is left appears to make Bell's argument fail, without altering the empirical predictions of quantum mechanics. I think however that it is a smoke screen, and the slogan "lost in math" comes to my mind. I will discuss some other recent disproofs of Bell's theorem using the language of causality based on causal graphs. Causal thinking is also central to law and justice. I will mention surprising connections to my work on serial killer nurse cases, in particular the Dutch case of Lucia de Berk and the current UK case of Lucy Letby.
Slide 1: Title Slide
Extrachromosomal Inheritance
Slide 2: Introduction to Extrachromosomal Inheritance
Definition: Extrachromosomal inheritance refers to the transmission of genetic material that is not found within the nucleus.
Key Components: Involves genes located in mitochondria, chloroplasts, and plasmids.
Slide 3: Mitochondrial Inheritance
Mitochondria: Organelles responsible for energy production.
Mitochondrial DNA (mtDNA): Circular DNA molecule found in mitochondria.
Inheritance Pattern: Maternally inherited, meaning it is passed from mothers to all their offspring.
Diseases: Examples include Leber’s hereditary optic neuropathy (LHON) and mitochondrial myopathy.
Slide 4: Chloroplast Inheritance
Chloroplasts: Organelles responsible for photosynthesis in plants.
Chloroplast DNA (cpDNA): Circular DNA molecule found in chloroplasts.
Inheritance Pattern: Often maternally inherited in most plants, but can vary in some species.
Examples: Variegation in plants, where leaf color patterns are determined by chloroplast DNA.
Slide 5: Plasmid Inheritance
Plasmids: Small, circular DNA molecules found in bacteria and some eukaryotes.
Features: Can carry antibiotic resistance genes and can be transferred between cells through processes like conjugation.
Significance: Important in biotechnology for gene cloning and genetic engineering.
Slide 6: Mechanisms of Extrachromosomal Inheritance
Non-Mendelian Patterns: Do not follow Mendel’s laws of inheritance.
Cytoplasmic Segregation: During cell division, organelles like mitochondria and chloroplasts are randomly distributed to daughter cells.
Heteroplasmy: Presence of more than one type of organellar genome within a cell, leading to variation in expression.
Slide 7: Examples of Extrachromosomal Inheritance
Four O’clock Plant (Mirabilis jalapa): Shows variegated leaves due to different cpDNA in leaf cells.
Petite Mutants in Yeast: Result from mutations in mitochondrial DNA affecting respiration.
Slide 8: Importance of Extrachromosomal Inheritance
Evolution: Provides insight into the evolution of eukaryotic cells.
Medicine: Understanding mitochondrial inheritance helps in diagnosing and treating mitochondrial diseases.
Agriculture: Chloroplast inheritance can be used in plant breeding and genetic modification.
Slide 9: Recent Research and Advances
Gene Editing: Techniques like CRISPR-Cas9 are being used to edit mitochondrial and chloroplast DNA.
Therapies: Development of mitochondrial replacement therapy (MRT) for preventing mitochondrial diseases.
Slide 10: Conclusion
Summary: Extrachromosomal inheritance involves the transmission of genetic material outside the nucleus and plays a crucial role in genetics, medicine, and biotechnology.
Future Directions: Continued research and technological advancements hold promise for new treatments and applications.
Slide 11: Questions and Discussion
Invite Audience: Open the floor for any questions or further discussion on the topic.
The increased availability of biomedical data, particularly in the public domain, offers the opportunity to better understand human health and to develop effective therapeutics for a wide range of unmet medical needs. However, data scientists remain stymied by the fact that data remain hard to find and to productively reuse because data and their metadata i) are wholly inaccessible, ii) are in non-standard or incompatible representations, iii) do not conform to community standards, and iv) have unclear or highly restricted terms and conditions that preclude legitimate reuse. These limitations require a rethink on data can be made machine and AI-ready - the key motivation behind the FAIR Guiding Principles. Concurrently, while recent efforts have explored the use of deep learning to fuse disparate data into predictive models for a wide range of biomedical applications, these models often fail even when the correct answer is already known, and fail to explain individual predictions in terms that data scientists can appreciate. These limitations suggest that new methods to produce practical artificial intelligence are still needed.
In this talk, I will discuss our work in (1) building an integrative knowledge infrastructure to prepare FAIR and "AI-ready" data and services along with (2) neurosymbolic AI methods to improve the quality of predictions and to generate plausible explanations. Attention is given to standards, platforms, and methods to wrangle knowledge into simple, but effective semantic and latent representations, and to make these available into standards-compliant and discoverable interfaces that can be used in model building, validation, and explanation. Our work, and those of others in the field, creates a baseline for building trustworthy and easy to deploy AI models in biomedicine.
Bio
Dr. Michel Dumontier is the Distinguished Professor of Data Science at Maastricht University, founder and executive director of the Institute of Data Science, and co-founder of the FAIR (Findable, Accessible, Interoperable and Reusable) data principles. His research explores socio-technological approaches for responsible discovery science, which includes collaborative multi-modal knowledge graphs, privacy-preserving distributed data mining, and AI methods for drug discovery and personalized medicine. His work is supported through the Dutch National Research Agenda, the Netherlands Organisation for Scientific Research, Horizon Europe, the European Open Science Cloud, the US National Institutes of Health, and a Marie-Curie Innovative Training Network. He is the editor-in-chief for the journal Data Science and is internationally recognized for his contributions in bioinformatics, biomedical informatics, and semantic technologies including ontologies and linked data.
This pdf is about the Schizophrenia.
For more details visit on YouTube; @SELF-EXPLANATORY;
https://www.youtube.com/channel/UCAiarMZDNhe1A3Rnpr_WkzA/videos
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Seminar of U.V. Spectroscopy by SAMIR PANDASAMIR PANDA
Spectroscopy is a branch of science dealing the study of interaction of electromagnetic radiation with matter.
Ultraviolet-visible spectroscopy refers to absorption spectroscopy or reflect spectroscopy in the UV-VIS spectral region.
Ultraviolet-visible spectroscopy is an analytical method that can measure the amount of light received by the analyte.
Earliest Galaxies in the JADES Origins Field: Luminosity Function and Cosmic ...Sérgio Sacani
We characterize the earliest galaxy population in the JADES Origins Field (JOF), the deepest
imaging field observed with JWST. We make use of the ancillary Hubble optical images (5 filters
spanning 0.4−0.9µm) and novel JWST images with 14 filters spanning 0.8−5µm, including 7 mediumband filters, and reaching total exposure times of up to 46 hours per filter. We combine all our data
at > 2.3µm to construct an ultradeep image, reaching as deep as ≈ 31.4 AB mag in the stack and
30.3-31.0 AB mag (5σ, r = 0.1” circular aperture) in individual filters. We measure photometric
redshifts and use robust selection criteria to identify a sample of eight galaxy candidates at redshifts
z = 11.5 − 15. These objects show compact half-light radii of R1/2 ∼ 50 − 200pc, stellar masses of
M⋆ ∼ 107−108M⊙, and star-formation rates of SFR ∼ 0.1−1 M⊙ yr−1
. Our search finds no candidates
at 15 < z < 20, placing upper limits at these redshifts. We develop a forward modeling approach to
infer the properties of the evolving luminosity function without binning in redshift or luminosity that
marginalizes over the photometric redshift uncertainty of our candidate galaxies and incorporates the
impact of non-detections. We find a z = 12 luminosity function in good agreement with prior results,
and that the luminosity function normalization and UV luminosity density decline by a factor of ∼ 2.5
from z = 12 to z = 14. We discuss the possible implications of our results in the context of theoretical
models for evolution of the dark matter halo mass function.
A brief information about the SCOP protein database used in bioinformatics.
The Structural Classification of Proteins (SCOP) database is a comprehensive and authoritative resource for the structural and evolutionary relationships of proteins. It provides a detailed and curated classification of protein structures, grouping them into families, superfamilies, and folds based on their structural and sequence similarities.
In silico drugs analogue design: novobiocin analogues.pptx
Sampling-Based Planning Algorithms for Multi-Objective Missions
1. 1 / 28
Sampling-Based Planning Algorithms for
Multi-Objective Missions
Md Mahbubur Rahman1, Leonardo Bobadilla1, Brian Rapp2
Florida International University1
Army Research Lab2
August 23, 2016
3. Motivation
Introduction
Motivation
Problem Definition
Related Work
Problem Statement
Methods
Experiment
Conclusions and Future
Work
3 / 28
Increasing demand in military missions, rescue operations,
construction job-sites.
Lack of robotic path planning algorithm that compromises multiple
objectives.
Commonly no solution that optimizes all the objective functions.
Military Mission Construction Work
4. Problem Definition
Introduction
Motivation
Problem Definition
Related Work
Problem Statement
Methods
Experiment
Conclusions and Future
Work
4 / 28
Generate a path that:
Best optimizes multiple cost functions instead of one such as
conserves fuel, provides a comfortable ride, avoids locations with a
high risk.
Avoids obstacles and respect differential constraints.
Optimal in terms of additive and non-additive costs.
6. Multi-Optimization State of Art
Introduction
Related Work
Multi-Optimization
State of Art
Problem Statement
Methods
Experiment
Conclusions and Future
Work
6 / 28
Modified Bellman-Ford method (Dyer ’92) in a known graph that
assigns a normalized label to each node to find a multi-criteria
shortest path.
Prioritizes one objective over another (K. Fujimura ’96). This type of
hierarchization biases the path mostly towards the top priority
objective.
Scalarization of objectives where the objectives are weighted
(Tarapata ’07, Oleiwi ’14). Exact weights are hard to find.
A number of Pareto Path using RRT* forest (Yi and Goodrich ’15)
instead of a single path.
7. Problem Statement
Introduction
Related Work
Problem Statement
Problem Formulation in
Motion Planning
Problem Formulation in
Motion
Planning(Contd.)
Problem 1:
Multi-objective optimal
Path
Problem 2:
Cooperative Path
Planning
Methods
Experiment
Conclusions and Future
Work
7 / 28
8. Problem Formulation in Motion Planning
Introduction
Related Work
Problem Statement
Problem Formulation in
Motion Planning
Problem Formulation in
Motion
Planning(Contd.)
Problem 1:
Multi-objective optimal
Path
Problem 2:
Cooperative Path
Planning
Methods
Experiment
Conclusions and Future
Work
8 / 28
A workspace, W, and a set of obstacles O; free space E = W O.
A number of robots A = {A1, A2, . . . , Ak} with configuration
defined as Ci = E × S1 for Ai.
robots have differential constraints such as,
˙xi = ui
s cos θ, ˙yi = ui
s sin θ, and ˙θi = ui
s
Li tan ui
φ.
ui
s is the forward speed and ui
φ is the steering angle.
Obstacles and Robots Differential Drive and States
9. Problem Formulation in Motion Planning(Contd.)
Introduction
Related Work
Problem Statement
Problem Formulation in
Motion Planning
Problem Formulation in
Motion
Planning(Contd.)
Problem 1:
Multi-objective optimal
Path
Problem 2:
Cooperative Path
Planning
Methods
Experiment
Conclusions and Future
Work
9 / 28
X be the state space for one vehicle where X = Ci.
Each state x ∈ X of the robot is associated with multiple objective
costs.
Vector of n cost functions L = {l1, l2, . . . , ln} are assigned to a
state x, where li : X → R≥0.
L(x) = (l1(x), l2(x), . . . , ln(x)) where x ∈ X (1)
10. Problem 1: Multi-objective optimal Path
Introduction
Related Work
Problem Statement
Problem Formulation in
Motion Planning
Problem Formulation in
Motion
Planning(Contd.)
Problem 1:
Multi-objective optimal
Path
Problem 2:
Cooperative Path
Planning
Methods
Experiment
Conclusions and Future
Work
10 / 28
Calculate σ : [0, t] → Xfree as an obstacle-free feasible trajectory
where Xfree is the set of collision free states.
σ starts from xI ∈ X and ends in a goal set XG ⊂ X.
σ must optimize L.
11. Problem 2: Cooperative Path Planning
Introduction
Related Work
Problem Statement
Problem Formulation in
Motion Planning
Problem Formulation in
Motion
Planning(Contd.)
Problem 1:
Multi-objective optimal
Path
Problem 2:
Cooperative Path
Planning
Methods
Experiment
Conclusions and Future
Work
11 / 28
Given a set of friendly robot vehicles A1, A2, . . . , Ak, with a common
cost lc, find a set of collision-free continuous trajectories,
σ1, σ2, . . . , σk, that solve Problem 1 and best optimize
lc : X1 × X2 × · · · × Xk → R≥0.
12. Methods
Introduction
Related Work
Problem Statement
Methods
Main Algorithm
Recap of Standard
RRT*
Our Modification to
RRT*
ChooseParent
Modification
ChooseParent
Modification (contd.)
Update Node Cost
Rewire Method
Modification
Cooperative Tree
Expansion
Experiment
Conclusions and Future
Work
12 / 28
13. Main Algorithm
Introduction
Related Work
Problem Statement
Methods
Main Algorithm
Recap of Standard
RRT*
Our Modification to
RRT*
ChooseParent
Modification
ChooseParent
Modification (contd.)
Update Node Cost
Rewire Method
Modification
Cooperative Tree
Expansion
Experiment
Conclusions and Future
Work
13 / 28
Modify Rapidly Exploring Random Tree star (RRT*) algorithm
(Karaman ’10) to adapt multiple costs.
Expand a tree T through random sampling of the states from the free
space similar to RRT*.
We completely modify the ChooseParent and Rewire methods.
We compute multiple cooperative trees in case of the presence of
multiple robots.
14. Recap of Standard RRT*
Introduction
Related Work
Problem Statement
Methods
Main Algorithm
Recap of Standard
RRT*
Our Modification to
RRT*
ChooseParent
Modification
ChooseParent
Modification (contd.)
Update Node Cost
Rewire Method
Modification
Cooperative Tree
Expansion
Experiment
Conclusions and Future
Work
14 / 28
Start a tree from the root state node xI.
Sampling: samples a random configuration xrand ∈ Xfree.
Nearest Node: xnearest = NearestNode(T , xrand) returns the
node xnearest of the tree T that is nearest to the sampled node xrand
in terms of a distance metric.
Steer: xnew = Steer(x1, x2) is used to solve control inputs us and
uφ for a dynamic control system.
Collision Checking: checks whether a path from xnew to xnearest
avoids all the obstacles O.
15. Our Modification to RRT*
Introduction
Related Work
Problem Statement
Methods
Main Algorithm
Recap of Standard
RRT*
Our Modification to
RRT*
ChooseParent
Modification
ChooseParent
Modification (contd.)
Update Node Cost
Rewire Method
Modification
Cooperative Tree
Expansion
Experiment
Conclusions and Future
Work
15 / 28
Choose Parent: Selects a parent for xnew in the radius r.
Re-wire: xnew now becomes the parent of a number of neighbors if
this gives lower cost.
16. ChooseParent Modification
Introduction
Related Work
Problem Statement
Methods
Main Algorithm
Recap of Standard
RRT*
Our Modification to
RRT*
ChooseParent
Modification
ChooseParent
Modification (contd.)
Update Node Cost
Rewire Method
Modification
Cooperative Tree
Expansion
Experiment
Conclusions and Future
Work
16 / 28
Select a set of nearest neighbors Xnear as potential parent.
Cost vector L(xj) of node xj is dominated by cost vector L(xi) of
node xi,
xi ≺ xj ⇔ ∀k, lk(xi) ≤ lk(xj); where 1 ≤ k ≤ n. (2)
Dominated Node: The set of dominated nodes DX is defined as,
DX = {xj ∈ Xnear|∃i xi ≺ xj}. (3)
Set of non-dominated nodes PX comprises the Pareto frontier,
PX = Xnear DX. (4)
17. ChooseParent Modification (contd.)
Introduction
Related Work
Problem Statement
Methods
Main Algorithm
Recap of Standard
RRT*
Our Modification to
RRT*
ChooseParent
Modification
ChooseParent
Modification (contd.)
Update Node Cost
Rewire Method
Modification
Cooperative Tree
Expansion
Experiment
Conclusions and Future
Work
17 / 28
Minimum cost tuple from PX ⊆ Xnear is,
L∗
= ( min
1≤i≤|PX |
l1(xi), . . . , min
1≤i≤|PX |
ln(xi)) (5)
Similarly Optimum cost tuple for arc costs is:
C∗
= ( min
1≤i≤|PX |
c1(xi, xnew), . . . , min
1≤i≤|PX |
cn(xi, xnew)) (6)
Select the parent xopt for node xnew with minimum normalized cost,
xopt = argmin
xj∈PX
n
i=1
αi
li(xj)
l∗
i
+
ci(xj, xnew)
c∗
i
(7)
18. Update Node Cost
Introduction
Related Work
Problem Statement
Methods
Main Algorithm
Recap of Standard
RRT*
Our Modification to
RRT*
ChooseParent
Modification
ChooseParent
Modification (contd.)
Update Node Cost
Rewire Method
Modification
Cooperative Tree
Expansion
Experiment
Conclusions and Future
Work
18 / 28
Additive costs la(xnew) like distance are allocated by combining the
cost of a parent and the arc cost,
la
i (xnew) = la
i (xopt) + ci(xopt, xnew); ∀ i, 1 ≤ i ≤ k. (8)
Non-additive costs lna (such as visibility) require in-place
computation. The parent’s cost and the current node’s cost are
averaged,
lna
j (xnew) =
lna
j (xopt) + lna
j (xnew)
2
; ∀ j, k + 1 ≤ j ≤ n. (9)
Add xnew to the tree T through the edge c(xopt, xnew).
19. Rewire Method Modification
Introduction
Related Work
Problem Statement
Methods
Main Algorithm
Recap of Standard
RRT*
Our Modification to
RRT*
ChooseParent
Modification
ChooseParent
Modification (contd.)
Update Node Cost
Rewire Method
Modification
Cooperative Tree
Expansion
Experiment
Conclusions and Future
Work
19 / 28
A neighbor x ∈ Xnear is connected through the newly added node
xnew if the following two conditions are satisfied:
1) updating connection reduce additive cost la(x).
2) xnew becomes a better parent than the existing parent in terms of
non-additive cost lna(x).
Therefore
Additive: ∀ i, 1 ≤ i ≤ k; la
i (xnew) + c(xnew, x) ≤ la
i (x) (10)
Non-Additive: ∀ j, k + 1 ≤ j ≤ n; lna
j (xnew) ≤ lna
j (x.parent)
(11)
20. Cooperative Tree Expansion
Introduction
Related Work
Problem Statement
Methods
Main Algorithm
Recap of Standard
RRT*
Our Modification to
RRT*
ChooseParent
Modification
ChooseParent
Modification (contd.)
Update Node Cost
Rewire Method
Modification
Cooperative Tree
Expansion
Experiment
Conclusions and Future
Work
20 / 28
Two cooperative trees Tu and Tv expand in parallel while affecting
each other.
lc : X × X → {0, 1} checks whether the two newly sampled
vertices xu, xv from the two trees, Tu and Tv cooperate.
Reward: If they cooperate, ωk : X × X → R≥0 and
lk(xu) = lk(xu) − ωk(xu, xv)
lk(xv) = lk(xv) − ωk(xu, xv).
Penalty: If they don’t cooperate, ρk : X × X → R≥0 and
lk(xu) = lk(xu) + ρk(xu, xv)
lk(xv) = lk(xv) + ρk(xu, xv).
22. Study Case I: Single Unit Visibility and Patrolling
Introduction
Related Work
Problem Statement
Methods
Experiment
Study Case I: Single
Unit Visibility and
Patrolling
Study Case II: Two
Vehicles, Two Units
Comparison Chart
Cooperative Planning
Tree
Conclusions and Future
Work
22 / 28
Following is a comparison of standard RRT* and our Multi RRT*.
RRT*
MultiObjectiveRRT*
23. Study Case II: Two Vehicles, Two Units
Introduction
Related Work
Problem Statement
Methods
Experiment
Study Case I: Single
Unit Visibility and
Patrolling
Study Case II: Two
Vehicles, Two Units
Comparison Chart
Cooperative Planning
Tree
Conclusions and Future
Work
23 / 28
MultiObjectiveRRT* Weighted Sum Scalarization
500 iterations 500 iterations
2000 iterations 2000 iterations
5000 iterations 5000 iterations
24. Comparison Chart
Introduction
Related Work
Problem Statement
Methods
Experiment
Study Case I: Single
Unit Visibility and
Patrolling
Study Case II: Two
Vehicles, Two Units
Comparison Chart
Cooperative Planning
Tree
Conclusions and Future
Work
24 / 28
Table 1: Trajectory Analysis in Terms of Multiple Objectives
Iteration Objective Tchebycheff Our Multi RRT*
V ehicle 1
500
Visibility 0.62 0.46
Distance 98 111
2000
Visibility 0.81 0.46
Distance 106 111
5000
Visibility 0.47 0.53
Distance 91 88
V ehicle 2
500
Visibility 0.80 0.58
Distance 83 96
2000
Visibility 0.80 0.58
Distance 103 96
5000
Visibility 0.55 0.58
Distance 117 96
25. Cooperative Planning Tree
Introduction
Related Work
Problem Statement
Methods
Experiment
Study Case I: Single
Unit Visibility and
Patrolling
Study Case II: Two
Vehicles, Two Units
Comparison Chart
Cooperative Planning
Tree
Conclusions and Future
Work
25 / 28
Cooperative Path MultiObjectiveRRT* path
26. Conclusions and Future Work
Introduction
Related Work
Problem Statement
Methods
Experiment
Conclusions and Future
Work
Conclusions
Future Work
26 / 28
27. Conclusions
Introduction
Related Work
Problem Statement
Methods
Experiment
Conclusions and Future
Work
Conclusions
Future Work
27 / 28
Modify RRT* to adapt multiple costs and generate a single Pareto
optimal path.
Handled both additive (cost from root to node) and non-additive cost
(local cost).
Propose new algorithm to generate multiple cooperative RRT* trees.
Our MultiObjectiveRRT* running time is O(n · RRT∗) for n costs.
Validate through simulations and comparisons with existing works.
28. Future Work
Introduction
Related Work
Problem Statement
Methods
Experiment
Conclusions and Future
Work
Conclusions
Future Work
28 / 28
Testing the performance on an articulated robot body.
Modeling unmanned aerial vehicles (UAVs).
Adapt it to the non-cooperative game theoretical scenarios.
Thank you!