This document proposes a method for short-term conflict resolution of unmanned aircraft using a distributed framework. It formulates the problem as a multi-agent Markov decision process (MDP) and decomposes it into pairwise encounters to address scalability issues. The approach discretizes state spaces and uses value iteration to compute optimal policies for advising aircraft on control actions. Numerical experiments simulate encounters of multiple aircraft and evaluate the method's ability to balance safety and efficiency through distributed coordination of pairwise solutions.
Cooperative Collision Avoidance via Proximal Message PassingLyft
We propose a distributed model predictive controller to cooperatively solve the collision avoidance problem for a set of vehicles. The method, called proximal message passing, is completely decentralized and needs no global coordination other than synchronizing iterations, allowing us to solve the avoidance problem extremely efficiently and in parallel.
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
Cooperative Collision Avoidance via Proximal Message PassingLyft
We propose a distributed model predictive controller to cooperatively solve the collision avoidance problem for a set of vehicles. The method, called proximal message passing, is completely decentralized and needs no global coordination other than synchronizing iterations, allowing us to solve the avoidance problem extremely efficiently and in parallel.
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
Research Inventy : International Journal of Engineering and Science is published by the group of young academic and industrial researchers with 12 Issues per year. It is an online as well as print version open access journal that provides rapid publication (monthly) of articles in all areas of the subject such as: civil, mechanical, chemical, electronic and computer engineering as well as production and information technology. The Journal welcomes the submission of manuscripts that meet the general criteria of significance and scientific excellence. Papers will be published by rapid process within 20 days after acceptance and peer review process takes only 7 days. All articles published in Research Inventy will be peer-reviewed.
In this paper, a novel adaptive control approach for Unmanned Aerial Manipulators (UAMs) is proposed. The UAMs are a new configuration of the Unmanned Arial Vehicles (UAVs) which are characterized by several inhered nonlinearities, uncertainties and coupling. The studied UAM is a Quadrotor endowed with two degrees of freedom robotic arm. The main objectives of our contribution are to achieve both a tracking error convergence by avoiding any singularity problem and also the chattering amplitude attenuation in the presence of perturbations. Therefore, the proposed Adaptive Nonsingular Terminal Super-Twisting controller (ANTSTW) consists of the hybridization of a Nonsingular Terminal Sliding Mode Control and an Adaptive Super Twisting. The adaptive law, which adjust the Super-Twisting’s parameters, is obtained by using stability Lyapunov theorem. Simulation experiments in trajectory tracking mode were realized and compared with Nonsingular Terminal Super-twisting control to prove the superiority and the effectiveness of the proposed approach.
The sliding mode control approach is recognized as one of the
efficient tools to design robust controllers for complex high-order non-linear dynamic plant operating under uncertainty conditions.
AIRCRAFT PITCH EECE 682 Computer Control Of Dynamic.docxgalerussel59292
AIRCRAFT PITCH
EECE 682
Computer Control Of Dynamic System
Project Report
Boeing Aircraft- Pitch Controller
Example: Dynamics, Modeling, Simulation, Analysis
Instructor:
Dr. Adel Ghandakly
Dept. Electrical and Computer Engineering
California State University, Chico
Submitted By:
Nasser Al Ahbabi
AIRCRAFT PITCH
BOEING AIRCRAFT- PITCH CONTROLLER
Example: Dynamics, Modeling, Simulation, Analysis
by
Nasser Al Ahbabi
California State University, Chico.
NOVEMBER 2014
Abstract
Though airplane has a number of important factors, its stability and control is a key design parameter that must be met. In an airplane
the stability is defined in three angles i.e. pitch, yaw, and roll. In this paper I have focused on the pitch. The system transfer was
AIRCRAFT PITCH
obtained through analyzing the various parameter involved in the pitch control. In all the designs, I considered the design parameter
requirements i.e. the percentage overshoot, steady state error, settling time, and rise time of Boeing aircraft. The designs of pitch
controller using various techniques have been implemented on the system transfer function. I have provided an extension of to these
techniques by using MATLAB/Simulink models that plays an important role in monitoring the results of designed controllers. In
addition, I have also provided a descriptive analysis of the system response to the designed controllers and their conclusions.
Keywords: Aircraft, Pitch, Ackerman, Digitized PID, Diophantine, Optimal Control, Controller , Simulink and MATLAB design.
CONTENTS:
INTRODUCTION
� INTRODUCTION
MATHEMATICAL MODEL
� BOEING AIRCRAFT: PHYSICAL SETUP AND SYSTEM EQUATIONS
� TRANSFER FUNCTION AND STATE-SPACE MODEL
� DESIGN REQUIREMENTS:
CONTROLLER DESIGN
AIRCRAFT PITCH
� DESIGN 1 : DIGITIZED PID
� DESIGN 2 : DIRECT METHOD ( CLOSED FORM)
� DESIGN 3 : DIRECT METHOD ( DIOPHANTINE)
� DESIGN 4 : POLE PLACEMENT (ACKERMAN’S FORMULA)
� DESIGN 5 : OPTIMAL CONTROL
CONCLUSION
REFERENCES
1. INTRODUCTION
Aircrafts are perfect and good examples of a Controller system. They possess unique characteristics that make their controller design a
more challenging problem. On linearization of the model we can attain results with simplified controller designs.
Major parameter in the design of aircrafts entails the horizontal speed, pitch control and the throttle. The throttle controls the main
motor revolutions per minute; the pitch controls the magnitude of the motor thrust. There are two inputs that are independent; the
longitudinal input and the lateral cyclic input. These controls
An aircraft in flight is free to rotate in three dimensions: pitch, nose up or down about an axis running from wing to wing, yaw, nose
left or right about an axis running up and down; and roll, rotation about an axis running from nose to tail. In this .
This article considers different approaches for autopilot controller gain values adjustment. The correct autopilot
performance is tested using modeling methods. A variant of land-based autopilot is considered. Examined are
scenarios of UAV airplanes in level flight. The latter are applicable to tasks such as remote sensing, controlled
area surveillance, etc.
Big Bang- Big Crunch Optimization in Second Order Sliding Mode ControlIJMTST Journal
In this article, Second order sliding mode with Big Bang- Big Crunch optimization technique is employed
for nonlinear uncertain system.The sliding surface describes the transient behavior of a system in sliding
mode. Frequently, PD- type sliding surface is chosen as a hyperplane in the system state space.An integral
term incorporated in the sliding surface expression that resulted in a type of PID sliding surface as hyperbolic
function for alleviating chattering effect. The sliding mode control law is derived using direct Lyapunov
stability approach and asymptotic stability is proved theoretically. Here, novel tuning scheme is introduced for
estimation of PID sliding surface coefficients, due to which it reduces the reaching time as well as disturbance
effect.The simulation results are presented to make a quantitative comparison with the traditional sliding
mode control. It is demonstrated that the proposed control law improves the tracking performance of system
dynamic model in case of external disturbances and parametric uncertainties.
Optimal and Pid Controller for Controlling Camera's Position InUnmanned Aeria...Zac Darcy
This paper describes two controllers designed specifically for adjusting camera’s position in a small
unmanned aerial vehicle (UAV). The optimal controller was designed and first simulated by using
MATLAB technique and the results displayed graphically, also PID controller was designedand
simulatedby using MATLAB technique .The goal of this paper is to connect the tow controllers in cascade
mode to obtain the desired performance and correction in camera’s position in both roll and pitch.
Research Inventy : International Journal of Engineering and Science is published by the group of young academic and industrial researchers with 12 Issues per year. It is an online as well as print version open access journal that provides rapid publication (monthly) of articles in all areas of the subject such as: civil, mechanical, chemical, electronic and computer engineering as well as production and information technology. The Journal welcomes the submission of manuscripts that meet the general criteria of significance and scientific excellence. Papers will be published by rapid process within 20 days after acceptance and peer review process takes only 7 days. All articles published in Research Inventy will be peer-reviewed.
In this paper, a novel adaptive control approach for Unmanned Aerial Manipulators (UAMs) is proposed. The UAMs are a new configuration of the Unmanned Arial Vehicles (UAVs) which are characterized by several inhered nonlinearities, uncertainties and coupling. The studied UAM is a Quadrotor endowed with two degrees of freedom robotic arm. The main objectives of our contribution are to achieve both a tracking error convergence by avoiding any singularity problem and also the chattering amplitude attenuation in the presence of perturbations. Therefore, the proposed Adaptive Nonsingular Terminal Super-Twisting controller (ANTSTW) consists of the hybridization of a Nonsingular Terminal Sliding Mode Control and an Adaptive Super Twisting. The adaptive law, which adjust the Super-Twisting’s parameters, is obtained by using stability Lyapunov theorem. Simulation experiments in trajectory tracking mode were realized and compared with Nonsingular Terminal Super-twisting control to prove the superiority and the effectiveness of the proposed approach.
The sliding mode control approach is recognized as one of the
efficient tools to design robust controllers for complex high-order non-linear dynamic plant operating under uncertainty conditions.
AIRCRAFT PITCH EECE 682 Computer Control Of Dynamic.docxgalerussel59292
AIRCRAFT PITCH
EECE 682
Computer Control Of Dynamic System
Project Report
Boeing Aircraft- Pitch Controller
Example: Dynamics, Modeling, Simulation, Analysis
Instructor:
Dr. Adel Ghandakly
Dept. Electrical and Computer Engineering
California State University, Chico
Submitted By:
Nasser Al Ahbabi
AIRCRAFT PITCH
BOEING AIRCRAFT- PITCH CONTROLLER
Example: Dynamics, Modeling, Simulation, Analysis
by
Nasser Al Ahbabi
California State University, Chico.
NOVEMBER 2014
Abstract
Though airplane has a number of important factors, its stability and control is a key design parameter that must be met. In an airplane
the stability is defined in three angles i.e. pitch, yaw, and roll. In this paper I have focused on the pitch. The system transfer was
AIRCRAFT PITCH
obtained through analyzing the various parameter involved in the pitch control. In all the designs, I considered the design parameter
requirements i.e. the percentage overshoot, steady state error, settling time, and rise time of Boeing aircraft. The designs of pitch
controller using various techniques have been implemented on the system transfer function. I have provided an extension of to these
techniques by using MATLAB/Simulink models that plays an important role in monitoring the results of designed controllers. In
addition, I have also provided a descriptive analysis of the system response to the designed controllers and their conclusions.
Keywords: Aircraft, Pitch, Ackerman, Digitized PID, Diophantine, Optimal Control, Controller , Simulink and MATLAB design.
CONTENTS:
INTRODUCTION
� INTRODUCTION
MATHEMATICAL MODEL
� BOEING AIRCRAFT: PHYSICAL SETUP AND SYSTEM EQUATIONS
� TRANSFER FUNCTION AND STATE-SPACE MODEL
� DESIGN REQUIREMENTS:
CONTROLLER DESIGN
AIRCRAFT PITCH
� DESIGN 1 : DIGITIZED PID
� DESIGN 2 : DIRECT METHOD ( CLOSED FORM)
� DESIGN 3 : DIRECT METHOD ( DIOPHANTINE)
� DESIGN 4 : POLE PLACEMENT (ACKERMAN’S FORMULA)
� DESIGN 5 : OPTIMAL CONTROL
CONCLUSION
REFERENCES
1. INTRODUCTION
Aircrafts are perfect and good examples of a Controller system. They possess unique characteristics that make their controller design a
more challenging problem. On linearization of the model we can attain results with simplified controller designs.
Major parameter in the design of aircrafts entails the horizontal speed, pitch control and the throttle. The throttle controls the main
motor revolutions per minute; the pitch controls the magnitude of the motor thrust. There are two inputs that are independent; the
longitudinal input and the lateral cyclic input. These controls
An aircraft in flight is free to rotate in three dimensions: pitch, nose up or down about an axis running from wing to wing, yaw, nose
left or right about an axis running up and down; and roll, rotation about an axis running from nose to tail. In this .
This article considers different approaches for autopilot controller gain values adjustment. The correct autopilot
performance is tested using modeling methods. A variant of land-based autopilot is considered. Examined are
scenarios of UAV airplanes in level flight. The latter are applicable to tasks such as remote sensing, controlled
area surveillance, etc.
Big Bang- Big Crunch Optimization in Second Order Sliding Mode ControlIJMTST Journal
In this article, Second order sliding mode with Big Bang- Big Crunch optimization technique is employed
for nonlinear uncertain system.The sliding surface describes the transient behavior of a system in sliding
mode. Frequently, PD- type sliding surface is chosen as a hyperplane in the system state space.An integral
term incorporated in the sliding surface expression that resulted in a type of PID sliding surface as hyperbolic
function for alleviating chattering effect. The sliding mode control law is derived using direct Lyapunov
stability approach and asymptotic stability is proved theoretically. Here, novel tuning scheme is introduced for
estimation of PID sliding surface coefficients, due to which it reduces the reaching time as well as disturbance
effect.The simulation results are presented to make a quantitative comparison with the traditional sliding
mode control. It is demonstrated that the proposed control law improves the tracking performance of system
dynamic model in case of external disturbances and parametric uncertainties.
Optimal and Pid Controller for Controlling Camera's Position InUnmanned Aeria...Zac Darcy
This paper describes two controllers designed specifically for adjusting camera’s position in a small
unmanned aerial vehicle (UAV). The optimal controller was designed and first simulated by using
MATLAB technique and the results displayed graphically, also PID controller was designedand
simulatedby using MATLAB technique .The goal of this paper is to connect the tow controllers in cascade
mode to obtain the desired performance and correction in camera’s position in both roll and pitch.
Smart aerosonde UAV longitudinal flight control system based on genetic algor...journalBEEI
Synthesis of a flight control system for such an aircraft that achieves stable and acceptable performance across a specified flying envelope in the presence of uncertainties represents an attractive and challenging design problem. This study uses the genetic self-tuning PID algorithm to develop an intelligent flight control system for the aerosonde UAV model. To improve the system's transient responses, the gains of the PID controller are improved using a genetic algorithm (GA). Simulink/MATLAB software is used to model and simulate the proposed system. The proposed PID controller integrated with the GA is compared with the classical one. Three simulation scenarios are carried out. In the first scenario, and at normal conditions, the proposed controller performance is better than the classical one. While in the second scenario, identical results are achieved from both controllers. Finally, in the third scenario, the PID controller with GA shows the robustness and durability of the system compared with the classical PID in presence of external wind disturbance. The simulation results prove the system parameters optimization.
Vaccine management system project report documentation..pdfKamal Acharya
The Division of Vaccine and Immunization is facing increasing difficulty monitoring vaccines and other commodities distribution once they have been distributed from the national stores. With the introduction of new vaccines, more challenges have been anticipated with this additions posing serious threat to the already over strained vaccine supply chain system in Kenya.
Industrial Training at Shahjalal Fertilizer Company Limited (SFCL)MdTanvirMahtab2
This presentation is about the working procedure of Shahjalal Fertilizer Company Limited (SFCL). A Govt. owned Company of Bangladesh Chemical Industries Corporation under Ministry of Industries.
Quality defects in TMT Bars, Possible causes and Potential Solutions.PrashantGoswami42
Maintaining high-quality standards in the production of TMT bars is crucial for ensuring structural integrity in construction. Addressing common defects through careful monitoring, standardized processes, and advanced technology can significantly improve the quality of TMT bars. Continuous training and adherence to quality control measures will also play a pivotal role in minimizing these defects.
Sachpazis:Terzaghi Bearing Capacity Estimation in simple terms with Calculati...Dr.Costas Sachpazis
Terzaghi's soil bearing capacity theory, developed by Karl Terzaghi, is a fundamental principle in geotechnical engineering used to determine the bearing capacity of shallow foundations. This theory provides a method to calculate the ultimate bearing capacity of soil, which is the maximum load per unit area that the soil can support without undergoing shear failure. The Calculation HTML Code included.
Overview of the fundamental roles in Hydropower generation and the components involved in wider Electrical Engineering.
This paper presents the design and construction of hydroelectric dams from the hydrologist’s survey of the valley before construction, all aspects and involved disciplines, fluid dynamics, structural engineering, generation and mains frequency regulation to the very transmission of power through the network in the United Kingdom.
Author: Robbie Edward Sayers
Collaborators and co editors: Charlie Sims and Connor Healey.
(C) 2024 Robbie E. Sayers
NO1 Uk best vashikaran specialist in delhi vashikaran baba near me online vas...Amil Baba Dawood bangali
Contact with Dawood Bhai Just call on +92322-6382012 and we'll help you. We'll solve all your problems within 12 to 24 hours and with 101% guarantee and with astrology systematic. If you want to take any personal or professional advice then also you can call us on +92322-6382012 , ONLINE LOVE PROBLEM & Other all types of Daily Life Problem's.Then CALL or WHATSAPP us on +92322-6382012 and Get all these problems solutions here by Amil Baba DAWOOD BANGALI
#vashikaranspecialist #astrologer #palmistry #amliyaat #taweez #manpasandshadi #horoscope #spiritual #lovelife #lovespell #marriagespell#aamilbabainpakistan #amilbabainkarachi #powerfullblackmagicspell #kalajadumantarspecialist #realamilbaba #AmilbabainPakistan #astrologerincanada #astrologerindubai #lovespellsmaster #kalajaduspecialist #lovespellsthatwork #aamilbabainlahore#blackmagicformarriage #aamilbaba #kalajadu #kalailam #taweez #wazifaexpert #jadumantar #vashikaranspecialist #astrologer #palmistry #amliyaat #taweez #manpasandshadi #horoscope #spiritual #lovelife #lovespell #marriagespell#aamilbabainpakistan #amilbabainkarachi #powerfullblackmagicspell #kalajadumantarspecialist #realamilbaba #AmilbabainPakistan #astrologerincanada #astrologerindubai #lovespellsmaster #kalajaduspecialist #lovespellsthatwork #aamilbabainlahore #blackmagicforlove #blackmagicformarriage #aamilbaba #kalajadu #kalailam #taweez #wazifaexpert #jadumantar #vashikaranspecialist #astrologer #palmistry #amliyaat #taweez #manpasandshadi #horoscope #spiritual #lovelife #lovespell #marriagespell#aamilbabainpakistan #amilbabainkarachi #powerfullblackmagicspell #kalajadumantarspecialist #realamilbaba #AmilbabainPakistan #astrologerincanada #astrologerindubai #lovespellsmaster #kalajaduspecialist #lovespellsthatwork #aamilbabainlahore #Amilbabainuk #amilbabainspain #amilbabaindubai #Amilbabainnorway #amilbabainkrachi #amilbabainlahore #amilbabaingujranwalan #amilbabainislamabad
Immunizing Image Classifiers Against Localized Adversary Attacksgerogepatton
This paper addresses the vulnerability of deep learning models, particularly convolutional neural networks
(CNN)s, to adversarial attacks and presents a proactive training technique designed to counter them. We
introduce a novel volumization algorithm, which transforms 2D images into 3D volumetric representations.
When combined with 3D convolution and deep curriculum learning optimization (CLO), itsignificantly improves
the immunity of models against localized universal attacks by up to 40%. We evaluate our proposed approach
using contemporary CNN architectures and the modified Canadian Institute for Advanced Research (CIFAR-10
and CIFAR-100) and ImageNet Large Scale Visual Recognition Challenge (ILSVRC12) datasets, showcasing
accuracy improvements over previous techniques. The results indicate that the combination of the volumetric
input and curriculum learning holds significant promise for mitigating adversarial attacks without necessitating
adversary training.
Explore the innovative world of trenchless pipe repair with our comprehensive guide, "The Benefits and Techniques of Trenchless Pipe Repair." This document delves into the modern methods of repairing underground pipes without the need for extensive excavation, highlighting the numerous advantages and the latest techniques used in the industry.
Learn about the cost savings, reduced environmental impact, and minimal disruption associated with trenchless technology. Discover detailed explanations of popular techniques such as pipe bursting, cured-in-place pipe (CIPP) lining, and directional drilling. Understand how these methods can be applied to various types of infrastructure, from residential plumbing to large-scale municipal systems.
Ideal for homeowners, contractors, engineers, and anyone interested in modern plumbing solutions, this guide provides valuable insights into why trenchless pipe repair is becoming the preferred choice for pipe rehabilitation. Stay informed about the latest advancements and best practices in the field.
About
Indigenized remote control interface card suitable for MAFI system CCR equipment. Compatible for IDM8000 CCR. Backplane mounted serial and TCP/Ethernet communication module for CCR remote access. IDM 8000 CCR remote control on serial and TCP protocol.
• Remote control: Parallel or serial interface.
• Compatible with MAFI CCR system.
• Compatible with IDM8000 CCR.
• Compatible with Backplane mount serial communication.
• Compatible with commercial and Defence aviation CCR system.
• Remote control system for accessing CCR and allied system over serial or TCP.
• Indigenized local Support/presence in India.
• Easy in configuration using DIP switches.
Technical Specifications
Indigenized remote control interface card suitable for MAFI system CCR equipment. Compatible for IDM8000 CCR. Backplane mounted serial and TCP/Ethernet communication module for CCR remote access. IDM 8000 CCR remote control on serial and TCP protocol.
Key Features
Indigenized remote control interface card suitable for MAFI system CCR equipment. Compatible for IDM8000 CCR. Backplane mounted serial and TCP/Ethernet communication module for CCR remote access. IDM 8000 CCR remote control on serial and TCP protocol.
• Remote control: Parallel or serial interface
• Compatible with MAFI CCR system
• Copatiable with IDM8000 CCR
• Compatible with Backplane mount serial communication.
• Compatible with commercial and Defence aviation CCR system.
• Remote control system for accessing CCR and allied system over serial or TCP.
• Indigenized local Support/presence in India.
Application
• Remote control: Parallel or serial interface.
• Compatible with MAFI CCR system.
• Compatible with IDM8000 CCR.
• Compatible with Backplane mount serial communication.
• Compatible with commercial and Defence aviation CCR system.
• Remote control system for accessing CCR and allied system over serial or TCP.
• Indigenized local Support/presence in India.
• Easy in configuration using DIP switches.
TECHNICAL TRAINING MANUAL GENERAL FAMILIARIZATION COURSEDuvanRamosGarzon1
AIRCRAFT GENERAL
The Single Aisle is the most advanced family aircraft in service today, with fly-by-wire flight controls.
The A318, A319, A320 and A321 are twin-engine subsonic medium range aircraft.
The family offers a choice of engines
3. Unmanned aircraft traffic management
automating conflict avoidance is critical to integrating small
unmanned aerial systems (UAS) to civil airspace
automation to augment controllers
Introduction 3
4. Conflict avoidance
given potential conflicts between drones, find the best set of advisories
focus on horizontal or co-altitude conflict resolution
– NASA’s proposed airspace for UAS is under 150 m (500 ft)
– flight altitudes need to avoid disruption and buildings
conflict defined as loss of minimum separation between aircraft
Introduction 4
5. Complications
multiagent problem
– system must coordinate between many aircraft
– large search space for solution
uncertainty in system
– imperfect sensor measurements of current state
– variable pilot response, vehicle performance, etc. affect future path
appropriate trade-off between safety and efficiency not obvious
Introduction 5
6. Goals
robust and efficient method
tractable approach to complex stochastic problem
– account for uncertainty in large problem with reasonable compute
resources
balances between airspace safety and efficiency
– ensure safety and provide timely conflict alerts to aircraft
arbitrary-scale optimization
– real-time conflict resolution for large airspace
Introduction 6
7. Previous approaches
mathematical programming (MIP, SCP) [SVFH05, ASD12]
– can work well for simple vehicle networks
distributed convex optimization [OG15]
– hard to incorporate stochastic objectives/constraints
Markov decision process formulation (ACAS X) [KC11, CK12]
– assumes “white noise” accelerations for intruder aircraft
and many more. . . [KY00]
Introduction 7
9. Conflict Resolution as a UTM service
UTM server client side
resolution server
filtered sector
status updates
advisories
advisories
status updates
Introduction 9
10. Conflict Resolution as a Standalone service
derivative UTM client service
– subscribes to UTM server to track aircraft and deconflict routes
– pub-sub system that operators subscribe to to receive advisories
current implementation uses standalone model
– still unclear about end architecture for resolution service
– clean approach to get started with prototype system
– implemented with scalability and modularity in mind
Introduction 10
12. Markov decision process (MDP)
defined by the tuple (S, A, T, R)
S and A are the sets of all possible states and actions, respectively
T (s, a, s ) gives the probability of transitioning into state s by
taking action a at the current state s
R (s, a) gives the reward for taking action a at the current state s
environment
T(s,a,s’)!
agent
action
a!
state
s!
reward
R(s,a)!
Mathematical formulation 12
13. Value iteration
agent
π⋆(s)"
action
a"
state
s"
want to find the optimal policy π (s)
– gives action that maximizes the utility Q (s, a) from any given state
π (s) = argmax
a∈A
Q (s, a)
value iteration updates value function guess ˆQ until convergence
– expensive one-off compute but cheap policy extraction ( ˆQ lookup)
ˆQ (s, a) := R (s, a) +
s ∈S
T (s, a, s ) max
a ∈A
ˆQ (s , a )
Mathematical formulation 13
14. Multiagent MDP
extension of MDP to cooperative multiagent setting
similar to case where centralized planner has access to system state
also defined by the tuple (S, A, T, R), except
– S and A are all possible joint states and actions
– T and R operate on elements of S and A
Mathematical formulation 14
15. Short-term conflict resolution
horizontal conflict resolution between n aircraft
– conflict defined as loss of minimum separation distance, 500 m
– aircraft at risk if it could experience a conflict within two minutes
alert aircraft that need corrective maneuvers
– but not to the extent that alerts become a nuisance
Mathematical formulation 15
16. Resolution advisories
at each time step T = 5 s, system issues a joint advisory
– joint advisory φ chosen out of a finite set of corrective bank angles
for each aircraft
action set A = {−20◦
, −10◦
, 0◦
, 10◦
, 20◦
, COC}
n
– positive and negative angles correspond to left and right banks
– COC is a clear-of-conflict status advisory—nothing needs to be done
higher resolution comes at the cost of computational complexity
Mathematical formulation 16
17. Dynamics
ith aircraft state si = (xi, yi, ψi, vi, pi)
– latitude and longitude xi and yi
– heading ψi
– groundspeed vi
– responsiveness indicator pi
aircraft follow Dubin’s kinematic model
– simplicity avoids risk of overfitting complicated models
˙xi = vi cos ψi ˙yi = vi sin ψi
˙ψi =
g tan φi
vi
ψi!
bank: φi!
(xi ,yi)!
vi!
Mathematical formulation 17
18. Pilot model
advisory response determined stochastically by Bernoulli process
5 s average response delay to first corrective advisory as
recommended by ICAO [ICA07]
– when responding, the pilot executes the advisory for 5 s time step
non-responsive:
white noise path
responsive:
corrective action
COC
5 s average
response lag
Mathematical formulation 18
19. Reward function
balance competing objectives
maximize safety
loss of
separation
cost
separation
closeness
penalty
minimize disruption
cost
advisoryCOC ±0° ±10° ±20°
conflict
penalty
Mathematical formulation 19
21. Curse of dimensionality
in an MMDP, S and A are all possible joint states and actions
problem: S and A scale exponentially with number of agents
3 drones: 1000 states
2 drones: 100 states
1 drone: 10 states
Approach 21
22. Decompose and coordinate
multi-agent pairwise joint policy
decompose
pairwise solutions
+
coordination
=
ac1: left
ac2: right
ac3: straight
Approach 22
24. Pairwise conflict resolution
decompose full MMDP into O n2
pairwise encounters
action set, pilot response model, and reward function unchanged
use relative positions and bearing to reduce state space
– arbitrarily set one aircraft as ownship, and the other as intruder
– speeds remain in global reference frame
s = xrel
, yrel
, ψrel
, vownship
, vintruder
, pownship
, pintruder
(0,0)!
vownship
!
ψrel
(xrel ,yrel)!
vintruder
Approach 24
25. Discretization
in general, no analytical solution to problem with continuous state
space
approximate value function ˆQ over discretized state space
– discretize state space via multilinear interpolation
– iteratively update ˆQ until convergence
ˆQ (s, a) := R (s, a) +
s ∈S
T (s, a, s ) max
a ∈A
ˆQ (s , a )
Approach 25
27. Implementation
discretize continuous state space into 9.6 million states
– expensive but one-off computation
ˆQ converges in 7 hours (40 iterations over S × A)
– solver: parallel implementation in Julia
– machine: 20 2.3 GHz Intel Xeon cores with 125 GB RAM
Variable Minimum Maximum Number of values
xrel
, yrel
−3000 m 3000 m 51
ψrel
0◦
360◦
37
vownship
, vintruder
10 m s−1
20 m s−1
5
pownship
, pintruder
- - 2
Approach 27
33. Utility fusion
idea: combine pairwise utilities Qij to form proxy for global utility Q
Qij operates on the pairwise MDP state-action pair (sij, aij) for
aircraft i and j
fusion strategies π(s) = argmax
a∈A
Q(s, a)
– max-min: “worst-case”
Qmin
(s, a) = min
i<j
{Uij(sij, aij)}
– max-sum: “average”
Qsum
(s, a) =
i<j
Uij(sij, aij)
problem: action space A scales exponentially with number of aircraft
Approach 33
34. Search heuristic: Alternating maximization
maximize global utility by varying one action and fixing the rest
e.g., fix drone 1 and 2’s actions (R and L) and vary drone 3’s action
1.1 1.3 0.9
2.1 1.1 0.9
1.3 2.3 0.8
2.3 1.4 0.3
0.9 1.7 0.8
0.8 2.2 1.4
2.1 1.1 0.3
argmax
pair 1 pair 2
L
R
S
L
R
S
L R S L R S
aircraft 3 action: L
Q13(s,a)! Q23(s,a)!
min
Approach 34
35. Distributed variant I: Parallel search
greedy local maximization
0 L 0
0 0 L
R 0 0
broadcast action
R L L
R L L
R L L
repeat until convergence
Approach 35
36. Distributed variant II: Consensus search
parallel alt. maximization
R R L
R L L
R L R
broadcast joint action
R R L
R L L
R L R
greedy local maximization
R L L
R R L
R R L
incorporate msgs
R ? L
R R ?
? R L
Approach 36
37. Consensus search message
parallel search could incur large communication cost
messages reduce communication between compute nodes, but risk
yielding unsynchronized joint action across aircraft
incorporate messages by “averaging” what intruders think ownship
should do to approximate consensus
broadcast joint action
R R L
R L L
R L R
incorporate msgs
R L? L
R R 0?
R? R L
Approach 37
38. Joint policy visualization
triple aircraft encounter max-min (top) and max-sum policies (bottom)
COC
−2,000−1,000 0 1,000 2,000
−2,000
0
2,000
x (m)
y(m)
First aircraft action
−2,000−1,000 0 1,000 2,000
−2,000
0
2,000
x (m)
Second aircraft action
−2,000−1,000 0 1,000 2,000
−2,000
0
2,000
x (m)
Third aircraft action
−20
−10
0
10
20
COC
−2,000−1,000 0 1,000 2,000
−2,000
0
2,000
x (m)
y(m)
First aircraft action
COC
−2,000−1,000 0 1,000 2,000
−2,000
0
2,000
x (m)
Second aircraft action
COC
−2,000−1,000 0 1,000 2,000
−2,000
0
2,000
x (m)
Third aircraft action
−20
−10
0
10
20
Approach 38
40. Encounter model
generate uniformly random starting aircraft states in annulus
– speeds uniformly random
– headings initialized to point to annulus center
– resample if loss of separation
Numerical experiments 40
41. Aircraft model
ODE solver for dynamics
– continuous Dubin’s kinematic model
– Gaussian noise in acceleration and banking
corrective bank angles mapped to PID control policies
Numerical experiments 41
42. Baseline methods
closest threat
– multithreat scenario decomposed into pairwise encounters
– aircraft execute solution to encounter with closest intruder
uncoordinated
– aircraft assumes all other aircraft are white-noise intruders
– executes greedy local maximization to find own action
Numerical experiments 42
43. Experiment
>1 million simulations from 2 to 10 aircraft
– code: Julia implementation
– machine: 3.4 GHz Intel i7 processor with 32 GB RAM
∼10 ms decision times (vs. 5 s decision period)
– serial solve times of <10 ms for up to 10 aircraft sufficient for
server-based resolution system
– unoptimized code that doesn’t account for communication costs for
distributed variants
Numerical experiments 43
44. Safety performance
max-min utility fusion
4 5 6 7 8 9 10
6
7
8
9
×10−3
number of aircraft
conflictprobability
uncoord
centralized
parallel
consensus
max-sum utility fusion
4 5 6 7 8 9 10
3
4
5
6
×10−2
number of aircraft
conflictprobability
uncoord
centralized
parallel
consensus
distributed variants perform as well as serial version
coordinated methods >10 times better than closest threat heuristic
coordinated methods ∼10% better than uncoordinated method
Numerical experiments 44
45. Safety performance takeaways
no near mid-air collisions (NMACs) in simulations
– NMAC threshold distance defined at 30 m (100 ft)
– due to penalty on smaller separation distances even in conflict
simulations validate all algorithmic variants
– distributed variants perform as well as serial version
– coordinated methods significantly better than baselines
Numerical experiments 45
46. Trade-off: Safety vs. alert rate
vary relative penalty between loss of separation and disruption
best performance at “knee” of Pareto frontier
0 2 4 6
×10−2
1.8
1.9
2
2.1
2.2
×10−2
conflict probability
alertrate
centralized
parallel
consensus
Numerical experiments 46
48. Practical conflict avoidance
recall: pub-sub system that UTM clients subscribe to for advisories
– standalone system that subscribes to UTM server for flight tracking
goals: robustness, scalability, and modularity
– cluster computing framework for large-scale data processing
– streaming analytics for real-time conflict resolution
– built-in system fault-tolerance
UTM-α: A distributed framework 48
50. System design
why Kafka?
– designed as unified, high-throughput, low-latency platform for
handling real-time data feeds
– open source with active industry use (originally developed by
LinkedIn)
why Spark?
– easy, high-level API
– ease of operations with minimal fuss over fault-tolerance
– enormous, active community and industry deployments (CISCO,
Netflix, Intel,. . . )
UTM-α: A distributed framework 50
51. Driver-worker model
driver-worker model for conflict resolution
– driver loads up policy object that contains lookup table and
algorithm for conflict resolution
– workers receives policy object via broadcast and subsequently
delegated tasks by driver
formatted conflict data stored as an “infinite” stream of resilient
distributed datasets (RDDs)
– distributed, partitioned collection of conflict objects (JSON)
– automatically rebuilt on failure
UTM-α: A distributed framework 51
52. Pub-sub model
ingestor publishes conflict objects to conflict Kafka topic
– subscribes to and ingests UTM server stream and crudely identifies
potential conflicts by proximity
– formats conflict data as JSON strings
advisor publishes advisories to advisory Kafka topic
– producers are worker nodes in cluster generating advisories
– consumers are UTM operator clients flying drones
UTM-α: A distributed framework 52
53. Working model
Spark driver launches workers/executors and broadcasts policy object
workers receive conflict data in parallel from conflict Kafka topic
as RDD stream
workers publish advisories to advisory Kafka topic for UTM
operator clients
UTM-α: A distributed framework 53
55. Summary
three variants of coordination-based conflict resolution algorithm
– 1 centralized/serial and 2 distributed variants
millisecond solve times for real-time application for multiple aircraft
robust distributed system for practical conflict avoidance
Conclusion and future work 55
56. Implementation
policy generation on Julia scientific programming language
https://github.com/sisl/ConflictAvoidanceDASC
distributed system with Apache Kafka and Spark Streaming
https://bitbucket.org/sisl/utm-alpha
Conclusion and future work 56
57. Further research
tracking aircraft via belief states
– use the UTM client server to track reported aircraft states
– generate local set of belief states for all aircraft to better estimate
state uncertainty in real-time
best action for drone in case of communication loss
– figure out the response state of the aircraft via belief state tracking
– where to go in case of communication loss, what flight profile to
take, and what UTM should assume about the drones flight
dealing with adversarial flights
Conclusion and future work 57
58. Further software development
standardize resolution advisory and advisory receipt formats with
focus on minimizing message size
quantify delay times for sending packets between advisory server and
clients in order to model it into the problem
Conclusion and future work 58
59. References
Federico Augugliaro, Angela P Schoellig, and Raffaello D’Andrea.
Generation of collision-free trajectories for a quadrocopter fleet: A
sequential convex programming approach.
In IEEE/RSJ International Conference on Intelligent Robots and
Systems (IROS), pages 1917–1922, October 2012.
J. P. Chryssanthacopoulos and M. J. Kochenderfer.
Decomposition methods for optimized collision avoidance with
multiple threats.
Journal of Guidance, Control, and Dynamics, 35(2):398–405, 2012.
International Civil Aviation Organization ICAO.
Surveillance, radar and collision avoidance.
International Standards and Recommended Practices, 4, 2007.
59
60. References
M.J. Kochenderfer and J.P. Chryssanthacopoulos.
Robust airborne collision avoidance through dynamic programming.
Project Report ATC-371, MIT Lincoln Laboratory, 2011.
J. K. Kuchar and L. C. Yang.
A review of conflict detection and resolution modeling methods.
IEEE Trans. Intell. Transp. Syst., 1(4):179–189, 2000.
H. Y. Ong and J. C. Gerdes.
Cooperative collision avoidance via proximal message passing.
In American Control Conference (ACC), July 2015.
Tom Schouwenaars, Mario Valenti, Eric Feron, and Jonathan How.
Implementation and flight test results of MILP-based UAV guidance.
In IEEE Aerospace Conference, pages 1–13. IEEE, 2005.
60
61. Impact of search timeout
conflict decreases with iteration limit but with diminishing returns
2 3 4 5 6
4.8
4.9
5
5.1
5.2
5.3
×10−3
iteration limit
conflictprobability
61
62. Restriction messages for consensus search
2 4 6 8 10
0.5
1
1.5
2
2.5
3
3.5
×10−3
number of uavs
conflictprobability
consensus (maxmin)
restriction (maxmin)
instead of broadcasting joint action, aircraft broadcast restriction
messages that indicate what other aircraft shouldn’t do
– e.g., reward straight path when it receives no left and no right
messages at a decision period
62