Knowledge Based Genetic Algorithm for Robot Path Planning
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This document summarizes a research paper that proposes a knowledge-based genetic algorithm for mobile robot path planning. The algorithm uses a grid-based representation and specialized genetic operators informed by domain knowledge. Simulation results show the algorithm can find optimal or near-optimal paths in static and dynamic environments. Comparisons demonstrate the specialized operators improve GA performance over standard operators. Future work could better utilize domain knowledge and handle changes in dynamic environments.
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This document presents a modified artificial potential fields algorithm for mobile robot path planning in unknown and dynamic environments. The algorithm uses artificial potential fields to iteratively find optimal points to form a collision-free path from the start to destination. For static obstacles, potential values are used to identify clusters of points around the start and goal, and find a connecting midpoint. This process is repeated iteratively. For dynamic obstacles, Markov models are used to analyze obstacle behavior from sensor data and predict collision points. The robot's path is replanned as needed to avoid collisions based on feedback from sensors and odometry. Simulation results show the algorithm can efficiently plan paths in unknown environments and avoid both static and dynamic obstacles.
Paper id 2320143
This document proposes a hybrid optimization algorithm using ant colony optimization and particle swarm optimization to solve the multiobjective multicast routing problem in wireless sensor networks. The goal is to optimize two objectives simultaneously - end-to-end delay and total transmitted power. ACO and PSO are combined to find Pareto-optimal solutions efficiently. Simulation results show the algorithm can find near-optimal solutions for minimizing delay and power consumption when routing data from a source to multiple destinations in wireless sensor networks.
Ica 2013021816274759
This document proposes and evaluates a new metaheuristic optimization algorithm called Current Search (CS) and applies it to optimize PID controller parameters for DC motor speed control. The CS is inspired by electric current flow and aims to balance exploration and exploitation. It outperforms genetic algorithm, particle swarm optimization, and adaptive tabu search on benchmark optimization problems, finding better solutions faster. When applied to optimize a PID controller for DC motor speed control, the CS successfully controlled motor speed.
Seminer-Merve AYDIN-4802220035-SUNUM.pptx
The document discusses a new approach for mobile robot path planning in complex dynamic environments using fuzzy logic and ant colony optimization algorithms. It proposes combining these methods to improve path finding. The ant colony algorithm is used to evolve and optimize the fuzzy rule table to help the robot select optimal paths without collisions amidst moving obstacles of varying speeds, directions and shapes. Simulation results show the combined approach finds shorter paths in less time compared to using just fuzzy logic.
Evolving CSP Algorithm in Predicting the Path Loss of Indoor Propagation Models
Constraint programming is the study of system which is based on constraints. The solution of a constraint satisfaction problem is a set of
variable value assignments, which satisfies all members of the set of constraints in the CSP. In this paper the application of constraint satisfaction
programming is used in predicting the path loss of various indoor propagation models using chronological backtrack algorithm, which is basic
algorithm of CSP. After predicting the path loss at different set of parameters such as frequencies (f), floor attenuation factor (FAF), path loss
coefficient (n), we find the optimum set of parameter frequency (f), floor attenuation factor (FAF), path loss coefficient(n) at which the path loss is
minimum. The Branch and bound algorithm is used to optimize the constraint satisfaction problem.
Ijarcet vol-2-issue-7-2292-2296
1) The document discusses optimizing cross-layer interactions between the physical and MAC layers of wireless networks using genetic algorithms and smart antennas.
2) It proposes using a continuous genetic algorithm to optimize an array factor cost function for a smart antenna with varying element spacings.
3) The results show the continuous genetic algorithm can successfully optimize the array factor and minimize sidelobes for different element spacings, allowing nulls to be placed to improve the radiation pattern.
Ijarcet vol-2-issue-7-2292-2296
1) The document discusses optimizing cross-layer interactions between the physical and MAC layers of wireless networks using genetic algorithms and smart antennas.
2) It proposes using a continuous genetic algorithm to optimize an array factor cost function for a smart antenna with varying element spacings.
3) The results show the continuous genetic algorithm can successfully optimize the array factor and minimize sidelobes for different element spacings, allowing for null placement in the antenna radiation pattern.
Path Planning And Navigation
The document discusses various path planning techniques for mobile robots to navigate between a starting point and destination while avoiding collisions. It describes methods like visibility graphs, roadmaps, cell decomposition, and potential fields. It also covers implementing techniques like breadth-first search on visibility graphs and optimizing trajectories using factors like travel time, distance and sensor information.
Ant Colony Optimization and path planning.pptx
Presentation I made for Ant colony optimization and path planning of mobile robot. Useful for computer science as well as electronics.
Mobile robot path planning using ant colony optimization
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
A Real Time Framework of Multiobjective Genetic Algorithm for Routing in Mobi...
Routing in mobile networks is a multiobjective
optimization problem. The problem needs to consider multiple
objectives simultaneously such as Quality of Service
parameters, delay and cost. This paper uses the NSGA-II
multiobjectve genetic algorithm to solve the dynamic shortest
path routing problem in mobile networks and proposes a
framework for real-time software implementation.
Simulations confirm a good quality of solution (route
optimality) and a high rate of convergence.
MS Project
1) The document describes a vehicle routing project that uses a multi-commodity network flow formulation to explore sub-optimal solutions for object classification with noisy sensors on a 2D grid.
2) It formulates the problem as assigning tasks to vehicles (commodities) that must flow through the graph in 4 directions while being constrained by boundaries and returning to base.
3) The algorithm uses a look-ahead window to consider future moves and a rollout step using linear programming to approximate costs farther in time and decide optimal vehicle movements.
Combinational circuit designer using 2D Genetic Algorithm
This document discusses using genetic algorithms to optimize the design of combinational logic circuits. It describes representing circuit designs as chromosomes that are evolved over generations using genetic operators like crossover and mutation to minimize the number of gates needed. Specifically, it proposes encoding the circuit as a 2D matrix chromosome to allow 2D genetic operators that may provide faster optimization compared to 1D encodings. The evolved designs aim to implement the desired logic function with the highest number of "wire" null gates.
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Knowledge Based Genetic Algorithm for Robot Path Planning
- 1. A KNOWLEDGE BASED GENETIC ALGORITHM FOR PATH PLANNING OF A MOBILE ROBOT Presented by: Tarundeep Dhot Dept of ECE Concordia University
- 2. This presentation is based on a research paper written by the following authors: Y. Hu and Simon Yang This paper was published at: International Conference of Robotics and Automation, New Orleans LA – April 2004 This presentation is solely meant for educational purposes. Acknowledgements
- 6. THE PROPOSED KNOWLEDGE-BASED GA PROBLEM REPRESENTATION EVALUATION METHOD GENETIC OPERATORS Represented by orderly Distinguishes whether Crossover numbered grids , each path is feasible or not. Mutation of which represents a Indicates difference between Node-Repair location in the environment. path qualities in either Line-Repair category. Improvement Deletion GRID REPRESENTATION EVALUATION FUNCTION
- 16. U SHAPE OBSTACLE ENVIRONMENT FIGURE (a): BEST INITIAL SOLUTION (COST = 80.59) FIGURE (b): BEST SOLUTION IN GENERATION 8 (COST = 42.02) FIGURE (c): BEST SOLUTION IN GENERATION 22 (COST = 33.98) FIGURE (d): OPTIMAL PATH: BEST SOLUTION IN GENERATION 30 (COST = 29.10)
- 17. PATH PLANNING IN COMPLEX ENVIRONMENT FIGURE (a): PATH OBTAINED BY GA IN ONE TYPICAL RUN FIGURE (b): THREE ALTERNATIVE PATHS OBTAINED BY GA FROM DIFFERENT RUNS
- 18. PATH PLANNING IN A DYNAMIC ENVIRONMENT FIGURE (a): PATH OBTAINED IN THE ORIGINAL ENVIRONMENT FIGURE (b): PATH AFTER ADDING OBSTACLE FIGURE (c): PATH AFTER REMOVAL OF THE OBSTACLE
- 19. COMPARISON OF THE GA WITH AND WITHOUT SPECIALIZED OPERATORS SD: STANDARD DEVIATION Thus, the use of specialized operators improve performance of GA significantly. SPECIALIZED OPERATORS WITH WITHOUT NO. OF RUNS 20 20 BEST FOUND PATH COST MEAN 30.85 61.41 SD 0.67 13.54 NO. OF GENERATIONS MEAN 257 799 SD 123 470
- 21. THANK YOU !!