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Robot path planning and cooperation : foundations, algorithms and experimentations

Author: Anis KoubâaHachemi BennaceurImen ChaariSahar TriguiAdel AmmarAll authors
Publisher: Cham, Switzerland : Springer, [2018]
Series: Studies in computational intelligence, v. 772.
Edition/Format:   Print book : EnglishView all editions and formats
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Document Type: Book
All Authors / Contributors: Anis Koubâa; Hachemi Bennaceur; Imen Chaari; Sahar Trigui; Adel Ammar; Mohamed-Foued Sriti; Maram Alajlan; Omar Cheikhrouhou; Yasir Javed
ISBN: 3319770403 9783319770406
OCLC Number: 1039625064
Description: xix, 190 pages : color illustrations ; 25 cm.
Contents: Machine generated contents note: pt. I Global Robot Path Planning --
1.Introduction to Mobile Robot Path Planning --
1.1.Introduction --
1.2.Overview of the Robot Path Planning Problem --
1.2.1.Problem Formulation --
1.3.Path Planning Categories --
1.4.Spatial Representations Commonly Used in Path Planning --
1.4.1.Environment Characterization --
1.4.2.Path Planning Complexity --
1.5.Conclusion --
References --
2.Background on Artificial Intelligence Algorithms for Global Path Planning --
2.1.Introduction --
2.2.Classical Approaches --
2.3.Graph Search Approaches --
2.3.1.The AStar (A*) Algorithm --
2.4.Heuristic Approaches --
2.4.1.Tabu Search --
2.4.2.Genetic Algorithms --
2.4.3.Neural Networks --
2.4.4.Ant Colony Optimization --
2.4.5.Hybrid Approaches --
2.4.6.Comparative Study of Heuristic and Exact Approaches --
2.4.7.Comparative Study of Heuristic Approaches --
2.4.8.Comparative Study of Exact Methods --
2.5.Conclusion --
References Note continued: 3.Design and Evaluation of Intelligent Global Path Planning Algorithms --
3.1.Introduction --
3.2.System Model --
3.3.Design of Exact and Heuristic Algorithms --
3.3.1.A Relaxed Version of A* for Robot Path Planning --
3.3.2.The Tabu Search Algorithm for Robot Path Planning (TS-PATH) --
3.3.3.The Genetic Algorithm for Robot Path Planning --
3.3.4.The Ant Colony Optimization Algorithm for Robot Path Planning --
3.4.Performance Analysis of Global Path Planning Techniques --
3.4.1.Simulation Environment --
3.4.2.Simulation Results --
3.5.Hybrid Algorithms for Robot Path Planning --
3.5.1.Design of Hybrid Path Planners --
3.5.2.Performance Evaluation --
3.6.Conclusion --
References --
4.Integration of Global Path Planners in ROS --
4.1.Introduction --
4.2.Navigation Stack --
4.2.1.Global Planner --
4.2.2.Local Planner --
4.3.How to Integrate a New Path Planner as Plugin? --
4.3.1.Writing the Path Planner Class --
4.3.2.Writing Your Plugin Note continued: 4.3.3.Running the Plugin --
4.4.ROS Environment Configuration --
4.5.Performance Evaluation --
4.6.Conclusion --
References --
5.Robot Path Planning Using Cloud Computing for Large Grid Maps --
5.1.Introduction --
5.2.Cloud Computing and Robotics --
5.3.Literature Review --
5.4.Hadoop: Overview --
5.4.1.Hadoop Architecture Overview --
5.5.Giraph: Overview --
5.5.1.Giraph Architecture --
5.5.2.The Bulk Synchronous Parallel Model --
5.6.Implementation of RA* Using Giraph --
5.7.Performance Evaluation --
5.7.1.Cloud Framework --
5.7.2.Experimental Scenarios --
5.7.3.Impact of Number of Workers --
5.7.4.Execution Times --
5.7.5.Total Number of Messages Exchanged, Memory Footprint and CPU Usage of RA* --
5.8.Lessons Learned --
5.9.Conclusion --
References --
pt. II Multi-robot Task Allocation --
6.General Background on Multi-robot Task Allocation --
6.1.Introduction --
6.2.The Multi-robot Task Allocation --
6.2.1.Centralized Approaches Note continued: 6.2.2.Distributed Approaches --
6.2.3.Market-Based Approaches --
6.3.The Multiple Traveling Salesman Problem --
6.3.1.MTSP Overview --
6.3.2.Related Works on MTSP --
6.3.3.Multi-objective Optimization Problem (MOP) --
6.4.Conclusion --
References --
7.Different Approaches to Solve the MRTA Problem --
7.1.Introduction --
7.2.Objective Functions --
7.3.Improved Distributed Market-Based Approach --
7.3.1.Distributed Market-Based (DMB) Algorithm --
7.3.2.Improvement Step --
7.4.Clustering Market-Based Coordination Approach --
7.4.1.CM-MTSP Algorithm Steps --
7.4.2.Illustrative Example --
7.5.Fuzzy Logic-Based Approach --
7.5.1.Fuzzy Logic Rules Design --
7.5.2.Algorithm Design --
7.6.Move-and-Improve: A Market-Based Multi-robot Approach for Solving the MD-MTSP --
7.7.Conclusion --
References --
8.Performance Analysis of the MRTA Approaches for Autonomous Mobile Robot --
8.1.Introduction --
8.2.Performance Evaluation of the IDMB Approach Note continued: 8.2.1.Simulation Study --
8.2.2.Experimentation --
8.3.Performance Evaluation of the CM-MTSP Approach --
8.3.1.Comparison of the CM-MTSP with a Single-Objective Algorithm --
8.3.2.Comparison of the CM-MTSP with a Greedy Algorithm --
8.4.Performance Evaluation of the FL-MTSP --
8.4.1.Impact of the Number of Target Locations --
8.4.2.Impact of the Number of Robots --
8.4.3.Comparison with MDMTSP_GA --
8.4.4.Comparison with NSGA-II --
8.4.5.Comparison Between FL-MTSP, MDMTSP_GA, MTSP_TT, and MTSP_MT Algorithms --
8.4.6.Impact of the TSP Solver on the Execution Time --
8.5.Performance Evaluation of the Move-and-Improve Approach --
8.6.Conclusion --
References.
Series Title: Studies in computational intelligence, v. 772.
Responsibility: Anis Koubaa, Hachemi Bennaceur, Imen Chaari, Sahar Trigui, Adel Ammar, Mohamed-Foued Sriti, Maram Alajlan, Omar Cheikhrouhou, Yasir Javed.

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