Page 10 - Innovations in Intelligent Machines
P. 10
X Contents
4 Meta-Analysis of the Experimental
and Modeling Prediction methods . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 33
5 Conclusions.................................................. 36
References . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 36
Team, Game, and Negotiation based Intelligent Autonomous
UAV Task Allocation for Wide Area Applications
P.B. Sujit, A. Sinha, and D. Ghose ................................ 39
1 Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 39
2 Existing Literature ........................................... 41
3 Task Allocation Using Team Theory . . . . . . . . . . . . . . . . . . . . . . . . . . . 42
3.1 Basics of Team Theory .................................. 42
3.2 Problem Formulation . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 43
3.3 Team Theoretic Solution . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 45
3.4 Simulation Results . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 47
4 Task Allocation using Negotiation . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 50
4.1 Problem Formulation . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 50
4.2 Decision-making ........................................ 53
4.3 Simulation Results . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 58
5 Search using Game Theoretic Strategies ........................ 61
5.1 N-person Game Model ................................... 62
5.2 Solution Concepts . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 63
5.3 Simulation Results . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 69
6 Conclusions ................................................. 72
References . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 72
UAV Path Planning Using Evolutionary Algorithms
Ioannis K. Nikolos, Eleftherios S. Zografos, and Athina N. Brintaki .... 77
1 Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 77
1.1 Basic Definitions. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 77
1.2 Cooperative Robotics . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 79
1.3 Path Planning for Single and Multiple UAVs . . . . . . . . . . . . . . . . 80
1.4 Outline of the Current Work . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 85
2 B-Spline and Evolutionary Algorithms Fundamentals . . . . . . . . . . . . . 86
2.1 B-Spline Curves ......................................... 86
2.2 Fundamentals of Evolutionary Algorithms (EAs) . . . . . . . . . . . . 88
2.3 The Solid Boundary Representation . . . . . . . . . . . . . . . . . . . . . . . 89
3 Off-line Path Planner for a Single UAV . . . . . . . . . . . . . . . . . . . . . . . . . 90
4 Coordinated UAV Path Planning ............................... 92
4.1 Constraints and Objectives ............................... 92
4.2 Path Modeling Using B-Spline Curves ..................... 93
4.3 Objective Function Formulation . . . . . . . . . . . . . . . . . . . . . . . . . . . 94
5 The Optimization Procedure . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 97
5.1 Differential Evolution Algorithm . . . . . . . . . . . . . . . . . . . . . . . . . . 97
5.2 Radial Basis Function Network for DE Assistance . . . . . . . . . . . 99
