TUM Library

Intro -- Half Title -- Series Page -- Title Page -- Copyright Page -- Table of Contents -- Author -- Chapter 1 Multi-Aerial-Robot Planning -- 1.1 Introduction -- 1.2 Team Approach -- 1.2.1 Cooperation -- 1.2.2 Cascade-Type Guidance Law -- 1.2.3 Consensus Approach -- 1.2.3.1 Consensus Opinion -- 1.2.3.2 Reachability and Observability -- 1.2.4 Flocking Behavior -- 1.2.4.1 Collective Potential of Flocks -- 1.2.4.2 Distributed Flocking Algorithms -- 1.2.5 Connectivity and Convergence of Formations -- 1.2.5.1 Problem Formulation -- 1.2.5.2 Stability of Formations in Time-Invariant Communication

1.3 Deterministic Decision-Making -- 1.3.1 Distributed Receding Horizon Control -- 1.3.2 Conflict Resolution -- 1.3.2.1 Distributed Reactive Collision Avoidance -- 1.3.2.2 Deconfliction Maintenance -- 1.3.3 Artificial Potential -- 1.3.3.1 Velocity Field -- 1.3.3.2 Artificial Potential Field -- 1.3.3.3 Pattern Formation and Reconfigurability -- 1.3.4 Symbolic Planning -- 1.4 Association with Limited Communication -- 1.4.1 Introduction -- 1.4.2 Problem Formulation -- 1.4.2.1 Decentralized Resolution of Inconsistent Association -- 1.4.3 Genetic Algorithms -- 1.4.4 Games Theory Reasoning

1.4.4.1 Cooperative Protocol -- 1.4.4.2 Non-Cooperative Protocol -- 1.4.4.3 Leader/Follower Protocol -- 1.5 Multiagent Decision-Making under Uncertainty -- 1.5.1 Decentralized Team Decision Problem -- 1.5.1.1 Bayesian Strategy -- 1.5.1.2 Semi-Modeler Strategy -- 1.5.1.3 Communication Models -- 1.5.2 Algorithms for Optimal Planning -- 1.5.2.1 Multiagent A* (MAA*): A Heuristic Search Algorithm for DEC-POMDP -- 1.5.2.2 Policy Iteration for Infinite Horizon -- 1.5.2.3 Linear-Quadratic Approach -- 1.5.2.4 Decentralized Chance-Constrained Finite Horizon Optimal Control

1.5.3 Task Allocation: Optimal Assignment -- 1.5.3.1 Hungarian Algorithm -- 1.5.3.2 Interval Hungarian Algorithm -- 1.5.3.3 Quantifying the Effect of Uncertainty -- 1.5.3.4 Uncertainty Measurement for a Single Utility -- 1.5.4 Distributed Chance-Constrained Task Allocation -- 1.5.4.1 Chance-Constrained Task Allocation -- 1.5.4.2 Distributed Approximation to the Chance-Constrained Task Allocation Problem -- 1.6 Case Studies -- 1.6.1 Reconnaissance Mission -- 1.6.1.1 General Vehicle Routing Problem -- 1.6.1.2 Chinese Postman Problem -- 1.6.1.3 Cluster Algorithm -- 1.6.1.4 The Rural CPP

1.6.2 Expanding Grid Coverage -- 1.6.3 Optimization of Perimeter Patrol Operations -- 1.6.3.1 Multiagent Markov Decision Process -- 1.6.3.2 Anytime Error Minimization Search -- 1.6.4 Stochastic Strategies for Surveillance -- 1.6.4.1 Analysis Methods -- 1.6.4.2 Problems in 1D -- 1.6.4.3 Complete Graphs -- 1.7 Conclusions -- Chapter 2 Flight Planning -- 2.1 Introduction -- 2.2 Path and Trajectory Planning -- 2.2.1 Trim Trajectories -- 2.2.2 Trajectory Planning -- 2.2.2.1 Time Optimal Trajectories -- 2.2.2.2 Nonholonomic Motion Planning -- 2.2.3 Path Planning -- 2.2.3.1 B-Spline Formulation

Multi-robot systems are a major research topic in robotics. Designing, testing, and deploying aerial robots in the real world is a possibility due to recent technological advances. This book explores different aspects of cooperation in multiagent systems. It covers the team approach as well as deterministic decision-making. It also presents distributed receding horizon control, as well as conflict resolution, artificial potentials, and symbolic planning. The book also covers association with limited communications, as well as genetic algorithms and game theory reasoning. Multiagent decision-making and algorithms for optimal planning are also covered along with case studies. Key features: Provides a comprehensive introduction to multi-robot systems planning and task allocation Explores multi-robot aerial planning; flight planning; orienteering and coverage; and deployment, patrolling, and foraging Includes real-world case studies Treats different aspects of cooperation in multiagent systems Both scientists and practitioners in the field of robotics will find this text valuable.

OCLC-licensed vendor bibliographic record.