Autonomous Mobile Robots
Planning, Navigation and Simulation
- 1st Edition - September 1, 2023
- Imprint: Academic Press
- Author: Rahul Kala
- Language: English
- Paperback ISBN:9 7 8 - 0 - 4 4 3 - 1 8 9 0 8 - 1
- eBook ISBN:9 7 8 - 0 - 4 4 3 - 1 8 9 0 9 - 8
Autonomous Mobile Robots: Planning, Navigation, and Simulation presents detailed coverage of the domain of robotics in motion planning and associated topics in navigatio… Read more
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Request a sales quoteAutonomous Mobile Robots: Planning, Navigation, and Simulation presents detailed coverage of the domain of robotics in motion planning and associated topics in navigation. This book covers numerous base planning methods from diverse schools of learning, including deliberative planning methods, reactive planning methods, task planning methods, fusion of different methods, and cognitive architectures. It is a good resource for doing initial project work in robotics, providing an overview, methods and simulation software in one resource. For more advanced readers, it presents a variety of planning algorithms to choose from, presenting the tradeoffs between the algorithms to ascertain a good choice.
Finally, the book presents fusion mechanisms to design hybrid algorithms.
- Presents intuitive and practical coverage of all sub-problems of mobile robotics to enable easy comprehension of sophisticated modern-day robots
- Covers a wide variety of motion planning algorithms, giving a near-exhaustive treatment of the domain with thought provoking comparisons between algorithms
- Dives into detailed discussions on robot operating systems and other simulators to get hands-on knowledge without the need of in-house robots
Graduate students learning robotics and senior undergraduate robotics students.
- Cover image
- Title page
- Table of Contents
- Copyright
- Preface
- Acknowledgements
- Chapter 1: An introduction to robotics
- Abstract
- 1.1: Introduction
- 1.2: Application areas
- 1.3: Hardware perspectives
- 1.4: Kinematics
- 1.5: Computer vision
- Questions
- References
- Chapter 2: Localization and mapping
- Abstract
- 2.1: Introduction
- 2.2: AI primer
- 2.3: Localization primer
- 2.4: Mapping primer
- 2.5: Planning
- 2.6: Control
- 2.7: Tracking
- 2.8: Localization
- 2.9: Mapping
- 2.10: Simultaneous Localization and Mapping
- Questions
- References
- Chapter 3: Visual SLAM, planning, and control
- Abstract
- 3.1: Introduction
- 3.2: Visual simultaneous localization and mapping
- 3.3: Configuration space and problem formulation
- 3.4: Planning objectives
- 3.5: Assessment
- 3.6: Terminologies
- 3.7: Control
- 3.8: Bug algorithms
- Questions
- References
- Chapter 4: Intelligent graph search basics
- Abstract
- 4.1: Introduction
- 4.2: An overview of graphs
- 4.3: Breadth-first search
- 4.4: State space approach
- 4.5: Uniform cost search
- 4.6: A* algorithm
- 4.7: Adversarial search
- Questions
- References
- Chapter 5: Graph search-based motion planning
- Abstract
- 5.1: Introduction
- 5.2: Motion planning using A* algorithm
- 5.3: Planning with robot’s kinematics
- 5.4: Planning in dynamic environments with the D* algorithm
- 5.5: Lifelong planning A*
- 5.6: D* lite
- 5.7: Other variants
- Questions
- References
- Chapter 6: Configuration space and collision checking
- Abstract
- 6.1: Introduction
- 6.2: Configuration and configuration space
- 6.3: Collision detection and proximity query primer
- 6.4: Types of maps
- 6.5: Space partitioning
- 6.6: Bounding volume hierarchy
- 6.7: Continuous collision detection
- 6.8: Representations
- 6.9: State spaces
- Questions
- References
- Chapter 7: Roadmap and cell decomposition-based motion planning
- Abstract
- 7.1: Introduction
- 7.2: Roadmaps
- 7.3: Visibility graphs
- 7.4: Voronoi
- 7.5: Cell decomposition
- 7.6: Trapezoids
- 7.7: Other decompositions
- 7.8: Navigation mesh
- 7.9: Homotopy and homology
- Questions
- References
- Chapter 8: Probabilistic roadmap
- Abstract
- 8.1: Introduction to sampling-based motion planning
- 8.2: Probabilistic roadmaps
- 8.3: Sampling techniques
- 8.4: Edge connection strategies
- 8.5: Lazy PRM
- Questions
- References
- Chapter 9: Rapidly-exploring random trees
- Abstract
- 9.1: Introduction
- 9.2: The algorithm
- 9.3: RRT variants
- 9.4: Sampling-based roadmap of trees
- 9.5: Parallel implementations of RRT
- 9.6: Multi-tree approaches
- 9.7: Distance functions
- 9.8: Topological aspects of configuration spaces
- Questions
- References
- Chapter 10: Artificial potential field
- Abstract
- 10.1: Introduction
- 10.2: Artificial potential field
- 10.3: Working of artificial potential field in different settings
- 10.4: Problems with potential fields
- 10.5: Navigation functions
- 10.6: Social potential field
- 10.7: Elastic strip
- Questions
- References
- Chapter 11: Geometric and fuzzy logic-based motion planning
- Abstract
- 11.1: Introduction
- 11.2: Velocity obstacle method
- 11.3: Vector field histogram
- 11.4: Other geometric approaches
- 11.5: Fuzzy logic
- 11.6: Training
- Questions
- References
- Chapter 12: An introduction to machine learning and deep learning
- Abstract
- 12.1: Introduction
- 12.2: Neural network architecture
- 12.3: Learning
- 12.4: Limited connectivity and shared weight neural networks
- 12.5: Recurrent neural networks
- 12.6: Deep learning
- 12.7: Auto-encoders
- 12.8: Deep convolution neural networks
- 12.9: Long-short term memory networks
- 12.10: Adaptive neuro-fuzzy inference system
- Questions
- References
- Chapter 13: Learning from demonstrations for robotics
- Abstract
- 13.1: Introduction
- 13.2: Incorporating machine learning in SLAM
- 13.3: Dealing with a lack of data
- 13.4: Data set creation for supervised learning
- 13.5: Some other concepts and architectures
- 13.6: Robot motion planning with embedded neurons
- 13.7: Robot motion planning using behaviour cloning
- Questions
- References
- Chapter 14: Motion planning using reinforcement learning
- Abstract
- 14.1: Introduction
- 14.2: Planning in uncertainty
- 14.3: Reinforcement learning
- 14.4: Deep reinforcement learning
- 14.5: Pretraining using imitation learning from demonstrations
- 14.6: Inverse Reinforcement Learning
- 14.7: Reinforcement learning for motion planning
- 14.8: Partially observable Markov decision process
- Questions
- References
- Chapter 15: An introduction to evolutionary computation
- Abstract
- 15.1: Introduction
- 15.2: Genetic algorithms
- 15.3: Particle swarm optimization
- 15.4: Topologies
- 15.5: Differential evolution
- 15.6: Local search
- 15.7: Memetic computing
- Questions
- References
- Chapter 16: Evolutionary robot motion planning
- Abstract
- 16.1: Introduction
- 16.2: Diversity preservation
- 16.3: Multiobjective optimization
- 16.4: Path planning using a fixed size individual
- 16.5: Path planning using a variable sized individual
- 16.6: Evolutionary motion planning variants
- 16.7: Simulation results
- Questions
- References
- Chapter 17: Hybrid planning techniques
- Abstract
- 17.1: Introduction
- 17.2: Fusion of deliberative and reactive algorithms
- 17.3: Fusion of deliberative and reactive planning
- 17.4: Behaviours
- 17.5: Fusion of multiple behaviours
- 17.6: Behavioural finite state machines
- 17.7: Behaviour Trees
- 17.8: Fusion of cell decomposition and fuzzy logic
- 17.9: Fusion with deadlock avoidance
- 17.10: Bi-level genetic algorithm
- Questions
- References
- Chapter 18: Multi-robot motion planning
- Abstract
- 18.1: Multi-robot systems
- 18.2: Planning in multi-robot systems
- 18.3: Centralized motion planning
- 18.4: Decentralized motion planning
- 18.5: Path velocity decomposition
- 18.6: Repelling robot trajectories
- 18.7: Co-evolutionary approaches
- Questions
- References
- Chapter 19: Task planning approaches
- Abstract
- 19.1: Task planning in robotics
- 19.2: Representations
- 19.3: Backward search
- 19.4: GRAPHPLAN
- 19.5: Constraint satisfaction
- 19.6: Partial order planning
- 19.7: Integration of task and geometric planning
- 19.8: Temporal logic
- Questions
- References
- Chapter 20: Swarm and evolutionary robotics
- Abstract
- 20.1: Swarm robotics
- 20.2: Swarm robotics problems
- 20.3: Neuro-evolution
- 20.4: Evolutionary robotics
- 20.5: Simulations with ARGOS
- 20.6: Evolutionary mission planning
- Questions
- References
- Chapter 21: Simulation systems and case studies
- Abstract
- 21.1: Introduction
- 21.2: General simulation framework
- 21.3: Robot Operating System
- 21.4: Simulation software
- 21.5: Traffic simulation
- 21.6: Planning humanoids
- 21.7: Case studies
- Questions
- References
- Index
- Edition: 1
- Published: September 1, 2023
- Imprint: Academic Press
- No. of pages: 1088
- Language: English
- Paperback ISBN: 9780443189081
- eBook ISBN: 9780443189098
RK
Rahul Kala
Rahul Kala is an assistant professor at the Centre of Intelligent Robotics, Indian Institute of Information Technology, Allahabad, India, where he received his B.Tech. and M.Tech. degrees in information technology. He received his Ph.D. degree in cybernetics from the University of Reading, UK in 2013. Dr. Kala has authored four books, 100 scientific papers, and is an active reviewer of leading journals of the domain. He has received numerous scholarships and grants from the Government of India, and is a recipient of the Best PhD Dissertation award from the IEEE Intelligent Transportation Systems Society.
Affiliations and expertise
Assistant Professor, Robotics and Artificial Intelligence Laboratory, Indian Institute of Information Technology, Allahabad, IndiaRead Autonomous Mobile Robots on ScienceDirect