Since the start of modern computing, the studies of living organisms have inspired the progress in developing computers and intelligent machines. In particular, the methods of search and foraging are the benchmark problems for robotics and multi-agent systems. The highly developed theory of search and screening involves optimal search plans that ar

Presents a probabilistic and information-theoretic framework for a search for static or moving targets in discrete time and space.

Probabilistic Search for Tracking Targets uses an information-theoretic scheme to present a unified approach for known search methods to allow the development of new algorithms of search. The book addresses search methods under different constraints and assumptions, such as search uncertainty under incomplete information, probabilistic search scheme, observation errors, group testing, search games, distribution of search efforts, single and multiple targets and search agents, as well as online or offline search schemes. The proposed approach is associated with path planning techniques, optimal search algorithms, Markov decision models, decision trees, stochastic local search, artificial intelligence and heuristic information-seeking methods. Furthermore, this book presents novel methods of search for static and moving targets along with practical algorithms of partitioning and search and screening.

Probabilistic Search for Tracking Targets includes complete material for undergraduate and graduate courses in modern applications of probabilistic search, decision-making and group testing, and provides several directions for further research in the search theory.

The authors:

Provide a generalized information-theoretic approach to the problem of real-time search for both static and moving targets over a discrete space. Present a theoretical framework, which covers known information-theoretic algorithms of search, and forms a basis for development and analysis of different algorithms of search over probabilistic space. Use numerous examples of group testing, search and path planning algorithms to illustrate direct implementation in the form of running routines. Consider a relation of the suggested approach with known search theories and methods such as search and screening theory, search games, Markov decision process models of search, data mining methods, coding theory and decision trees. Discuss relevant search applications, such as quality-control search for nonconforming units in a batch or a military search for a hidden target.  Provide an accompanying website featuring the algorithms discussed throughout the book, along with practical implementations procedures.

Presents a probabilistic and information-theoretic framework for a search for static or moving targets in discrete time and space.

Probabilistic Search for Tracking Targets uses an information-theoretic scheme to present a unified approach for known search methods to allow the development of new algorithms of search. The book addresses search methods under different constraints and assumptions, such as search uncertainty under incomplete information, probabilistic search scheme, observation errors, group testing, search games, distribution of search efforts, single and multiple targets and search agents, as well as online or offline search schemes. The proposed approach is associated with path planning techniques, optimal search algorithms, Markov decision models, decision trees, stochastic local search, artificial intelligence and heuristic information-seeking methods. Furthermore, this book presents novel methods of search for static and moving targets along with practical algorithms of partitioning and search and screening.

Probabilistic Search for Tracking Targets includes complete material for undergraduate and graduate courses in modern applications of probabilistic search, decision-making and group testing, and provides several directions for further research in the search theory.

The authors:

Provide a generalized information-theoretic approach to the problem of real-time search for both static and moving targets over a discrete space. Present a theoretical framework, which covers known information-theoretic algorithms of search, and forms a basis for development and analysis of different algorithms of search over probabilistic space. Use numerous examples of group testing, search and path planning algorithms to illustrate direct implementation in the form of running routines. Consider a relation of the suggested approach with known search theories and methods such as search and screening theory, search games, Markov decision process models of search, data mining methods, coding theory and decision trees. Discuss relevant search applications, such as quality-control search for nonconforming units in a batch or a military search for a hidden target.  Provide an accompanying website featuring the algorithms discussed throughout the book, along with practical implementations procedures.

Offers a theoretical and practical guide to the communication and navigation of autonomous mobile robots and multi-robot systemsThis book covers the methods and algorithms for the navigation, motion planning, and control of mobile robots acting individually and in groups. It addresses methods of positioning in global and local coordinates systems, off-line and on-line path-planning, sensing and sensors fusion, algorithms of obstacle avoidance, swarming techniques and cooperative behavior. The book includes ready-to-use algorithms, numerical examples and simulations, which can be directly implemented in both simple and advanced mobile robots, and is accompanied by a website hosting codes, videos, and PowerPoint slidesAutonomous Mobile Robots and Multi-Robot Systems: Motion-Planning, Communication and Swarming consists of four main parts. The first looks at the models and algorithms of navigation and motion planning in global coordinates systems with complete information about the robot’s location and velocity. The second part considers the motion of the robots in the potential field, which is defined by the environmental states of the robot's expectations and knowledge. The robot's motion in the unknown environments and the corresponding tasks of environment mapping using sensed information is covered in the third part. The fourth part deals with the multi-robot systems and swarm dynamics in two and three dimensions. Provides a self-contained, theoretical guide to understanding mobile robot control and navigationFeatures implementable algorithms, numerical examples, and simulationsIncludes coverage of models of motion in global and local coordinates systems with and without direct communication between the robotsSupplemented by a companion website offering codes, videos, and PowerPoint slidesAutonomous Mobile Robots and Multi-Robot Systems: Motion-Planning, Communication and Swarming is an excellent tool for researchers, lecturers, senior undergraduate and graduate students, and engineers dealing with mobile robots and related issues.

Offers a theoretical and practical guide to the communication and navigation of autonomous mobile robots and multi-robot systems

This book covers the methods and algorithms for the navigation, motion planning, and control of mobile robots acting individually and in groups. It addresses methods of positioning in global and local coordinates systems, off-line and on-line path-planning, sensing and sensors fusion, algorithms of obstacle avoidance, swarming techniques and cooperative behavior. The book includes ready-to-use algorithms, numerical examples and simulations, which can be directly implemented in both simple and advanced mobile robots, and is accompanied by a website hosting codes, videos, and PowerPoint slides

Autonomous Mobile Robots and Multi-Robot Systems: Motion-Planning, Communication and Swarming consists of four main parts. The first looks at the models and algorithms of navigation and motion planning in global coordinates systems with complete information about the robot’s location and velocity. The second part considers the motion of the robots in the potential field, which is defined by the environmental states of the robot''s expectations and knowledge. The robot''s motion in the unknown environments and the corresponding tasks of environment mapping using sensed information is covered in the third part. The fourth part deals with the multi-robot systems and swarm dynamics in two and three dimensions.

Provides a self-contained, theoretical guide to understanding mobile robot control and navigation Features implementable algorithms, numerical examples, and simulations Includes coverage of models of motion in global and local coordinates systems with and without direct communication between the robots Supplemented by a companion website offering codes, videos, and PowerPoint slides

Autonomous Mobile Robots and Multi-Robot Systems: Motion-Planning, Communication and Swarming is an excellent tool for researchers, lecturers, senior undergraduate and graduate students, and engineers dealing with mobile robots and related issues.

Offers a theoretical and practical guide to the communication and navigation of autonomous mobile robots and multi-robot systems

This book covers the methods and algorithms for the navigation, motion planning, and control of mobile robots acting individually and in groups. It addresses methods of positioning in global and local coordinates systems, off-line and on-line path-planning, sensing and sensors fusion, algorithms of obstacle avoidance, swarming techniques and cooperative behavior. The book includes ready-to-use algorithms, numerical examples and simulations, which can be directly implemented in both simple and advanced mobile robots, and is accompanied by a website hosting codes, videos, and PowerPoint slides

Autonomous Mobile Robots and Multi-Robot Systems: Motion-Planning, Communication and Swarming consists of four main parts. The first looks at the models and algorithms of navigation and motion planning in global coordinates systems with complete information about the robot’s location and velocity. The second part considers the motion of the robots in the potential field, which is defined by the environmental states of the robot''s expectations and knowledge. The robot''s motion in the unknown environments and the corresponding tasks of environment mapping using sensed information is covered in the third part. The fourth part deals with the multi-robot systems and swarm dynamics in two and three dimensions.

Provides a self-contained, theoretical guide to understanding mobile robot control and navigation Features implementable algorithms, numerical examples, and simulations Includes coverage of models of motion in global and local coordinates systems with and without direct communication between the robots Supplemented by a companion website offering codes, videos, and PowerPoint slides

Autonomous Mobile Robots and Multi-Robot Systems: Motion-Planning, Communication and Swarming is an excellent tool for researchers, lecturers, senior undergraduate and graduate students, and engineers dealing with mobile robots and related issues.

Plan optimal multi-robot search paths despite imperfect sensor information When multiple robots must locate targets in presence of false positive and false negative detection errors, path planning becomes extraordinarily complex. Multi-Agent Search under Uncertainty addresses this challenge directly. Written by researchers with combined expertise spanning defense systems, applied mathematics, and machine learning, this book delivers both theoretical foundations in search and screening theory and ready-to-use algorithms for practical implementation. The book covers cooperative search and navigation methods for autonomous mobile agents operating with incomplete or noisy information. Readers learn how Deep Q-Learning enables robots to develop complex behaviors through trial-and-error interactions rather than pre-programmed instructions. Applications span search and rescue operations, military surveillance, environmental monitoring, and security systems. An accompanying website provides Python code for simulation practice. Key topics include: Value-based Q-Learning methods where robots learn expected rewards for specific actions in given states under sensor uncertainty conditionsMulti-agent reinforcement learning approaches for swarm robotics where multiple robots learn cooperatively to accomplish collaborative search tasksDeep reinforcement learning using neural networks to process high-dimensional sensory inputs and execute complex search and tracking behaviorsAlgorithms for finding and tracking both stationary and moving targets while minimizing detection time despite false negative and positive readingsTheoretical contributions to search and screening theory alongside practical algorithms validated in autonomous robotic systems developmentDesigned for graduate students and researchers in robotics and reinforcement learning, this book bridges advanced theory with practical application. Professional developers building autonomous systems will find algorithms tested in real-world robotic development.

Plan optimal multi-robot search paths despite imperfect sensor information When multiple robots must locate targets in presence of false positive and false negative detection errors, path planning becomes extraordinarily complex. Multi-Agent Search under Uncertainty addresses this challenge directly. Written by researchers with combined expertise spanning defense systems, applied mathematics, and machine learning, this book delivers both theoretical foundations in search and screening theory and ready-to-use algorithms for practical implementation. The book covers cooperative search and navigation methods for autonomous mobile agents operating with incomplete or noisy information. Readers learn how Deep Q-Learning enables robots to develop complex behaviors through trial-and-error interactions rather than pre-programmed instructions. Applications span search and rescue operations, military surveillance, environmental monitoring, and security systems. An accompanying website provides Python code for simulation practice. Key topics include: Value-based Q-Learning methods where robots learn expected rewards for specific actions in given states under sensor uncertainty conditions Multi-agent reinforcement learning approaches for swarm robotics where multiple robots learn cooperatively to accomplish collaborative search tasks Deep reinforcement learning using neural networks to process high-dimensional sensory inputs and execute complex search and tracking behaviors Algorithms for finding and tracking both stationary and moving targets while minimizing detection time despite false negative and positive readings Theoretical contributions to search and screening theory alongside practical algorithms validated in autonomous robotic systems development Designed for graduate students and researchers in robotics and reinforcement learning, this book bridges advanced theory with practical application. Professional developers building autonomous systems will find algorithms tested in real-world robotic development.

Since the start of modern computing, the studies of living organisms have inspired the progress in developing computers and intelligent machines. In particular, the methods of search and foraging are the benchmark problems for robotics and multi-agent systems. The highly developed theory of search and screening involves optimal search plans that are obtained by standard optimization techniques while the foraging theory addresses search plans that mimic the behavior of living foragers.Search and Foraging: Individual Motion and Swarm Dynamics examines how to program artificial search agents so that they demonstrate the same behavior as predicted by the foraging theory for living organisms. For cybernetics, this approach yields techniques that enable the best online search planning in varying environments. For biology, it allows reasonable insights regarding the internal activity of living organisms performing foraging tasks.The book discusses foraging theory as well as search and screening theory in the same mathematical and algorithmic framework. It presents an overview of the main ideas and methods of foraging and search theories, making the concepts of one theory accessible to specialists of the other. The book covers Brownian walks and Lévy flight models of individual foraging and corresponding diffusion models and algorithms of search and foraging in random environments both by single and multiple agents. It also describes the active Brownian motion models for swarm dynamics with corresponding Fokker–Planck equations. Numerical examples and laboratory verifications illustrate the application of both theories.

Since the start of modern computing, the studies of living organisms have inspired the progress in developing computers and intelligent machines. In particular, the methods of search and foraging are the benchmark problems for robotics and multi-agent systems. The highly developed theory of search and screening involves optimal search plans that ar

Presents a probabilistic and information-theoretic framework for a search for static or moving targets in discrete time and space.

Probabilistic Search for Tracking Targets uses an information-theoretic scheme to present a unified approach for known search methods to allow the development of new algorithms of search. The book addresses search methods under different constraints and assumptions, such as search uncertainty under incomplete information, probabilistic search scheme, observation errors, group testing, search games, distribution of search efforts, single and multiple targets and search agents, as well as online or offline search schemes. The proposed approach is associated with path planning techniques, optimal search algorithms, Markov decision models, decision trees, stochastic local search, artificial intelligence and heuristic information-seeking methods. Furthermore, this book presents novel methods of search for static and moving targets along with practical algorithms of partitioning and search and screening.

Probabilistic Search for Tracking Targets includes complete material for undergraduate and graduate courses in modern applications of probabilistic search, decision-making and group testing, and provides several directions for further research in the search theory.

The authors:

Provide a generalized information-theoretic approach to the problem of real-time search for both static and moving targets over a discrete space. Present a theoretical framework, which covers known information-theoretic algorithms of search, and forms a basis for development and analysis of different algorithms of search over probabilistic space. Use numerous examples of group testing, search and path planning algorithms to illustrate direct implementation in the form of running routines. Consider a relation of the suggested approach with known search theories and methods such as search and screening theory, search games, Markov decision process models of search, data mining methods, coding theory and decision trees. Discuss relevant search applications, such as quality-control search for nonconforming units in a batch or a military search for a hidden target.  Provide an accompanying website featuring the algorithms discussed throughout the book, along with practical implementations procedures.

Presents a probabilistic and information-theoretic framework for a search for static or moving targets in discrete time and space.

Probabilistic Search for Tracking Targets uses an information-theoretic scheme to present a unified approach for known search methods to allow the development of new algorithms of search. The book addresses search methods under different constraints and assumptions, such as search uncertainty under incomplete information, probabilistic search scheme, observation errors, group testing, search games, distribution of search efforts, single and multiple targets and search agents, as well as online or offline search schemes. The proposed approach is associated with path planning techniques, optimal search algorithms, Markov decision models, decision trees, stochastic local search, artificial intelligence and heuristic information-seeking methods. Furthermore, this book presents novel methods of search for static and moving targets along with practical algorithms of partitioning and search and screening.

Probabilistic Search for Tracking Targets includes complete material for undergraduate and graduate courses in modern applications of probabilistic search, decision-making and group testing, and provides several directions for further research in the search theory.

The authors:

Provide a generalized information-theoretic approach to the problem of real-time search for both static and moving targets over a discrete space. Present a theoretical framework, which covers known information-theoretic algorithms of search, and forms a basis for development and analysis of different algorithms of search over probabilistic space. Use numerous examples of group testing, search and path planning algorithms to illustrate direct implementation in the form of running routines. Consider a relation of the suggested approach with known search theories and methods such as search and screening theory, search games, Markov decision process models of search, data mining methods, coding theory and decision trees. Discuss relevant search applications, such as quality-control search for nonconforming units in a batch or a military search for a hidden target.  Provide an accompanying website featuring the algorithms discussed throughout the book, along with practical implementations procedures.

Offers a theoretical and practical guide to the communication and navigation of autonomous mobile robots and multi-robot systemsThis book covers the methods and algorithms for the navigation, motion planning, and control of mobile robots acting individually and in groups. It addresses methods of positioning in global and local coordinates systems, off-line and on-line path-planning, sensing and sensors fusion, algorithms of obstacle avoidance, swarming techniques and cooperative behavior. The book includes ready-to-use algorithms, numerical examples and simulations, which can be directly implemented in both simple and advanced mobile robots, and is accompanied by a website hosting codes, videos, and PowerPoint slidesAutonomous Mobile Robots and Multi-Robot Systems: Motion-Planning, Communication and Swarming consists of four main parts. The first looks at the models and algorithms of navigation and motion planning in global coordinates systems with complete information about the robot’s location and velocity. The second part considers the motion of the robots in the potential field, which is defined by the environmental states of the robot's expectations and knowledge. The robot's motion in the unknown environments and the corresponding tasks of environment mapping using sensed information is covered in the third part. The fourth part deals with the multi-robot systems and swarm dynamics in two and three dimensions. Provides a self-contained, theoretical guide to understanding mobile robot control and navigationFeatures implementable algorithms, numerical examples, and simulationsIncludes coverage of models of motion in global and local coordinates systems with and without direct communication between the robotsSupplemented by a companion website offering codes, videos, and PowerPoint slidesAutonomous Mobile Robots and Multi-Robot Systems: Motion-Planning, Communication and Swarming is an excellent tool for researchers, lecturers, senior undergraduate and graduate students, and engineers dealing with mobile robots and related issues.

Offers a theoretical and practical guide to the communication and navigation of autonomous mobile robots and multi-robot systems

This book covers the methods and algorithms for the navigation, motion planning, and control of mobile robots acting individually and in groups. It addresses methods of positioning in global and local coordinates systems, off-line and on-line path-planning, sensing and sensors fusion, algorithms of obstacle avoidance, swarming techniques and cooperative behavior. The book includes ready-to-use algorithms, numerical examples and simulations, which can be directly implemented in both simple and advanced mobile robots, and is accompanied by a website hosting codes, videos, and PowerPoint slides

Autonomous Mobile Robots and Multi-Robot Systems: Motion-Planning, Communication and Swarming consists of four main parts. The first looks at the models and algorithms of navigation and motion planning in global coordinates systems with complete information about the robot’s location and velocity. The second part considers the motion of the robots in the potential field, which is defined by the environmental states of the robot''s expectations and knowledge. The robot''s motion in the unknown environments and the corresponding tasks of environment mapping using sensed information is covered in the third part. The fourth part deals with the multi-robot systems and swarm dynamics in two and three dimensions.

Provides a self-contained, theoretical guide to understanding mobile robot control and navigation Features implementable algorithms, numerical examples, and simulations Includes coverage of models of motion in global and local coordinates systems with and without direct communication between the robots Supplemented by a companion website offering codes, videos, and PowerPoint slides

Autonomous Mobile Robots and Multi-Robot Systems: Motion-Planning, Communication and Swarming is an excellent tool for researchers, lecturers, senior undergraduate and graduate students, and engineers dealing with mobile robots and related issues.

Offers a theoretical and practical guide to the communication and navigation of autonomous mobile robots and multi-robot systems

This book covers the methods and algorithms for the navigation, motion planning, and control of mobile robots acting individually and in groups. It addresses methods of positioning in global and local coordinates systems, off-line and on-line path-planning, sensing and sensors fusion, algorithms of obstacle avoidance, swarming techniques and cooperative behavior. The book includes ready-to-use algorithms, numerical examples and simulations, which can be directly implemented in both simple and advanced mobile robots, and is accompanied by a website hosting codes, videos, and PowerPoint slides

Autonomous Mobile Robots and Multi-Robot Systems: Motion-Planning, Communication and Swarming consists of four main parts. The first looks at the models and algorithms of navigation and motion planning in global coordinates systems with complete information about the robot’s location and velocity. The second part considers the motion of the robots in the potential field, which is defined by the environmental states of the robot''s expectations and knowledge. The robot''s motion in the unknown environments and the corresponding tasks of environment mapping using sensed information is covered in the third part. The fourth part deals with the multi-robot systems and swarm dynamics in two and three dimensions.

Provides a self-contained, theoretical guide to understanding mobile robot control and navigation Features implementable algorithms, numerical examples, and simulations Includes coverage of models of motion in global and local coordinates systems with and without direct communication between the robots Supplemented by a companion website offering codes, videos, and PowerPoint slides

Autonomous Mobile Robots and Multi-Robot Systems: Motion-Planning, Communication and Swarming is an excellent tool for researchers, lecturers, senior undergraduate and graduate students, and engineers dealing with mobile robots and related issues.

Plan optimal multi-robot search paths despite imperfect sensor information When multiple robots must locate targets in presence of false positive and false negative detection errors, path planning becomes extraordinarily complex. Multi-Agent Search under Uncertainty addresses this challenge directly. Written by researchers with combined expertise spanning defense systems, applied mathematics, and machine learning, this book delivers both theoretical foundations in search and screening theory and ready-to-use algorithms for practical implementation. The book covers cooperative search and navigation methods for autonomous mobile agents operating with incomplete or noisy information. Readers learn how Deep Q-Learning enables robots to develop complex behaviors through trial-and-error interactions rather than pre-programmed instructions. Applications span search and rescue operations, military surveillance, environmental monitoring, and security systems. An accompanying website provides Python code for simulation practice. Key topics include: Value-based Q-Learning methods where robots learn expected rewards for specific actions in given states under sensor uncertainty conditionsMulti-agent reinforcement learning approaches for swarm robotics where multiple robots learn cooperatively to accomplish collaborative search tasksDeep reinforcement learning using neural networks to process high-dimensional sensory inputs and execute complex search and tracking behaviorsAlgorithms for finding and tracking both stationary and moving targets while minimizing detection time despite false negative and positive readingsTheoretical contributions to search and screening theory alongside practical algorithms validated in autonomous robotic systems developmentDesigned for graduate students and researchers in robotics and reinforcement learning, this book bridges advanced theory with practical application. Professional developers building autonomous systems will find algorithms tested in real-world robotic development.

Plan optimal multi-robot search paths despite imperfect sensor information When multiple robots must locate targets in presence of false positive and false negative detection errors, path planning becomes extraordinarily complex. Multi-Agent Search under Uncertainty addresses this challenge directly. Written by researchers with combined expertise spanning defense systems, applied mathematics, and machine learning, this book delivers both theoretical foundations in search and screening theory and ready-to-use algorithms for practical implementation. The book covers cooperative search and navigation methods for autonomous mobile agents operating with incomplete or noisy information. Readers learn how Deep Q-Learning enables robots to develop complex behaviors through trial-and-error interactions rather than pre-programmed instructions. Applications span search and rescue operations, military surveillance, environmental monitoring, and security systems. An accompanying website provides Python code for simulation practice. Key topics include: Value-based Q-Learning methods where robots learn expected rewards for specific actions in given states under sensor uncertainty conditions Multi-agent reinforcement learning approaches for swarm robotics where multiple robots learn cooperatively to accomplish collaborative search tasks Deep reinforcement learning using neural networks to process high-dimensional sensory inputs and execute complex search and tracking behaviors Algorithms for finding and tracking both stationary and moving targets while minimizing detection time despite false negative and positive readings Theoretical contributions to search and screening theory alongside practical algorithms validated in autonomous robotic systems development Designed for graduate students and researchers in robotics and reinforcement learning, this book bridges advanced theory with practical application. Professional developers building autonomous systems will find algorithms tested in real-world robotic development.

Since the start of modern computing, the studies of living organisms have inspired the progress in developing computers and intelligent machines. In particular, the methods of search and foraging are the benchmark problems for robotics and multi-agent systems. The highly developed theory of search and screening involves optimal search plans that are obtained by standard optimization techniques while the foraging theory addresses search plans that mimic the behavior of living foragers.Search and Foraging: Individual Motion and Swarm Dynamics examines how to program artificial search agents so that they demonstrate the same behavior as predicted by the foraging theory for living organisms. For cybernetics, this approach yields techniques that enable the best online search planning in varying environments. For biology, it allows reasonable insights regarding the internal activity of living organisms performing foraging tasks.The book discusses foraging theory as well as search and screening theory in the same mathematical and algorithmic framework. It presents an overview of the main ideas and methods of foraging and search theories, making the concepts of one theory accessible to specialists of the other. The book covers Brownian walks and Lévy flight models of individual foraging and corresponding diffusion models and algorithms of search and foraging in random environments both by single and multiple agents. It also describes the active Brownian motion models for swarm dynamics with corresponding Fokker–Planck equations. Numerical examples and laboratory verifications illustrate the application of both theories.