Planning algorithms are impacting technical disciplines and industries around the world, including robotics, computer-aided design, manufacturing, computer graphics, aerospace applications, drug design, and protein folding. This coherent and comprehensive book unifies material from several sources, including robotics, control theory, artificial intelligence, and algorithms. The treatment is centered on robot motion planning but integrates material on planning in discrete spaces. A major part of the book is devoted to planning under uncertainty, including decision theory, Markov decision processes, and information spaces, which are the “configuration spaces” of all sensor-based planning problems. The last part of the book delves into planning under differential constraints that arise when automating the motions of virtually any mechanical system. Developed from courses taught by the author, the book is intended for students, engineers, and researchers in robotics, artificial intelligence, and control theory as well as computer graphics, algorithms, and computational biology.

Planning Algorithms: Introductory Material

A new motion planning method for robots in static workspaces is presented. This method proceeds in two phases: a learning phase and a query phase. In the learning phase, a probabilistic roadmap is constructed and stored as a graph whose nodes correspond to collision-free configurations and whose edges correspond to feasible paths between these configurations. These paths are computed using a simple and fast local planner. In the query phase, any given start and goal configurations of the robot are connected to two nodes of the roadmap; the roadmap is then searched for a path joining these two nodes. The method is general and easy to implement. It can be applied to virtually any type of holonomic robot. It requires selecting certain parameters (e.g., the duration of the learning phase) whose values depend on the scene, that is the robot and its workspace. But these values turn out to be relatively easy to choose, Increased efficiency can also be achieved by tailoring some components of the method (e.g., the local planner) to the considered robots. In this paper the method is applied to planar articulated robots with many degrees of freedom. Experimental results show that path planning can be done in a fraction of a second on a contemporary workstation (/spl ap/150 MIPS), after learning for relatively short periods of time (a few dozen seconds).

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Probabilistic roadmaps for path planning in high-dimensional configuration spaces

This paper presents the first randomized approach to kinodynamic planning (also known as trajectory planning or trajectory design). The task is to determine control inputs to drive a robot from an ...

Randomized kinodynamic planning

A text that makes the mathematical underpinnings of robot motion accessible and relates low-level details of implementation to high-level algorithmic concepts. Robot motion planning has become a major focus of robotics. Research findings can be applied not only to robotics but to planning routes on circuit boards, directing digital actors in computer graphics, robot-assisted surgery and medicine, and in novel areas such as drug design and protein folding. This text reflects the great advances that have taken place in the last ten years, including sensor-based planning, probabalistic planning, localization and mapping, and motion planning for dynamic and nonholonomic systems. Its presentation makes the mathematical underpinnings of robot motion accessible to students of computer science and engineering, rleating low-level implementation details to high-level algorithmic concepts.

Principles of Robot Motion: Theory, Algorithms, and Implementations

In this paper, we propose a new concept - the "Reciprocal Velocity Obstacle"- for real-time multi-agent navigation. We consider the case in which each agent navigates independently without explicit communication with other agents. Our formulation is an extension of the Velocity Obstacle concept [3], which was introduced for navigation among (passively) moving obstacles. Our approach takes into account the reactive behavior of the other agents by implicitly assuming that the other agents make a similar collision-avoidance reasoning. We show that this method guarantees safe and oscillation- free motions for each of the agents. We apply our concept to navigation of hundreds of agents in densely populated environments containing both static and moving obstacles, and we show that real-time and scalable performance is achieved in such challenging scenarios.

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Reciprocal Velocity Obstacles for real-time multi-agent navigation

Coordinated Path Planning for Multiple Robots

A probabilistic learning approach to motion planning

We present a new and complete multilevel approachfor solving path- planning problems for nonholonomic robots. At the first level, a path is found that disrespects (some of) the nonholonomic constraints. At each of the next levels, a new path is generated by transformation of the path generated at the previous level. The transformation is such that more nonholonomic constraints are respected than at the previous level. At the final level, all nonholonomic constraints are respected.We present two techniques for these transformations. The first, which we call the pick and link technique, repeatedly picks pieces from the given path, and tries to replace them by more feasible ones. The second technique restricts the free configuration space to a "tube" around the given path, and a road map that captures the free-space connectivity within this tube is constructed by the prob abilistic path planner. From this road map we retrieve a new, more feasible path.In the intermediate levels, we plan paths for what we ref...

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Multilevel Path Planning for Nonholonomic Robots Using Semiholonomic Subsystems

In this article, a recently developed learning approach for robot motion planning is extended and applied to two types of carlike robots: normal ones, and robots that can only move for ward. In this learning approach the motion planning process is split into two phases: the learning phase and the query phase. In the learning phase, a probabilistic roadmap is incremen tally constructed in configuration space. This roadmap is an undirected graph where nodes correspond to randomly chosen configurations in free space and edges correspond to simple collision-free paths between the nodes. These simple motions are computed using a fast local method. In the query phase, this roadmap can be used to find paths between different pairs of configurations.The approach can be applied to normal carlike robots (with nonholonomic constraints) by using suitable local methods that compute paths feasible for the robots. Application to carlike robots that can move only forward demands a more fundamen tal adaptation of the lear...

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Petr Švestka

Papers

Coordinated Path Planning for Multiple Robots

A probabilistic learning approach to motion planning

Multilevel Path Planning for Nonholonomic Robots Using Semiholonomic Subsystems

Motion planning for carlike robots using a probabilistic learning approach

Probabilistic path planning