Open AccessProceedings Article
OBPRM: an obstacle-based PRM for 3D workspaces
Nancy M. Amato,O. Burchan Bayazit,Lucia K. Dale,Christopher Jones,Daniel Vallejo +4 more
- pp 155-168
TLDR
This paper presents a new class of randomized path planning methods, known as Probabilistic Roadmap Methods (prms), which use randomization to construct a graph of representative paths in C-space whose vertices correspond to collision-free con gurations of the robot.Abstract:
Recently, a new class of randomized path planning methods, known as Probabilistic Roadmap Methods (prms) have shown great potential for solving complicated high-dimensional problems. prms use randomization (usually during preprocessing) to construct a graph of representative paths in C-space (a roadmap) whose vertices correspond to collision-free con gurations of the robot and in which two vertices are connected by an edge if a path between the two corresponding con gurations can be found by a local planning method.read more
Citations
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Smooth feedback planning
TL;DR: A new algorithm for the construction of feedback controllers with global convergence, safety, and smoothness guarantees is presented that integrates motion planning's emphasis on collision avoidance and algorithmic completeness with control theory's insistence on the use of feedback to achieve robust, efficient, real time control.
Book ChapterDOI
An Efficient Random Walk Strategy for Sampling Based Robot Motion Planners
TL;DR: A new technique based on a random walk strategy to generate samples in narrow regions quickly, thus improving efficiency of Probabilistic Roadmap Planners and substantially reduces instances of collision checking and thereby decreases computational time.
Posted Content
Predicting Sample Collision with Neural Networks.
TL;DR: This work presents a framework to address the cost of expensive primitive operations in sampling-based motion planning, which determines the validity of a sample robot configuration through a novel combination of a Contractive AutoEncoder and a Multilayer Perceptron.
Physically-based sampling for motion planning
TL;DR: A novel approach based on physics-based sampling for motion planning that can compute collision-free paths while also satisfying many physical constraints is presented that can successfully plan for thousands of simple robots in real-world scenarios.
Journal ArticleDOI
One Way to Fill All the Concave Region in Grid-Based Map
TL;DR: The filling container algorithm is proposed to alleviate the concave area problem in 2D map space, which is inspired from the scenario of pouring water into a cup, and this method, concave areas can be largely excluded by scanning the map repeatedly.
References
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Book
Robot Motion Planning
TL;DR: This chapter discusses the configuration space of a Rigid Object, the challenges of dealing with uncertainty, and potential field methods for solving these problems.
Journal ArticleDOI
Probabilistic roadmaps for path planning in high-dimensional configuration spaces
TL;DR: 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).
Journal ArticleDOI
Robot motion planning: a distributed representation approach
TL;DR: A new approach to robot path planning that consists of building and searching a graph connecting the local minima of a potential function defined over the robot's configuration space is proposed and a planner based on this approach has been implemented.
Journal ArticleDOI
Gross motion planning—a survey
Yong K. Hwang,Narendra Ahuja +1 more
TL;DR: This paper surveys the work on gross-motion planning, including motion planners for point robots, rigid robots, and manipulators in stationary, time-varying, constrained, and movable-object environments.
Proceedings Article
Complexity of the Mover's Problem and Generalizations Extended Abstract
TL;DR: This paper concerns the problem of moving a polyhedron through Euclidean space while avoiding polyhedral obstacles.