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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Book ChapterDOI
On a Hybrid Genetic Algorithm Solving a Global Path Planning for a Ground Mobile Robot
Piotr Bigaj,Jakub Bartoszek +1 more
TL;DR: A general framework for one of the key issues of modern mobile robotics is presented and a set of genotype improvement operators and specialized crossover operator have been proposed as a hybridization of an algorithm.
Book ChapterDOI
Path Planning in Changing Environments Based on “Frame” Difference
Zhi Wang,Fang Xiao,Hong Liu +2 more
TL;DR: HDA which is a hybrid of adjacent frame difference or K-frame difference is proposed which provides enough movement information of obstacles, and leads to safe path planning and lower replanning times and higher success rate than related planners.
Proceedings ArticleDOI
Computation of Approximate Solutions for Guided Sampling-Based Motion Planning of 3D Objects
Vojtech Vonasek,Robert Penicka +1 more
TL;DR: A modification of the iterative guiding process to avoid a situation where the part of the guiding path is too close to obstacles of the configuration space, which requires to estimate the surface of the obstacle region, which is achieved by detecting its boundary configurations during the sampling process.
The Impact of Approximate Methods on Local Learning in Motion Planning
TL;DR: It is shown that the impact of noise on learning depends on how much learning needs to take place given the topology of the environment, and a correlation between heterogeneity and the need for learning over a local region is demonstrated.
Proceedings ArticleDOI
Fast Convergence RRT for asymptotically-optimal Motion Planning
Risheng Kang,Hong Liu,Zhi Wang +2 more
TL;DR: Experimental results indicate that FCRRT significantly improves the exploration rate and success rate, and finds paths of a similar quality much more quickly compared to RRT*.
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.