Motion planning is a fundamental research area in robotics. Sampling-based methods offer
an efficient solution for what is otherwise a rather challenging dilemma of path planning …

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Motion planning has received much attention over the past 40 years. More than 15 years
have passed since the introduction of the successful sampling-based approach known as …

Probabilistic RoadMaps (PRMs) are quite successful in solving complex and high-
dimensional motion planning problems. While particularly suited for multiple-query …

Planning optimal paths for large numbers of robots is computationally expensive. In this
thesis, we present a new framework for multirobot path planning called subdimensional …

Sampling-based algorithms offer computationally efficient, practical solutions to the path
finding problem in high-dimensional complex configuration spaces by approximately …

Path planning in changing environments with difficult regions, such as narrow passages and
obstacle boundaries, creates significant challenges. As the obstacles in W-space move …

A recent trend in optimal motion planning has broadened the research area toward the
hybridization of sampling, optimization, and grid-based approaches. A synergy from such …

Every motion made by a moving object is either planned implicitly, eg, human natural
movement from one point to another, or explicitly, eg, pre-planned information about where …

Narrow passages in changing environments create huge difficulties, since locations and
shapes of narrow passages in Configuration Space (C-space) change frequently. It is very …