Gross motion planning—a survey

Abstract

Motion planning is one of the most important areas of robotics research. The complexity of the motion-planning problem has hindered the development of practical algorithms. 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. The general issues in motion planning are explained. Recent approaches and their performances are briefly described, and possible future research directions are discussed.

Team AnnieWAY's autonomous system for the 2007 DARPA Urban Challenge

“…A * search is a well-known concept in the domain of robotic path planning (Hwang & Ahuja, 1992) that allows for accelerating exploration of the search space by defining a cost function that gives a lower bound of expected cost to go for each node of the search graph. If the cost function underestimates the actual distance to the goal, A * is guaranteed to find the least-cost path.…”

Combinatorial Motion Planning

“…Early approaches include [14,25,35,48,49,52,53,125,138]. The Gilbert-Johnson-Keerthi algorithm [62] is an early collision detection approach that helped inspire sampling-based motion planning; see [80] and [102] for many early references. In much of the early work, randomization appeared to be the main selling point; however, more recently it has been understood that deterministic sampling can work at least as well while obtaining resolution completeness.…”

Safe planning for human-robot interaction

“…In general, it is always more efficient to search from the cluttered end of the path. 30,31 The inverse kinematics routine is used to generate the goal configuration, given the goal workspace position and the desired end effector orientation at the goal. The search is then initiated backwards from the goal configuration towards the start configuration.…”