Skip to content Skip to navigation


Associated Projects

Kostas Bekris

The increasing availability of low-cost, compliant and human-friendly manipulators allows robots, such as Rethink Robotics' Baxter, to be placed in close proximity to human workers. Unlike traditional automation systems, which needed to be kept in cages, these compliant robots can share a common workspace with human workers. A clear benefit of this close proximity is the opportunity for cooperation between a human worker and an assistive robot.

Probabilistic roadmap planners utilize an offline phase to build up information about the configuration space (C-space) and solve many practical motion planning problems. Traditionally, many of these planners focus on feasibility and may return paths of low quality; considerably different from the optimal ones, where path quality can be measured in terms of length, clearance, or smoothness. Smoothing can be used to improve some of these measures and algorithms exist that produce roadmaps with paths that are deformable to optimal ones. Hybridization graphs combine multiple solutions into a higher quality one that uses the best portions of each input path. These techniques, however, can be expensive for the online resolution of a query, especially when multiple queries must be answered.

Many state of the art algorithms for motion planning are concerned with asymptotic optimality (variants of PRM* and RRT*).