IIIT Hyderabad Publications
K-Means derived strategized placement of starting points for parallel RRT’s
Authors: K. Madhava Krishna
Report no: IIIT/TR/2016/4
Initial placement of RRT plays an important role when it comes down to improving performance in terms of uniform exploration of map. Algorithms like Distributed , K-Distributed for parallelising of RRT have left the question of initial placement of RRT modules unanswered. Strategised placement of RRT modules can yield parametrically faster performance with a more homogeneous exploration as compared to designs where placement is done randomly. For n > m, m RRTs with strategised placement can perform better than n RRTs with non-strategised or random placement. This strategised placement of RRTs is achieved by clustering the map in m clusters using complex implementation of K-Means clustering algorithm which is sensitive to geometrical constraints of the map. With our work, we propose strategised placement of starting points for RRT that can yield faster performance with a more homogeneous exploration.
Full report: pdf
Centre for Robotics
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