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A Dynamic Load-Balancing Parallel Search for Enumerative Robot Path Planning

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Abstract

We present a parallel formulation for enumerative search in high dimensional spaces and apply it to planning paths for a 6-dof manipulator robot. Participating processors perform local A* search towards the goal configuration. To exploit all the processors at their maximum capacity at all times, a dynamic load-balancing scheme matches idle and busy processors for load transfer. For comparison purposes, we have also implemented an existing parallel static load-balancing formulation based on regular domain decomposition. Both methods achieved almost linear speed-up in our experiments. The two methods follow different search strategies in parallel and the implementation of the existing method (with tuned space decomposition) was more time efficient on average. However, the planning time of that method is highly dependent on the distribution of the search space among the processors and its tuned decomposition varies for different obstacle placements. Empirical selection of the space decomposition parameters for the existing method does not guarantee minimal planning time in all environments and leads to slower planning than our dynamic load-balancing method in some cases. The performance of the developed dynamic method is independent of the obstacle placements and the method can achieve consistent speed-up in all environments.

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Authors and Affiliations

  1. Department of Electrical and Computer Engineering, Queen’s University, Kingston, Ontario, Canada

    Babak Taati & Michael Greenspan

  2. School of Computing, Queen’s University, Kingston, Ontario, Canada

    Michael Greenspan

  3. School of Engineering Science, Simon Fraser University, Burnaby, BC, Canada

    Kamal Gupta

Authors
  1. Babak Taati
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  2. Michael Greenspan
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  3. Kamal Gupta
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Correspondence to Babak Taati.

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Taati, B., Greenspan, M. & Gupta, K. A Dynamic Load-Balancing Parallel Search for Enumerative Robot Path Planning. J Intell Robot Syst 47, 55–85 (2006). https://doi.org/10.1007/s10846-006-9067-z

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  • DOI: https://doi.org/10.1007/s10846-006-9067-z

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