TY - GEN

T1 - Efficient motion planning for problems lacking optimal substructure

AU - Salzman, Oren

AU - Hou, Brian

AU - Srinivasa, Siddhartha

N1 - Publisher Copyright: Copyright © 2017, Association for the Advancement of Artificial intelligence (www.aaai.org).

PY - 2017

Y1 - 2017

N2 - We consider the motion-planning problem of planning a collision-free path of a robot in the presence of risk zones. The robot is allowed to travel in these zones but is penalized in a super-linear fashion for consecutive accumulative time spent there. We suggest a natural cost function that balances path length and risk-exposure time. Specifically, we consider the discrete setting where we are given a graph, or a roadmap, and we wish to compute the minimal-cost path under this cost function. Interestingly, paths defined using our cost function do not have an optimal substructure. Namely, subpaths of an optimal path are not necessarily optimal. Thus, the Bellman condition is not satisfied and standard graph-search algorithms such as Dijkstra cannot be used. We present a path-finding algorithm, which can be seen as a natural generalization of Dijkstra's algorithm. Our algorithm runs in O ((nB · n) log(nB · n) + nB · m) time, where n and m are the number of vertices and edges of the graph, respectively, and tib is the number of intersections between edges and the boundary of the risk zone. We present simulations on robotic platforms demonstrating both the natural paths produced by our cost function and the computational efficiency of our algorithm.

AB - We consider the motion-planning problem of planning a collision-free path of a robot in the presence of risk zones. The robot is allowed to travel in these zones but is penalized in a super-linear fashion for consecutive accumulative time spent there. We suggest a natural cost function that balances path length and risk-exposure time. Specifically, we consider the discrete setting where we are given a graph, or a roadmap, and we wish to compute the minimal-cost path under this cost function. Interestingly, paths defined using our cost function do not have an optimal substructure. Namely, subpaths of an optimal path are not necessarily optimal. Thus, the Bellman condition is not satisfied and standard graph-search algorithms such as Dijkstra cannot be used. We present a path-finding algorithm, which can be seen as a natural generalization of Dijkstra's algorithm. Our algorithm runs in O ((nB · n) log(nB · n) + nB · m) time, where n and m are the number of vertices and edges of the graph, respectively, and tib is the number of intersections between edges and the boundary of the risk zone. We present simulations on robotic platforms demonstrating both the natural paths produced by our cost function and the computational efficiency of our algorithm.

UR - http://www.scopus.com/inward/record.url?scp=85030550197&partnerID=8YFLogxK

M3 - منشور من مؤتمر

T3 - Proceedings International Conference on Automated Planning and Scheduling, ICAPS

SP - 531

EP - 539

BT - Proceedings of the 27th International Conference on Automated Planning and Scheduling, ICAPS 2017

A2 - Barbulescu, Laura

A2 - Smith, Stephen F.

A2 - Mausam, null

A2 - Frank, Jeremy D.

T2 - 27th International Conference on Automated Planning and Scheduling, ICAPS 2017

Y2 - 18 June 2017 through 23 June 2017

ER -