Near-optimal search-and-rescue path planning for a moving target

Abstract

Discrete search and rescue path planning for a moving target problem is computationally hard. Despite heuristic methods proposed so far, for which efficiency is further plagued by random episodic target kinematic behavior, qualifying real solution goodness and specifying optimality gap still remain elusive. In this paper a new alternate formulation is proposed to solve the search path planning problem for a moving target. Rather than tackling explicitly the original problem model, a remarkable property is exploited to solve a conjugate mixed-integer linear programming problem. The approach lies on a compact open-loop search path planning problem model with anticipated feedback to efficiently minimize probability of detection failure as opposed to traditionally maximize cumulative probability of success. Preserving path solution optimality, solving the conjugate problem proves very valuable in significantly reducing computational complexity. This is mainly achievable by virtue of the resulting problem objective property which concisely defines the function to be minimized in terms of terminal belief contributions only. For the first time optimal or upper bound solution computation are made possible in reasonable time for practical size problems. A network representation is further utilized to simplify modeling and facilitate constraint specification. Computational results show the strength of the approach for one and two searching agent problem instances.

Keywords:

• Linear programming
• search path planning
• search and rescue
• moving target

Disclosure statement

No potential conflict of interest was reported by the author(s).

Acknowledgements

The authors would like to offer special thanks to anonymous reviewers for providing helpful enlightening comments and critical guidance to improve readability of the paper.

Additional information

Funding

The reported work was co-funded by Defence Research and Development Canada Valcartier and SATWII Solutions Inc., Quebec City, Canada.