Trajectory optimization under kinematical constraints for moving target search

Abstract

Various recent events in the Mediterranean sea have shown the enormous importance of maritime search-and-rescue missions. By reducing the time to find floating victims, the number of casualties can be reduced. A major improvement can be achieved by employing unmanned aerial systems for autonomous search missions. In this context, the need for efficient search trajectory planning methods arises. Existing approaches either consider K-step-lookahead optimization without accounting for kinematics of fixed-wing platforms or propose a suboptimal myopic method. A few approaches consider both aspects, however only applicable to stationary target search. The contribution of this article consists of a novel method for Markovian target search-trajectory optimization. This is a unified method for fixed-wing and rotary-wing platforms, taking kinematical constraints into account. It can be classified as K-step-lookahead planning method, which allows for anticipation to the estimated future position and motion of the target. The method consists of a mixed integer linear program that optimizes the cumulative probability of detection. We show the applicability and effectiveness in computational experiments for three types of moving targets: diffusing, conditionally deterministic, and Markovian. This approach is the first K-step-lookahead method for Markovian target search under kinematical constraints.