Efficient tracking and pursuit of moving targets by heuristic solution of the traveling salesman problem

We consider a planning and control problem in which a large number of moving targets must be intercepted by a single agent as quickly as possible. The agent maintains estimates of the instantaneous position and velocity of all targets, and decides which target to pursue next by predicting their future positions. Decision-making is driven by the repeated heuristic solution of the traveling salesman problem (TSP); for which the Lin-Kernighan heuristic (LKH) is compared to a greedy heuristic over different problem parameterizations. We show that the benefit of a non-greedy solution depends on the speed of the targets relative to the agent, and the precision of the measurement process used to track the targets. LKH is superior to a greedy heuristic when the targets are moving at low speed, and its relative performance improves as sensor noise worsens.