A discrete optimisation approach for target path planning whilst evading sensors

TL;DR

This paper presents a discrete optimization method for planning agent paths in military scenarios to evade sensors, incorporating sensor knockout and confusion actions, solved via integer programming and heuristics.

Contribution

It introduces a novel discrete optimization framework for target path planning with sensor evasion, including agent actions and constraints, solved with commercial solvers and heuristics.

Findings

Optimal solutions achieved for test problems

Heuristic provides near-optimal solutions efficiently

Publicly available test problems facilitate benchmarking

Abstract

In this paper we deal with a practical problem that arises in military mission planning. The problem is to plan a path for one, or more, agents to reach a target without being detected by enemy sensors. Agents are not passive, rather they can initiate actions which aid evasion. They can knockout sensors. Here to knockout a sensor means to completely disable the sensor. They can also confuse sensors. Here to confuse a sensor means to reduce the probability that the sensor can detect an agent. Agent actions are path dependent and time limited. By path dependent we mean that an agent needs to be sufficiently close to a sensor to knock it out. By time limited we mean that a limit is imposed on how long a sensor is knocked out or confused before it reverts back to its original operating state. The approach adopted breaks the continuous space in which agents move into a discrete space. This enables the problem to be formulated as a zero-one integer program with linear constraints. The advantage of representing the problem in this manner is that powerful commercial software optimisation packages exist to solve the problem to proven global optimality. A heuristic for the problem based on successive shortest paths is also presented. Computational results are presented for a number of randomly generated test problems that are made publicly available.