Reverse Thinking Approach to Deceptive Path Planning Problems

Deceptive path planning (DPP) aims to find routes that reduce the chances of observers discovering the real goal before its attainment, which is essential for addressing public safety, strategic path planning, and preserving the confidentiality of logistics routes. Currently, no single metric is available to comprehensively evaluate the performance of deceptive paths. This paper introduces two new metrics, termed “Average Deception Degree” (ADD) and “Average Deception Intensity” (ADI) to measure the overall performance of a path. Unlike traditional methods that focus solely on planning paths from the start point to the endpoint, we propose a reverse planning approach in which paths are considered from the endpoint back to the start point. Inverting the path from the endpoint back to the start point yields a feasible DPP solution. Based on this concept, we extend the existing π d 1 ~ 4 method to propose a new approach, e _ π d 1 ~ 4 , and introduce two novel methods, Endpoint DPP_Q and LDP DPP_Q, based on the existing DPP_Q method. Experimental results demonstrate that e _ π d 1 ~ 4 achieves significant improvements over π d 1 ~ 4 (an overall average improvement of 8.07%). Furthermore, Endpoint DPP_Q and LDP DPP_Q effectively address the issue of local optima encountered by DPP_Q. Specifically, in scenarios where the real and false goals have distinctive distributions, Endpoint DPP_Q and LDP DPP_Q show notable enhancements over DPP_Q (approximately a 2.71% improvement observed in batch experiments on 10 × 10 maps). Finally, tests on larger maps from Moving-AI demonstrate that these improvements become more pronounced as the map size increases. The introduction of ADD, ADI and the three new methods significantly expand the applicability of π d 1 ~ 4 and DPP_Q in more complex scenarios.