Receding Horizon Multi-Agent Deceptive Path Planner

TL;DR

This paper introduces a receding-horizon framework for multi-agent deceptive path planning that balances deception, resource use, and smoothness, enabling online adaptation without full-horizon recomputation.

Contribution

It presents a unified, online, and adaptable approach using short-horizon trajectories and a Boltzmann distribution, improving scalability and flexibility over prior full-horizon methods.

Findings

Supports dynamic deception tuning in simulation

Maintains deception while adapting to environmental changes

Avoids expensive full-horizon recomputation

Abstract

Deceptive path planning enables autonomous agents to obscure their true goals from observers by deviating from an expected optimal path. Prior work largely solves full-horizon, end-to-end optimization for single agents, which is expensive to recompute online and difficult to scale or adapt en route. We propose a unified framework for deceptive path planning using a Boltzmann distribution, computing over short-horizon candidate trajectories within a receding-horizon loop. By param- By iterating a user-defined cost that captures deception, resources, and smoothness, and optionally includes coupling terms between agents, the framework yields stochastic policies that balance the tradeoff between optimal paths and deceptive deviation. Policies are updated locally and do not require training. The level of deception and adherence to constraints can be dynamically tuned, enabling online adaptation to changes in goals and constraints such as obstacles. This step-by-step tuning opens the door to new forms of dynamic deception. Simulation studies demonstrate the flexibility of our approach, maintaining deception while adapting to environmental and constraint updates, avoiding the recomputation required by full-horizon methods, and supporting intuitive tuning via a small set of parameters