Exploring Adversarial Obstacle Attacks in Search-based Path Planning for Autonomous Mobile Robots

2025-04-08

AI Summary

This work investigates the adversarial vulnerability of search-based path planning algorithms (e.g., A*) in autonomous mobile robots under malicious obstacle injection attacks by adversaries. Such attacks deploy false obstacles to induce detours and increase time-to-goal. We first systematically reveal how environmental constraints—particularly narrow passages—amplify the degradation of planning robustness. To rigorously evaluate this vulnerability, we establish a hybrid assessment framework integrating simulation (Gazebo/TurtleBot) and real-world deployment (Unitree Go1), complemented by a custom obstacle-injection toolkit and a novel metric for quantifying replanning latency. Experimental results show an average replanning delay of 36% in simulation, while the physical robot achieves 100% successful replanning success rate. Crucially, narrow environments exacerbate latency significantly, demonstrating that path planning robustness is highly contingent on operational context—not solely on algorithmic design.

Technology Category

• Planning, Routing, and Scheduling: Replanning and Plan Repair
• Intelligent Robots: Motion and Path Planning
• Search and Optimization: Adversarial Search

Application Category

• Responsible Web: Human-perceived consequences of algorithmic deployment on the web
• Search and Retrieval-Augmented AI: Agentic search
• Systems and Infrastructure for Web, Mobile and WoT: Applied ML and AI for Web-based mobile applications

Abstract

Path planning algorithms, such as the search-based A*, are a critical component of autonomous mobile robotics, enabling robots to navigate from a starting point to a destination efficiently and safely. We investigated the resilience of the A* algorithm in the face of potential adversarial interventions known as obstacle attacks. The adversary's goal is to delay the robot's timely arrival at its destination by introducing obstacles along its original path. We developed malicious software to execute the attacks and conducted experiments to assess their impact, both in simulation using TurtleBot in Gazebo and in real-world deployment with the Unitree Go1 robot. In simulation, the attacks resulted in an average delay of 36%, with the most significant delays occurring in scenarios where the robot was forced to take substantially longer alternative paths. In real-world experiments, the delays were even more pronounced, with all attacks successfully rerouting the robot and causing measurable disruptions. These results highlight that the algorithm's robustness is not solely an attribute of its design but is significantly influenced by the operational environment. For example, in constrained environments like tunnels, the delays were maximized due to the limited availability of alternative routes.

Problem

• Assessing A* algorithm resilience against adversarial obstacle attacks.
• Evaluating attack impact on robot path planning in simulations and real-world.
• Analyzing environmental constraints' effect on attack-induced delays.

Innovation

• Adversarial obstacle attacks on A* algorithm
• Simulation and real-world attack experiments
• Environment impacts algorithm robustness significantly