研究搜索类路径规划在恶意障碍攻击下的脆弱性，发现算法易被干扰导致严重延迟。
Exploring Adversarial Obstacle Attacks in Search-based Path Planning for Autonomous Mobile Robots
- 设计恶意软件模拟障碍攻击，干扰A*算法路径规划
- 仿真中平均延迟36%，真实机器人实验延迟更严重
- 受限环境如隧道中攻击效果最显著，适合安全评估参考
路径规划算法（如基于搜索的A*）是自主移动机器人实现高效安全导航的关键。本文研究了A*算法在对抗性障碍攻击下的鲁棒性，即攻击者通过在原路径上添加障碍物，以延迟机器人到达目的地。我们开发了恶意软件执行攻击，并在Gazebo中使用TurtleBot进行仿真，以及在真实环境中部署Unitree Go1机器人验证。仿真结果显示，平均延迟达36%，尤其在需走大幅延长的替代路径时延迟最大；真实实验中所有攻击均成功重导路径并造成可测量干扰。结果表明，算法鲁棒性不仅取决于自身设计，更受运行环境影响，例如在隧道等受限空间中，因可选路径少，延迟最为严重。
原文摘要 · Abstract (English)
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.
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