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.

翻译：路径规划算法，例如基于搜索的A*算法，是自主移动机器人技术的关键组成部分，使机器人能够高效且安全地从起点导航至目的地。本研究考察了A*算法在面对潜在对抗性干预（称为障碍攻击）时的鲁棒性。攻击者的目标是通过在机器人原始路径上引入障碍物，延迟其按时抵达目的地。我们开发了恶意软件以执行攻击，并通过仿真（使用Gazebo中的TurtleBot）和实际部署（使用Unitree Go1机器人）进行了实验以评估其影响。在仿真中，攻击导致平均延迟达36%，其中最为显著的延迟发生在机器人被迫选择明显更长的替代路径的场景中。在实际实验中，延迟更为显著，所有攻击均成功使机器人改道并造成可测量的干扰。这些结果表明，算法的鲁棒性不仅取决于其设计，还显著受运行环境影响。例如，在隧道等受限环境中，由于替代路径有限，延迟被最大化。