Robot swarms hold immense potential for performing complex tasks far beyond the capabilities of individual robots. However, the challenge in unleashing this potential is the robots' limited sensory capabilities, which hinder their ability to detect and adapt to unknown obstacles in real-time. To overcome this limitation, we introduce a novel robot swarm control method with an indirect obstacle detector using a smoothed particle hydrodynamics (SPH) model. The indirect obstacle detector can predict the collision with an obstacle and its collision point solely from the robot's velocity information. This approach enables the swarm to effectively and accurately navigate environments without the need for explicit obstacle detection, significantly enhancing their operational robustness and efficiency. Our method's superiority is quantitatively validated through a comparative analysis, showcasing its significant navigation and pattern formation improvements under obstacle-unaware conditions.

翻译：机器人集群在执行远超单个机器人能力的复杂任务方面具有巨大潜力。然而，释放这种潜力的挑战在于机器人有限的感知能力，这阻碍了它们实时检测并适应未知障碍物的能力。为克服这一限制，我们提出了一种新颖的机器人集群控制方法，该方法利用平滑粒子流体动力学（SPH）模型实现了间接障碍物检测器。该间接障碍物检测器仅凭机器人的速度信息即可预测与障碍物的碰撞及其碰撞点。这种方法使集群能够在无需显式障碍物检测的情况下，有效且精准地导航环境，显著增强了其操作鲁棒性和效率。通过对比分析，我们方法的优越性得到了定量验证，展示了在障碍物未知条件下导航与队形生成的显著改进。