Abstract
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.
Abstract (translated)
机器人群具有在远超过单个机器人的复杂任务中执行巨大潜力。然而，释放这一潜力的挑战是机器人的有限感知能力，这阻碍了它们在实时感知未知障碍的能力。为了克服这一限制，我们引入了一种使用平滑粒子流体动力学（SPH）模型的新型机器人群控制方法。这种间接障碍检测器可以通过机器人的速度信息仅预测与障碍的碰撞及其碰撞点。这种方法使群能够有效且准确地导航环境，而无需进行显式的障碍检测，显著提高了它们的操作稳健性和效率。通过对比分析验证，我们量化了该方法在无障碍条件下的导航和模式形成改善。
URL
https://arxiv.org/abs/2404.16309
