Fluidic robot swarm control for obstacle-unaware navigation

Swarm robotics holds significant potential for executing complex tasks that surpass the capabilities of individual robots. However, when robots have limited sensing abilities, their actions can be obstructed by unknown obstacles in the environment, thereby constraining this potential. To address this limitation, we propose a novel swarm robot controller that enables obstacle avoidance without sensors for detecting obstacles. Our proposed controller achieves efficient obstacle avoidance through a combination of indirect collision detection and fluidic swarm behavior using a Smoothed Particle Hydrodynamics (SPH)-based controller. The indirect collision detector utilizes only the velocity data of robots to detect collisions, thereby obtaining obstacle information without relying on specialized sensors. Furthermore, integrating collision points as virtual forces into the SPH-based controller enables effective obstacle avoidance. Our method's superiority is quantitatively validated through a comparative analysis, showcasing its significant navigation improvements under obstacle-unaware conditions. Furthermore, various investigations demonstrate the controller's utility, including pattern formation tasks and navigation tasks in dynamic obstacle environments.