  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.
