Avoiding Dense and Dynamic Obstacles in Enclosed Spaces: Application to Moving in Crowds
This work addresses the challenge of safe and reliable robot movement in crowded, enclosed spaces, which is incremental as it builds on existing flow-based methods with formal guarantees for dynamic obstacles.
The paper tackled the problem of robot navigation in dense, dynamic environments by developing a closed-form flow constraint method that guarantees obstacle avoidance and convergence to a target. The approach was successfully tested on an autonomous robot in static indoor settings, simulations with dense crowds, and a real-world outdoor marketplace, where the robot navigated through a diverse crowd with non-uniform motion patterns.
This paper presents a closed-form approach to constrain a flow within a given volume and around objects. The flow is guaranteed to converge and to stop at a single fixed point. We show that the obstacle avoidance problem can be inverted to enforce that the flow remains enclosed within a volume defined by a polygonal surface. We formally guarantee that such a flow will never contact the boundaries of the enclosing volume and obstacles, and will asymptotically converge towards an attractor. We further create smooth motion fields around obstacles with edges (e.g. tables). Both obstacles and enclosures may be time-varying, i.e. moving, expanding and shrinking. The technique enables a robot to navigate within an enclosed corridor while avoiding static and moving obstacles. It was applied on an autonomous robot (QOLO) in a static complex indoor environment, and also tested in simulations with dense crowds. The final proof of concept was performed in an outdoor environment in Lausanne. The QOLO-robot successfully traversed a marketplace in the center of town in presence of a diverse crowd with a non-uniform motion pattern.