This paper describes a novel method for nonholonomic robots of convex shape to avoid imminent collisions with moving obstacles. The method's purpose is to assist navigation in crowds by correcting steering from the robot's path planner or driver. We evaluate its performance using a custom simulator which replicates real crowd movements and corresponding metrics which quantify the agent's efficiency and the robot's impact on the crowd and count collisions. We implement and evaluate the method on the standing wheelchair Qolo. In our experiments, it drives in autonomous mode using on-board sensing (LiDAR, RGB-D camera and a system to track pedestrians) and avoids collisions with up to five pedestrians and passes through a door.

The evaluation of robot capabilities to navigate human crowds is essential to conceive new robots intended to operate in public spaces. This paper initiates the development of a benchmark tool to evaluate such capabilities; our long term vision is to provide the community with a simulation tool that generates virtual crowded environment to test robots, to establish standard scenarios and metrics to evaluate navigation techniques in terms of safety and efficiency, and thus, to install new methods to benchmarking robots' crowd navigation capabilities. This paper presents the architecture of the simulation tools, introduces first scenarios and evaluation metrics, as well as early results to demonstrate that our solution is relevant to be used as a benchmark tool.

We present a novel and intrinsically safe collision avoidance method for torque-or force-controlled robots. We propose to insert a dedicated module after the nominal controller into the existing feedback loop to blend the nominal control signal with repulsive forces derived from an artificial potential. This blending is regulated by the system's mechanical energy in a way that guarantees collision avoidance and at the same time allows navigating close to collisions. Although using well-known ingredients from previous reactive methods, our approach overcomes their limitations in respect of achieving reliability without significantly restricting the set of reachable configurations. We demonstrate the fitness of our approach by comparing it to a standard potential-based method in simulated experiments with a walking excavator.

We study the Acceleration Obstacle (AO) as a concept to enable a robot's navigation in human crowds. The AO's geometric properties are analyzed and a direct samplingfree algorithm is proposed to approximate its boundary by linear constraints. The resulting controller is formulated as a quadratic program and evaluated in interaction with simulated bi-directional crowd flow in a corridor. We compare it to alternative robotic controllers, considering the robot's and the crowd's performance and the robot's behavior with respect to emergent lanes. Our results indicate that the robot can achieve higher efficiency when being less integrated in lanes.