In this paper an optimal method for distributed collision avoidance among multiple non-holonomic robots is presented in theory and experiments. Non-holonomic optimal reciprocal collision avoidance (NH-ORCA) builds on the concepts introduced in [2], but further guarantees smooth and collision-free motions under non-holonomic constraints. Optimal control inputs and constraints in velocity space are formally derived for the non-holonomic robots. The theoretical results are validated in several collision avoidance experiments with up to fourteen e-puck robots set on collision course. Even in scenarios with very crowded situations, NH-ORCA showed to be collision-free for all times.

This paper presents a solution to decentralized Voronoi coverage in non-convex polygonal environments. We show that complications arise when existing approaches to Voronoi coverage are applied for deploying a group of robots in non-convex environments. We present an algorithm that is guaranteed to converge to a local optimum. Our algorithm combines classical Voronoi coverage with the Lloyd algorithm and the local path planning algorithm TangentBug to compute the motion of the robots around obstacles and corners. We present the algorithm and prove convergence and optimality. We also discuss experimental results from an implementation with five robots.

In this article we present a novel display that is created using a group of mobile robots. In contrast to traditional displays that are based on a fixed grid of pixels, such as a screen or a projection, this work describes a display in which each pixel is a mobile robot of controllable color. Pixels become mobile entities, and their positioning and motion are used to produce a novel experience. The system input is a single image or an animation created by an artist. The first stage is to generate physical goal configurations and robot colors to optimally represent the input imagery with the available number of robots. The run-time system includes goal assignment, path planning and local reciprocal collision avoidance, to guarantee smooth, fast and oscillation-free motion between images. The algorithms scale to very large robot swarms and extend to a wide range of robot kinematics. Experimental evaluation is done for two different physical swarms of size 14 and 50 differentially driven robots, and for simulations with 1,000 robot pixels.

International audienceIn the context of environment reconstruction for inspection, it is important to handle sensor noise properly to avoid distorted representations. A short survey of available sensors is realize to help their selection based on the payload capability of a robot. We then propose uncertainty models based on empirical results for three models of laser rangefinders: Hokuyo URG-04LX, UTM-30LX and the Sick LMS-151. The methodology, used to characterize those sensors, targets more specifically different metallic materials which often give distorted images due to reflexion. We also evaluate the impact of sensor noise on surface normal vector reconstruction and conclude with observations about the impact of sunlight and reflexions

We propose a method for controlling multiple active drifters in the presence of external forcing induced by the ocean. Our active drifters have one actuator: they can lower and raise their drogues in depth. By exploiting the vertically stratified nature of ocean currents, we show how classical multi-robot tasks (spreading out and aggregation) can be accomplished by the multi-drifter system. Tests with a realistic simulation based on an ocean model suggest that a practical implementation of active drifters which aggregate and disperse in the coastal ocean could be realized through our control method with relatively inexpensive components. Specifically, we are able to show that over a 90 day deployment a significant fraction of drifters can be made to aggregate in few clusters suitable for recovery.

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Although robotic coverage and collision avoidance are active areas of robotics research, the avoidance of collision situations between robots has often been neglected in the context of multi-robot coverage tasks. In fact, for robots of physical size, collisions are likely to happen during deployment and coverage in densely packed multi-robot configurations. For this reason, we aim to motivate by this paper the combined use of multi-robot coverage and reciprocal collision avoidance. We present a taxonomy of collision scenarios in multi-robot coverage problems. In particular, coverage tasks with built-in heterogeneity such as multiple antagonistic objectives or robot constraints are shown to benefit from the combination. Based on our taxonomy, we evaluate four representative robotic use cases in simulation by combining the specific methods of Voronoi coverage and reciprocal velocity obstacles.