Multi-Robot Simultaneous Coverage and Mapping of Complex Scene

International audienceIn this demonstration, participants will explore a system for multi-robot observation of a complex scene involving the activity of a person. Mobile robots have to cooperate to find a position around the scene maximizing its coverage, i.e. allowing a complete view of the human skeleton. Simultaneously, they have to map the unknown environment around the scene. We developed a simulator presented in this paper that allows to generate an environment, a scene, and to simulate robots' observations and motion. During the demonstration, users will be able to test our simulator, including setting up a scenario and a decision algorithm, monitoring the movements, observations and maps of the robots, and visualizing the performance of the team

Coordination and Control for a Team of Mobile Robots in an Unknown Dynamic Environment

This research presents a dual-level control structure for controlling a mobile robot or a group of robots to navigate through a dynamic environment (such as an object is moving in the workspace of a robot). The higher-level controller operates in cooperation with robot’s state estimation and mapping algorithm, Extended Kalman Filter – Simultaneous Localization and Mapping (EKFSLAM), and the lower-level controller (PID) controls the motion of the robot when it, encounters an obstacle, i.e., it reorients the robot to a predefined rebound angle and move it straight to maneuver around the obstacle until the robot is out of the obstacle range. The higher-level controller jumps in as soon as the robot is out of the obstacle range and moves the robot to the goal. The obstacle avoidance technique involves a novel approach to calculate the rebound angle. Further, the research implements the aforementioned technique to a Leader-Follower formation. Simulation and Experimental results have verified the effectiveness of the proposed control law

Multi-Robot Simultaneous Coverage and Mapping of Complex Scene - Comparison of Different Strategies

International audienceThis paper addresses the problem of optimizing the observation of a human scene using several mobile robots. Mobile robots have to cooperate to find a position around the scene maximizing its coverage. The scene coverage is defined as the observation of the human pose skeleton. It is assumed that the robots can communicate but have no map of the environment. Thus the robots have to simultaneously cover and map the scene and the environment. We consider an incremental approach to master state-space complexity. Robots build an hybrid metric-topological map while evaluating the observation of the human pose skeleton. To this end we propose and evaluate different online optimization strategies exploiting local versus global information. We discuss the difference of the performance and cost. Experiments are performed both in simulation and with real robots