Adapting to sensing and actuation variations in multi-robot coverage

Abstract

This article considers the problem of multi-robot coverage control, where a group of robots has to spread out over an environment to provide coverage. We propose a new approach for a group of robots carrying out this collaborative task that will adapt online to performance variations among the robots. Two types of performance variations are considered: variations in sensing performance (e.g. differences in sensor types, calibration, or noise), and variations in actuation (e.g. differences in terrain, vehicle types, or lossy motors). The robots have no prior knowledge of the relative strengths of their performance compared to the others in the team. We present an algorithm that learns the relative performance variations among the robots online, in a distributed fashion, and automatically compensates by assigning the weak robots a smaller portion of the environment and the strong robots a larger portion. Using a Lyapunov-type proof, we show that the robots converge to a locally optimal coverage configuration. The algorithm is also demonstrated in both MATLAB simulations and experiments with Pololu m3pi ground robots.

Social behavior for autonomous vehicles

“…An AV must also recognize these social cues of selfishness or cooperation, and failure to do so impacts the overall flow of the traffic network and even the safety of the traffic participants. AVs rely on explicit communication, state machines, or geometric reasoning about the driving interactions (4–8), neglecting social cues and driver personality. These approaches cannot handle complex interactions, resulting in conservative behavior and limiting autonomy solutions to simple road interactions.…”

Theoretical and Experimental Collaborative Area Coverage Schemes Using Mobile Agents

“…Proof. We start by evaluating the time derivative of the objective using the agent dynamics (1) we get ∂H ∂t ¼…”

“…It is usually categorized as either blanket or sweep coverage. In blanket or static coverage the goal of the robot team is a final static configuration at which an objective function is maximized [1][2][3]. In sweep or dynamic coverage on the other hand the mobile agents are tasked with maximizing a constantly changing objective, resulting in potentially continuous motion of the agents [4][5][6].…”

Simultaneous Deployment and Tracking Multi-Robot Strategies with Connectivity Maintenance

“…The works discussing limited sensing and communication capabilities are more realistic and thus have a higher interest. Circular sensing footprints are included in [ 21 ], while [ 22 ] focuses on how to learn and adjust the regions associated with each robot depending on their sensing and actuating performances. Lloyd methods also allow the inclusion of obstacle-avoidance by the definition of safety regions that ensure there will be no collisions between the agents [ 23 ].…”