Distributed Adaptive Coverage Control of Differential Drive Robotic Sensors
This work addresses coverage control for robotic sensors, which is incremental as it builds on existing methods by incorporating adaptive and consensus-based improvements.
The paper tackled the problem of deploying multiple mobile robots to autonomously cover a region with an unknown density function, by formulating it as an optimization problem and deriving adaptive control laws. They implemented these algorithms on real differential drive robots, comparing L2-distance based methods with locational optimization in experiments.
This paper is concerned with the deployment of multiple mobile robots in order to autonomously cover a region Q. The region to be covered is described using a density function which may not be apriori known. In this paper, we pose the coverage problem as an optimization problem over some space of functions on Q. In particular, we look at L 2 -distance based coverage algorithm and derive adaptive control laws for the same. We also propose a modified adaptive control law incorporating consensus for better parameter convergence. We implement the algorithms on real differential drive robots with both simulated density function as well as density function implemented using light sources. We also compare the L 2 -distance based method with the locational optimization method using experiments.