Abstract
Swarm robot systems, which consist of many cooperating mobile robots, have attracted attention for their environmental adaptability and fault tolerance advantages. One of the most important tasks for such systems is coverage control, in which robots autonomously deploy to approximate a given spatial distribution. In this study, we formulate a coverage control paradigm using the concept of optimal transport and propose a novel control technique, which we have termed the optimal transport-based coverage control (OTCC) method. The proposed OTCC, derived via the gradient flow of the cost function in the Kantorovich dual problem, is shown to covers a widely used existing control method as a special case. We also perform a Lyapunov stability analysis of the controlled system, and provide numerical calculations to show that the OTCC reproduces target distributions with better performance than the existing control method.
Highlights
S WARM robot systems, in which many mobile robots work cooperatively to perform given tasks, are expected to have strong environmental adaptability and high fault tolerance in comparison with single-robot systems [1]–[3]
Remark 1: We show that the Voronoi tessellation-based coverage control (VTCC) method [11] is regarded as a special case of the proposed optimal transport-based coverage control (OTCC)
We propose the optimal transport-based coverage control (OTCC) method as an improvement to the Voronoi tessellation-based coverage control (VTCC) method
Summary
S WARM robot systems, in which many mobile robots work cooperatively to perform given tasks, are expected to have strong environmental adaptability and high fault tolerance in comparison with single-robot systems [1]–[3]. By regarding a robot swarm as an abstract group of points in Euclidean space, the coverage control is interpreted as the problem of transporting a given discrete distribution to approximate a target continuous distribution. This is commonly referred to as the optimal transport problem, and its mathematical properties and numerical solutions have been widely investigated [13]–[15]. Our control method differs from existing methods in that it considers gradient flows for the cost function of the optimal transport problem, which allows us to compare the structure and performance of the proposed OTCC with that of the VTCC.
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