Anisotropic Diffusion-Driven Ergodic Coverage in Multi-Robot Systems
For multi-robot systems requiring ergodic coverage, this work provides a more flexible method that directs agent movement based on the gradient of Perona-Malik diffusion, though it is an incremental extension of existing ergodic search frameworks.
This paper introduces an anisotropic diffusion-driven ergodic coverage method for multi-robot systems, which generalizes previous isotropic approaches. Simulations show improved directional control over coverage compared to the heat equation-based method.
We consider the problem of combining potential field and ergodic search on multi-robot systems. Traditional ergodic search algorithms use metrics for ergodicity that account for the desired distribution at different scales. Recently, a heat equation-driven ergodic approach was proposed, which adds flexibility to the smoothing of the ergodic metric. However, such an approach, as it is an isotropic diffusion, propagates the error uniformly in all directions, regardless of changes in the desired distribution. We introduce a general class of anisotropic diffusion formulation of the ergodicity problem, which generates a potential field for the ergodic search. We demonstrate that this approach generalizes previous results, which consider radial basis functions and the solution of the heat equation to represent the difference between the goal density distribution and the covered trajectories. In our solution, the agent movement is directed using the gradient of the solution of the Perona-Malik diffusion, and our formulation includes the heat equation as a special case. We demonstrate the methodology with a series of simulations in different scenarios.