Abstract
We present a method that distributes a swarm of heterogeneous robots among a set of tasks that require specialized capabilities in order to be completed. We model the system of heterogeneous robots as a community of species, where each species (robot type) is defined by the traits (capabilities) that it owns. Our method is based on a continuous abstraction of the swarm at a macroscopic level as we model robots switching between tasks. We formulate an optimization problem that produces an optimal set of transition rates for each species, so that the desired trait distribution is reached as quickly as possible. Since our method is based on the derivation of an analytical gradient, it is very efficient with respect to state-of-the-art methods. Building on this result, we propose a real-time optimization method that enables an online adaptation of transition rates. Our approach is well-suited for real-time applications that rely on online redistribution of large-scale robotic systems.
Highlights
Technological advances in embedded systems, such as component miniaturization and improved efficiency of sensors and actuators, are enabling the deployment of very large-scale robot systems, i.e., swarms of robots
We present a method that distributes a swarm of heterogeneous robots among a set of tasks with the goal of satisfying a desired distribution of robot capabilities among those tasks
We propose a formulation for heterogeneous robot systems through species and traits, and show how this formulation is used to achieve an optimal distribution of robots as a function of a desired final trait configuration
Summary
Technological advances in embedded systems, such as component miniaturization and improved efficiency of sensors and actuators, are enabling the deployment of very large-scale robot systems, i.e., swarms of robots. Our objective is to distribute a swarm of heterogeneous robots as quickly and efficiently as possible among tasks that require specialized competences. This objective is part of a larger vision to develop control and coordination strategies for teams of heterogeneous robots with specific capabilities. We could consider the limited payload of aerial robots: If a given task requires rich sensory feedback, multiple heterogeneous aerial robots can complement each other by carrying distinct sensors, altogether more than a single one could carry on its own. Instances of information gathering lend themselves naturally to our problem formulation, with applications to surveillance, environmental monitoring, and situational awareness [3,4,5]
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