A distributed, plug-n-play algorithm for multi-robot applications with a priori non-computable objective functions
This addresses the challenge of deploying multi-robot systems in uncertain environments for applications like search and rescue or surveillance, though it appears incremental as it builds upon existing cognitive-based adaptive optimization methods.
The paper tackles the problem of multi-robot applications where objective functions are not computable a priori due to unknown factors, and introduces a distributed algorithm that transforms the problem into subcost functions for each robot, achieving convergence similar to block coordinate descent methods. It is evaluated in three heterogeneous simulation setups, showing effectiveness against general-purpose and problem-specific algorithms.
This paper presents a distributed algorithm applicable to a wide range of practical multi-robot applications. In such multi-robot applications, the user-defined objectives of the mission can be cast as a general optimization problem, without explicit guidelines of the subtasks per different robot. Owing to the unknown environment, unknown robot dynamics, sensor nonlinearities, etc., the analytic form of the optimization cost function is not available a priori. Therefore, standard gradient-descent-like algorithms are not applicable to these problems. To tackle this, we introduce a new algorithm that carefully designs each robot's subcost function, the optimization of which can accomplish the overall team objective. Upon this transformation, we propose a distributed methodology based on the cognitive-based adaptive optimization (CAO) algorithm, that is able to approximate the evolution of each robot's cost function and to adequately optimize its decision variables (robot actions). The latter can be achieved by online learning only the problem-specific characteristics that affect the accomplishment of mission objectives. The overall, low-complexity algorithm can straightforwardly incorporate any kind of operational constraint, is fault-tolerant, and can appropriately tackle time-varying cost functions. A cornerstone of this approach is that it shares the same convergence characteristics as those of block coordinate descent algorithms. The proposed algorithm is evaluated in three heterogeneous simulation set-ups under multiple scenarios, against both general-purpose and problem-specific algorithms. Source code is available at https://github.com/athakapo/A-distributed-plug-n-play-algorithm-for-multi-robot-applications.