A distributed, plug-n-play algorithm formulti-robot applications with a priorinon-computable objective functions
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, withoutexplicit 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, standardgradient-descent-like algorithms are not applicable to these problems. To tackle this, we introduce a new algorithm thatcarefully 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 itsdecision variables (robot actions). The latter can be achieved by online learning only the problem-specific characteristicsthat affect the accomplishment of mission objectives. The overall, low-complexity algorithm can straightforwardlyincorporate 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 coordinatedescent algorithms. The proposed algorithm is evaluated in three heterogeneous simulation set-ups under multiplescenarios, against both general-purpose and problem-specific algorithms.