Decentralized model predictive control for autonomous robot swarms with restricted communication skills in unknown environments
Alexander Puzicha, Peter Buchholz · Procedia Computer Science · 2021
The paper presents a new approach and a related real-time parallel simulation tool for modeling and analyzing a swarm of more than 60 distributed autonomous mobile robots communicating over an unreliable and capacity restricted wireless communication network. It includes a physical simulation of static obstacles, dynamic obstacles with scriptable movement, soil condition, active jammers, static and dynamic link obstacles with configurable damping as well as thermal noise. The simulated ground based mobile robots use CPBP [12] as probabilistic model predictive closed loop controller in combination with gradient free cost functions to evaluate complex unsteady goodness aggregations. The goal of this approach is the development of connection aware swarm behavior for complex missions such as terrain exploration, formations, convoy escorting or creation of a mobile ad hoc network in dynamic disaster areas under realistic environmental conditions. The missions can be combined in any manner and the target extraction of the high-level commands for each agent is done implicitly. To validate the developed behavior, the independent software control kernel is able to be used on real robots as well.