Cost Adaptation for Robust Decentralized Swarm Behaviour
This work addresses the problem of robust decentralized swarm behavior for robotics and multi-agent systems, offering an incremental improvement through adaptive optimization.
The paper tackles the challenge of efficiently combining multiple goals, costs, and constraints in decentralized receding horizon control for swarm coordination by introducing a meta-learning cost adaptation method. It demonstrates that this approach enables more efficient and safer task completion under varying conditions and larger swarm sizes in a simulated coordinated exploration task.
Decentralized receding horizon control (D-RHC) provides a mechanism for coordination in multi-agent settings without a centralized command center. However, combining a set of different goals, costs, and constraints to form an efficient optimization objective for D-RHC can be difficult. To allay this problem, we use a meta-learning process -- cost adaptation -- which generates the optimization objective for D-RHC to solve based on a set of human-generated priors (cost and constraint functions) and an auxiliary heuristic. We use this adaptive D-RHC method for control of mesh-networked swarm agents. This formulation allows a wide range of tasks to be encoded and can account for network delays, heterogeneous capabilities, and increasingly large swarms through the adaptation mechanism. We leverage the Unity3D game engine to build a simulator capable of introducing artificial networking failures and delays in the swarm. Using the simulator we validate our method on an example coordinated exploration task. We demonstrate that cost adaptation allows for more efficient and safer task completion under varying environment conditions and increasingly large swarm sizes. We release our simulator and code to the community for future work.