Optimization Based Collision Avoidance for Multi-Agent DynamicalSystems in Goal Reaching Task
This addresses safe navigation for multi-agent systems like drones or robots, but it is incremental as it builds on existing optimization-based collision avoidance methods.
The paper tackles multi-agent point-to-point navigation with collision avoidance by developing a distributed MPC approach using ADMM, achieving computational efficiency and reliable solutions.
This work presents a distributed MPC-based approach to solving the problem of multi-agent point-to-point transition with optimization-based collision avoidance. The problem is formulated, motivated by the work on collision avoidance for multi-agent systems and dynamic obstacles. With modifications to the formulation, the problem is converted into a distributed problem with a separable objective and coupled constraints. The problem is divided into local sub-problems and solved using Alternating Directions Method of Multipliers(ADMM) applied on an augmented local lagrangian objective.This work aims to understand the multi-agent point-to-point transition problem as an extension of optimization-based collision avoidance and analyze the aspects of computational times, reliability, and optimality of the solution obtained.