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Accelerating Multi-Agent Motion Planning in Complex Environments

Nadi, Elahe | 2025

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  1. Type of Document: M.Sc. Thesis
  2. Language: Farsi
  3. Document No: 58420 (05)
  4. University: Sharif University of Technology
  5. Department: Electrical Engineering
  6. Advisor(s): Babazadeh, Maryam
  7. Abstract:
  8. This study investigates the motion planning problem for multiple agents with nonlinear dynamics in environments with static obstacles. Designing safe, smooth, and optimal trajectories for a group of agents operating concurrently in a shared space is a critical challenge in robotics, multi-agent systems, and autonomous navigation. Traditional methods often rely on initial guesses close to the optimal solution or become computationally inefficient at large scales. To address these challenges, this thesis proposes a novel computational framework based on the Alternating Direction Method of Multipliers (ADMM) and the Convex Feasible Set (CFS) algorithm. The framework decomposes the overall problem into two independent subproblems: agent dynamics and collision avoidance with both obstacles and other agents, solved distributively among the agents. The ADMM enables parallel computation and coordination among agents, while the CFS algorithm converts non-convex constraints arising from obstacles and inter-agent interactions into solvable convex approximations. The proposed algorithm has been evaluated in environments with complex obstacles, and its performance in terms of convergence, trajectory quality, and computational speed has been compared with direct methods, such as the Interior Point Method (IPM). The results demonstrate that the proposed method not only achieves desirable accuracy and convergence but also effectively generates safe and coordinated trajectories for multiple agents while avoiding collisions with both obstacles and other agents
  9. Keywords:
  10. Alternating Direction Method of Multipliers (ADMM)Algorithm ; Collision Avoidance ; Convex Feasible Set (CFS) ; Multi-Agent Motion Planning ; Convex Obstacle Approximation

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