Distributed Stochastic Search for Multi-Agent Model Predictive Control
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arXiv
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| Format: | Preprint |
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2025
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| _version_ | 1866909860861313024 |
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| author | Yoon, Taehyun Saravanos, Augustinos D. Theodorou, Evangelos A. |
| author_facet | Yoon, Taehyun Saravanos, Augustinos D. Theodorou, Evangelos A. |
| contents | Many real-world multi-agent systems exhibit nonlinear dynamics and complex inter-agent interactions. As these systems increase in scale, the main challenges arise from achieving scalability and handling nonconvexity. To address these challenges, this paper presents a distributed sampling-based optimization framework for multi-agent model predictive control (MPC). We first introduce stochastic search, a generalized sampling-based optimization method, as an effective approach to solving nonconvex MPC problems because of its exploration capabilities. Nevertheless, optimizing the multi-agent systems in a centralized fashion is not scalable as the computational complexity grows intractably as the number of agents increases. To achieve scalability, we formulate a distributed MPC problem and employ the alternating direction method of multipliers (ADMM) to leverage the distributed approach. In multi-robot navigation simulations, the proposed method shows a remarkable capability to navigate through nonconvex environments, outperforming a distributed optimization baseline using the interior point optimizer (IPOPT). In a 64-agent multi-car formation task with a challenging configuration, our method achieves 100% task completion with zero collisions, whereas distributed IPOPT fails to find a feasible solution. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2510_18211 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Distributed Stochastic Search for Multi-Agent Model Predictive Control Yoon, Taehyun Saravanos, Augustinos D. Theodorou, Evangelos A. Optimization and Control Many real-world multi-agent systems exhibit nonlinear dynamics and complex inter-agent interactions. As these systems increase in scale, the main challenges arise from achieving scalability and handling nonconvexity. To address these challenges, this paper presents a distributed sampling-based optimization framework for multi-agent model predictive control (MPC). We first introduce stochastic search, a generalized sampling-based optimization method, as an effective approach to solving nonconvex MPC problems because of its exploration capabilities. Nevertheless, optimizing the multi-agent systems in a centralized fashion is not scalable as the computational complexity grows intractably as the number of agents increases. To achieve scalability, we formulate a distributed MPC problem and employ the alternating direction method of multipliers (ADMM) to leverage the distributed approach. In multi-robot navigation simulations, the proposed method shows a remarkable capability to navigate through nonconvex environments, outperforming a distributed optimization baseline using the interior point optimizer (IPOPT). In a 64-agent multi-car formation task with a challenging configuration, our method achieves 100% task completion with zero collisions, whereas distributed IPOPT fails to find a feasible solution. |
| title | Distributed Stochastic Search for Multi-Agent Model Predictive Control |
| topic | Optimization and Control |
| url | https://arxiv.org/abs/2510.18211 |