Cooperative receding horizon control for multi-agent rendezvous problems in uncertain environments

2010 ◽  
Author(s):  
Chen Yao ◽  
Xu Chu Ding ◽  
Christos G. Cassandras
Keyword(s):  
Receding Horizon Control ◽  
Receding Horizon ◽  
Uncertain Environments ◽  
Multi Agent

scholarly journals A Submodular Receding Horizon Control Strategy to Distributed Persistent Monitoring

2022 ◽  
Vol 2 ◽  
Author(s):  
Xiaohu Zhao ◽  
Yuanyuan Zou ◽  
Shaoyuan Li
Keyword(s):  
Estimation Error ◽  
Receding Horizon Control ◽  
Receding Horizon ◽  
Control Approach ◽  
Global Performance ◽  
Multi Agent ◽  
Target States ◽  
Definition Of ◽  
Monitoring Performance ◽  
Persistent Monitoring

This paper investigates the multi-agent persistent monitoring problem via a novel distributed submodular receding horizon control approach. In order to approximate global monitoring performance, with the definition of sub-modularity, the original persistent monitoring objective is divided into several local objectives in a receding horizon framework, and the optimal trajectories of each agent are obtained by taking into account the neighborhood information. Specifically, the optimization horizon of each local objective is derived from the local target states and the information received from their neighboring agents. Based on the sub-modularity of each local objective, the distributed greedy algorithm is proposed. As a result, each agent coordinates with neighboring agents asynchronously and optimizes its trajectory independently, which reduces the computational complexity while achieving the global performance as much as possible. The conditions are established to ensure the estimation error converges to a bounded global performance. Finally, simulation results show the effectiveness of the proposed method.

On Receding Horizon Chance Constraint Motion Planning for Uncertain Multi-Agent Systems

2019 ◽  
Author(s):  
Yash Bagla ◽  
Vaibhav Srivastava
Keyword(s):  
Motion Planning ◽  
Autonomous Systems ◽  
Random Trees ◽  
Chance Constraint ◽  
Multi Agent Systems ◽  
Receding Horizon ◽  
Uncertain Environments ◽  
Uncertainty Model ◽  
Planning Algorithm ◽  
Multi Agent

Abstract We propose and study a motion planning algorithm for multi-agent autonomous systems to navigate through uncertain and dynamic environments. We use a receding horizon chance constraint framework that allows for tuning the trade-off between the risk of collision and the infeasibility of paths. We consider sampling-based incremental planning algorithms and extend them to the case of multiple agents and dynamic and uncertain environments. The receding horizon control framework is used to incorporate sensor measurements at a fixed interval of time to reduce uncertainty about agents’ state and environment. Our presentation focuses on rapidly-exploring random trees (RRTs) and the assumption of Gaussian noise in the uncertainty model. Our algorithm is illustrated using several examples.

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