Abstract
The task priority planning problem is addressed in the task supervisor of null-space behavioral (NSB) control for multi-agent systems. Traditional methods rely on pre-defined logic-based or fuzzy rules to adjust task priority. In this work, a novel task supervisor is proposed using model predictive control (MPC) techniques. At each sampling instant, the task priority planning problem is formulated as a switching mode optimal control problem (OCP), which can be solved by efficient mixed-integer optimal control algorithms. The optimal task priority order is obtained based on current and predictive information of agents, without the need for a pre-defined rule. By explicitly introducing slack variables into constraints, the proposed MPC method is flexible to cope with dynamic obstacles in unknown environments. Simulations with static and dynamic obstacles show that the proposed method can provide significantly better control performance than the traditional logic-based method using less priority switchings.
Highlights
Because of their agility and versatility, multi-agent systems are widely applied in both military [1], [2] and civilian areas [3]–[5]
Autonomous mobile robots are typical multi-agent systems which usually work in an unstructured environment and need to accomplish multiple tasks such as moving through predefined via-points, avoiding static or dynamic obstacles, forming a formation and flocking
These tasks may conflict, e.g. an autonomous mobile robot cannot move through a predefined via-point while at the same time avoid an obstacle near the via-point
Summary
Because of their agility and versatility, multi-agent systems are widely applied in both military [1], [2] and civilian areas [3]–[5]. Autonomous mobile robots are typical multi-agent systems which usually work in an unstructured environment and need to accomplish multiple tasks such as moving through predefined via-points, avoiding static or dynamic obstacles, forming a formation and flocking. The priority of tasks, which is determined by the task supervisor, is usually set in advance and is fixed during the entire task execution process [12], [13] This may degrade the control performance of the NSB method and limit its application in dynamic and unknown environments. It should be noted that advanced priority planning or behavior switching algorithms have been studied in the field of robots and multi-agent systems but not in the NSB control framework. A novel task supervisor to dynamically plan task priority in the NSB control is proposed using MPC techniques.
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