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Abstract This paper is concerned with the multi-objective region reaching control for a swarm of robots which are formulated by Lagrangian dynamics. Two distributed multi-objective region reaching control protocols are proposed for the networked robotic systems under directed acyclic topology, and a unifying methodology is presented to perform the convergence analysis for the robotic systems with static and moving target regions. The control strategy is developed by using the potential energy function approach, and the specified shapes of the various desired regions are constructed by selecting appropriate objective functions. In this control strategy, a network of a large number of robots evolves into multiple groups, and the robots in each group only require communicating with their neighbors. Thus, the proposed control strategy is effective for multi-objective region reaching control for a swarm of robots in practical applications. Finally, simulation examples are given to show the validity of the theoretical results., [ABSTRACT FROM AUTHOR]

Abstract This study is concerned with the region reaching formation control problem with collision and obstacle avoidance for multi-robot systems in the presence of model uncertainties and external disturbances. A novel neural network based robust control scheme combining with the adaptive compensator and the adaptive control gain is proposed to achieve the region reaching formation control with collision and obstacle avoidance. It is shown that under the proposed control method, all the robots can always reach into the objective region, maintain their formation, and guarantee collision and obstacle avoidance. Illustrative examples are presented to show the effectiveness of the proposed control scheme. [ABSTRACT FROM AUTHOR]

The main objective of this paper is to advance the concept of region reaching control for a single robot to the study of region reaching consistency for uncertain fully‐actuated multi‐robot systems formulated by Lagrange dynamics. By introducing a directed network communication topology and utilizing the pinning‐like control methods, a distributed region reaching consensus control strategy is proposed. The desired region shape can be specified by selecting an appropriate potential objective function. A feature of the developed region reaching consensus algorithm is fully distributed, which is also an important generalization of pinning‐like set point tracking scheme based on the typical proportion‐integration‐differentiation control scheme. More importantly, the scope of consistency in this paper can be explicitly defined, which is different from traditional consistency control algorithms. Subsequently, numerical examples are presented to illustrate the performance of the designed controllers. [ABSTRACT FROM AUTHOR]

Controlling a robot manipulator system with both uncertain kinematics and dynamics is a challenging problem since the traditional control schemes that relying on the robot system models are no longer applicable. Developing a neural network‐based adaptive tracking control for such uncertain robot manipulator systems with region constraints is especially changing. In this paper, region tracking controllers are designed for a robot manipulator systems with uncertain kinematics and dynamics. The developed region tracking controllers ensures that the uncertain robot manipulator can track a moving region other than the traditional fixed point, which has better redundancy characteristics. The results are obtained through the development of the sliding‐mode and a novel proportion‐integration‐differentiation (PID)‐like method to address the region tracking control problem. Numerical simulations are presented to verify the proposed controller's performance. [ABSTRACT FROM AUTHOR]