IEEE Transactions on Systems, Man and Cybernetics
Task-priority based redundancy control of robot manipulators
International Journal of Robotics Research
Matrix computations (3rd ed.)
Modeling, Identification and Control of Robots
Modeling, Identification and Control of Robots
Cooperative Mobile Robotics: Antecedents and Directions
Autonomous Robots
Robotics: Modelling, Planning and Control
Robotics: Modelling, Planning and Control
Sliding mode speed auto-regulation technique for robotic tracking
Robotics and Autonomous Systems
A supervisory loop approach to fulfill workspace constraints in redundant robots
Robotics and Autonomous Systems
Multirobot coordination in pick-and-place tasks on a moving conveyor
Robotics and Computer-Integrated Manufacturing
A path conditioning method with trap avoidance
Robotics and Autonomous Systems
Task-oriented motion planning for multi-arm robotic systems
Robotics and Computer-Integrated Manufacturing
Integrated sliding-mode algorithms in robot tracking applications
Robotics and Computer-Integrated Manufacturing
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In this work, an approach based on task-priority redundancy resolution and sliding mode ideas is proposed for robot coordination. In particular, equality and inequality constraints representing the coordination of the multi-robot system are considered as mandatory (for instance, rigid-body manipulation constraints to distance between the end-effectors of several robot arms, or other inequality constraints guaranteeing safe operation of a robotic swarm or confining the robot's workspace to avoid collision and joint limits). Besides the mandatory constraints, other constraints with lower priority are considered for the tracking of the workspace reference and to achieve secondary goals. Thus, lower-priority constraints are satisfied only in the null space of the higher-priority ones. The fulfillment of the constraints is achieved using geometric invariance and sliding mode control theory. The validity and effectiveness of the proposed approach are substantiated by 2D and 3D simulation results using two 3R planar robots and two 6R PUMA-762 robots, respectively.