Abstract
This paper addresses the problem of cooperative object transportation in a constrained workspace involving static obstacles, with the coordination relying on implicit communication established via the commonly grasped object. In particular, we consider a decentralized leader-follower architecture for multiple mobile manipulators, where the leading robot, which has exclusive knowledge of both the object's desired configuration and the position of the obstacles in the workspace, tries to navigate the overall formation to the desired configuration while at the same time it avoids collisions with the obstacles. On the other hand, the followers estimate the object's desired trajectory profile via novel prescribed performance estimation laws that drive the estimation errors to an arbitrarily small predefined residual set. Moreover, a navigation function-based scheme is innovatively combined with adaptive control to deal with parametric uncertainty. Hence, the current state of the art in robust motion planning and collision avoidance is extended by studying second order non-linear dynamics with parametric uncertainty. Furthermore, the feedback relies exclusively on each robot's force/torque, position as well as velocity measurements and no explicit information is exchanged online among the robots, thus reducing the required communication bandwidth and increasing robustness. Finally, two simulation studies clarify the proposed methodology and verify its efficiency.
Highlights
The recent development of robotic technologies has introduced robots in various fields of industry, agriculture, security, etc
This paper extends our recent results in Tsiamis et al (2015a,b) by considering multiple mobile manipulators in the problem of decentralized cooperative object manipulation in a constrained workspace with static obstacles
This paper presented a leader-follower scheme for cooperative object transportation under implicit communication, avoiding completely tedious explicit on-line inter-robot communication
Summary
The recent development of robotic technologies has introduced robots in various fields of industry, agriculture, security, etc. Complex applications require multiple robots to execute a task in coordination efficiently, e.g., handling a heavy object (see Figure 1) or assembling a complex product. A great research effort has been made during the last three decades on the coordinated control of multiple robots. Most of the seminal works in this direction proposed centralized control algorithms, based on global information with respect to a common coordinate system. The recent advances in mobile manipulators, which allow free motion in a real world environment, have substantially increased the number of robots that can be involved in a coordinated task. Collaborative Multi-Robot Transportation centralized approaches render unrealistic, owing to the computational burden and the fact that various geometric errors that appear inevitably among the robots cannot be handled accurately based on a common coordinate system. To overcome the aforementioned issues, decentralized control of multiple robots emerged, in which each robot is controlled by its own controller based on its own local coordinate system
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