An omnidirectional mobile manipulator (OMM) provides extended capabilities to a robotic system since it enlarges its workspace at the expense of an omnidirectional mobile base, which is nowadays a reality. When various OMM transport an object, the resultant constrained OMM dynamics is quite complex that demands effective control schemes without neglecting dominant nonlinear effects nor interaction forces. In this paper, it is proposed a decentralized cooperative control for a set of k OMMs (k-OMM) subject to homogeneous holonomic constraints imposed by the object being manipulated. A chatterless integral sliding mode force-position control guarantees exponential tracking whose implementation does not require any knowledge of dynamics. This controller is decentralized in a sense that neither dynamics nor interaction forces of the i-OMM are required for the j-controller, and it is cooperative by correcting collectively the velocity deviation due to the pushing of the other j-OMM. Each omnidirectional base provides object mobility along passive smooth velocity fields to avoid obstacles on the floor, which is synthesized as the secondary task when resolving redundancy, and the force control stabilizes internal forces to ensure object grasp, which are declared together with end-effector position, as the primary task. Simulations are discussed for two 8-DoF Kuka youBot OMM system navigating in a cluttered environment.

No abstract

No abstract

This paper presents a vision-based navigation system for an autonomous underwater vehicle in semistructured environments with poor visibility. In terrestrial and aerial applications, the use of visual systems mounted in robotic platforms as a control sensor feedback is commonplace. However, robotic vision-based tasks for underwater applications are still not widely considered as the images captured in this type of environments tend to be blurred and/or color depleted. To tackle this problem, we have adapted thelαβcolor space to identify features of interest in underwater images even in extreme visibility conditions. To guarantee the stability of the vehicle at all times, a model-free robust control is used. We have validated the performance of our visual navigation system in real environments showing the feasibility of our approach.

No abstract