With a special choice for the system state equations, the use of the simplest quadratic form as candidate Lyapunov function directly leads to the definition of very simple, smooth and effective closed loop control laws for unicycle-like vehicles, suitable to be used for steering, path following, and navigation. The authors provide simulation examples to show the effectiveness and, in a sense, the “natural behavior” of the obtained closed loop motions (when compared with everyday driving experience

No abstract

No abstract

This paper presents the control framework that has been proposed and successfully employed within the TRIDENT EU FP7 project, the aim of which is to develop a multipurpose Intervention Autonomous Underwater Vehicle (I-AUV) exhibiting smart manipulation capabilities, for interventions within unstructured underwater environments. In particular, the work focuses on the exploitation of the highly redundant system for achieving a dexterous object grasping, while also satisfying a set of conditions of scalar inequality type to be achieved ultimately. These represent safety and/or operational-enabling conditions for the overall system itself, such as, for instance, respecting joint limits and keeping the object grossly centered in the camera system. Thus the design of a control architecture exhibiting such a property first required an extension of the classical task priority framework, to be performed in such a way as to also account, in a uniform manner, for inequality conditions to be achieved ultimately. Then, following a description on how such an extension has been made, both simulations and experimental trials are successively presented to show how the developed TRIDENT I-AUV system is able to properly exploit all the redundant degrees of freedom for achieving all the established objectives. C 2013 Wiley Periodicals, Inc.

The task priority based control is a formalism which allows to create complex control laws with nice invariance properties, i.e. lower priority tasks do not affect the execution of higher priority ones. However, the classical task priority framework (Siciliano and Slotine) lacked the ability of enabling and disabling tasks without causing discontinuities. Furthermore, tasks corresponding to inequality control objectives could not be efficiently represented within that framework. In this paper we present a novel technique to integrate both the activation and deactivation of tasks and the inequality control objectives in the priority based control. The technique, called iCAT (inequality control objectives, activations and transitions) task priority framework, exploits novel regularization methods to activate and deactivate any row of a given task in a prioritized hierarchy without incurring in practical discontinuities, while maintaining as much as possible the invariance properties of the other active tasks. Finally, as opposed to other techniques, the proposed approach has a linear cost in the number of tasks. Simulations, experimental results and a time analysis are presented to support the proposed technique

No abstract

The Industry 4.0 paradigm emphasizes the crucial benefits that collaborative robots, i.e., robots able to work alongside and together with humans, could bring to the whole production process. In this context, an enabling technology yet unreached is the design of flexible robots able to deal at all levels with humans' intrinsic variability, which is not only a necessary element for a comfortable working experience for the person, but also a precious capability for efficiently dealing with unexpected events. In this paper, a sensing, representation, planning and control architecture for flexible human-robot cooperation, referred to as FlexHRC, is proposed. FlexHRC relies on wearable sensors for human action recognition, AND/OR graphs for the representation of and reasoning upon cooperation models, and a Task Priority framework to decouple action planning from robot motion planning and control.

No abstract