A Novel Practical Technique to Integrate Inequality Control Objectives and Task Transitions in Priority Based Control

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

Handling robot constraints within a Set-Based Multi-Task Priority Inverse Kinematics Framework

Set-Based Multi-Task Priority is a recent framework to handle inverse kinematics for redundant structures. Both equality tasks, i.e., control objectives to be driven to a desired value, and set-bases tasks, i.e., control objectives to be satisfied with a set/range of values can be addressed in a rigorous manner within a priority framework. In addition, optimization tasks, driven by the gradient of a proper function, may be considered as well, usually as lower priority tasks. In this paper the proper design of the tasks, their priority and the use of a Set-Based Multi-Task Priority framework is proposed in order to handle several constraints simultaneously in real-time. It is shown that safety related tasks such as, e.g., joint limits or kinematic singularity, may be properly handled by consider them both at an higher priority as set-based task and at a lower within a proper optimization functional. Experimental results on a 7DOF Jaco$^2

Safety-related Tasks within the Set-Based Task-Priority Inverse Kinematics Framework

In this paper we present a framework that allows the motion control of a robotic arm automatically handling different kinds of safety-related tasks. The developed controller is based on a Task-Priority Inverse Kinematics algorithm that allows the manipulator's motion while respecting constraints defined either in the joint or in the operational space in the form of equality-based or set-based tasks. This gives the possibility to define, among the others, tasks as joint-limits, obstacle avoidance or limiting the workspace in the operational space. Additionally, an algorithm for the real-time computation of the minimum distance between the manipulator and other objects in the environment using depth measurements has been implemented, effectively allowing obstacle avoidance tasks. Experiments with a Jaco$^2$ manipulator, operating in an environment where an RGB-D sensor is used for the obstacles detection, show the effectiveness of the developed system

Flexible human-robot cooperation models for assisted shop-floor tasks

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.Comment: Submitted to Mechatronics (Elsevier

Task priority control of underwater intervention systems: Theory and applications

This paper presents a unifying task priority control architecture for underwater vehicle manipulator systems. The proposed control framework can be applied to different operative scenarios such as waypoint navigation, assisted teleoperation, interaction, landing and grasping. This work extends the results of the TRIDENT and MARIS projects, which were limited to the execution of grasping actions, to other applications taken from the DexROV and ROBUST projects. In particular, simulation results show how the control framework can be used, for example, for pipeline inspection scenarios and deep sea mining exploration

On Autonomous Robotic Cooperation Capabilities within Factory and Logistic Scenarios

The paper presents the development of a unified functional, algorithmic and Software (Sw) architecture, which can be adopted as a standard for controlling, at action level only, any robotic structure within a given wide class of them; even of reconfigurable type within the class. Such control architecture is therefore deemed very suitable for operating within factory and/or logistic, possibly reconfigurable, scenarios. Moreover, for the few cases of cooperative activities to be established between agents not allowed to be cable connected, an effective coordination policy, based on the exchange of a reduced information set, only regarding the cooperation goals, is developed; and relevant simulative and experimental trials are briefly outlined. Moreover, the advantage of having, in whatever operative condition, the possibility of commanding the involved structures only in terms of the ultimate goals of each action, also seems to be the right basis for having non-negligible improvements within their integration with automated action planning, and even learning, techniques

Robotized underwater interventions

Working in underwater environments poses many challenges for robotic systems. One of them is the low bandwidth and high latency of underwater acoustic communications, which limits the possibility of interaction with submerged robots. One solution is to have a tether cable to enable high speed and low latency communications, but that requires a support vessel and increases costs. For that reason, autonomous underwater robots are a very interesting solution. Several research projects have demonstrated autonomy capabilities of Underwater Vehicle Manipulator Systems (UVMS) in performing basic manipulation tasks, and, moving a step further, this chapter will present a unifying architecture for the control of an UVMS, comprehensive of all the control objectives that an UVMS should take into account, their different priorities and the typical mission phases that an UVMS has to tackle. The proposed strategy is supported both by a complete simulated execution of a test-case mission and experimental results

Task-Priority Control of Redundant Robotic Systems using Control Lyapunov and Control Barrier Function based Quadratic Programs

This paper presents a novel task-priority control framework for redundant robotic systems based on a hierarchy of control Lyapunov function (CLF) and control barrier function (CBF) based quadratic programs (QPs). The proposed method guarantees strict priority among different groups of tasks such as safety-related, operational and optimization tasks. Moreover, a soft priority measure in the form of penalty parameters can be employed to prioritize tasks at the same priority level. As opposed to kinematic control schemes, the proposed framework is a holistic approach to control of redundant robotic systems, which solves the redundancy resolution, dynamic control and control allocation problems simultaneously. Numerical simulations of a hyper-redundant articulated intervention autonomous underwater vehicle (AIAUV) is presented to validate the proposed framework.Comment: 21st IFAC World Congres

A Unified Task Priority Control Framework Design for Autonomous Underwater Vehicles

In this thesis, we investigate the problem of bringing various behaviours of Autonomous Underwater Vehicles under a common control framework. Thereby, we propose a unified guidance and control framework for AUVs based on the task priority control approach. This incorporate various behaviors such as path following, terrain following, obstacle avoidance, as well as homing and docking to stationary and moving docking stations. The integration of homing and docking maneuvers into the task priority framework is thus a novel contribution of this thesis. This integration allows, for example, to execute homing maneuvers close to uneven seafloor or obstacles, ensuring the safety of the AUV by giving the highest priority to the safety tasks. Furthermore, the proposed approach tackles a wide range of scenarios without ad hoc solutions. Indeed, the proposed approach is well suited for both the emerging trend of resident AUVs, which stay underwater for a long period inside garage stations, exiting to perform inspection and maintenance missions and homing back to them, and for AUVs that are required to dock to moving stations such as surface vehicles, or towed docking stations. The proposed techniques are further studied in a simulation setting, taking into account the rich number of aforementioned scenarios