1,720,980 research outputs found
A global approach to manipulability optimisation for a dual-arm manipulator
No full textIn this paper, we present a new approach to manipulability maximisation for a dual-arm manipulator, which takes into account the manipulability of the overall task. This method tries to overcome the drawbacks given by traditional approaches, which optimise the manipulability of the local configuration of the manipulator, but do not take into account the rest of the task, even though it is known a priori. In this way, it is possible to improve the average manipulability index over the task. The method is applied to a dual-arm system, wherein the task is expressed in terms of relative poses between the end-effectors. For this reason, the kinematic of the system is solved by means of the relative Jacobian
LINarm: a low-cost variable stiffness device for upper-limb rehabilitation
No full textThis paper presents LINarm, a device for at-home robotic upper-limb neuro rehabilitation. Exploiting peculiar aspects of variable-stiffness actuators, it features functionalities widely addressed by devices specificall designed for assisted rehabilitation as controlled motion, force feedback and safety, together with the low-cost requirement for a wide spread installation at patients’ home
Model-Based Reinforcement Learning Variable Impedance Control for Human-Robot Collaboration
No full textIndustry 4.0 is taking human-robot collaboration at the center of the production environment. Collaborative robots enhance productivity and flexibility while reducing human’s fatigue and the risk of injuries, exploiting advanced control methodologies. However, there is a lack of real-time model-based controllers accounting for the complex human-robot interaction dynamics. With this aim, this paper proposes a Model-Based Reinforcement Learning (MBRL) variable impedance controller to assist human operators in collaborative tasks. More in details, an ensemble of Artificial Neural Networks (ANNs) is used to learn a human-robot interaction dynamic model, capturing uncertainties. Such a learned model is kept updated during collaborative tasks execution. In addition, the learned model is used by a Model Predictive Controller (MPC) with Cross-Entropy Method (CEM). The aim of the MPC+CEM is to online optimize the stiffness and damping impedance control parameters minimizing the human effort (i.e, minimizing the human-robot interaction forces). The proposed approach has been validated through an experimental procedure. A lifting task has been considered as the reference validation application (weight of the manipulated part: 10 kg unknown to the robot controller). A KUKA LBR iiwa 14 R820 has been used as a test platform. Qualitative performance (i.e, questionnaire on perceived collaboration) have been evaluated. Achieved results have been compared with previous developed offline model-free optimized controllers and with the robot manual guidance controller. The proposed MBRL variable impedance controller shows improved human-robot collaboration. The proposed controller is capable to actively assist the human in the target task, compensating for the unknown part weight. The human-robot interaction dynamic model has been trained with a few initial experiments (30 initial experiments). In addition, the possibility to keep the learning of the human-robot interaction dynamics active allows accounting for the adaptation of human motor system
Analysis and synthesis of LinWWC-VSA, a Variable Stiffness Actuator for linear motion
No full textThis work presents the principle of operation of LinWWC-VSA, a Variable Stiffness Actuator (VSA) suitable to perform linear motions, conversely to the vast majority of VSAs typically designed to perform rotational movements and often affected by limits in the actually exploitable range of motions. It features two antagonist nonlinear equivalent springs, each of them made up of a cam wrapped by a wire and constrained by a torsion spring. This work presents methods both for the analysis and the synthesis of the actuator. Two synthesis methods, one numerical and one analytic, are described to design the cam profile as function of the desired stiffness-displacement characteristic of each equivalent nonlinear spring. The analytic method exploits the peculiar formulation of the logarithmic spiral. The theoretical aspects of the actuator are accompanied by numerical simulations
Flexible robot-based cast iron deburring cell for small batch production using single-point laser sensor
No full textThe presented work here is devoted to the definition of innovative methodologies to speed up the programming time of a robotized deburring task. The proposed solutions are defined in a standard cast iron foundry scenario, where the deburring workstations are equipped with flexible but inaccurate fixturing system, the working environment is dirty, and the production is characterized by small batches. The developed system exploits a 3D vision sensor, namely a single-point laser displacement sensor (SP-LS), in combination to a handshaking communication process for the robot-sensor information synchronization. Such approach enables the robot to be used as a measuring instrument allowing a fast reconstruction of 3D images extremely robust in hard working conditions. Adopting a two-stage methodology, the comparison of the reconstructed 3D point cloud with the nominal 3D point cloud allows the automatic adjustment of the robot deburring trajectories. An experimental campaign demonstrates the feasibility and the effectiveness of the proposed solutions
Design methodology of an active back-support exoskeleton with adaptable backbone-based kinematics
No full textManual labor is still strongly present in many industrial contexts (such as aerospace industry). Such operations commonly involve onerous tasks requiring to work in non-ergonomic conditions and to manipulate heavy parts. As a result, work-related musculoskeletal disorders are a major problem to tackle in workplace. In particular, back is one of the most affected regions. To solve such issue, many efforts have been made in the design and control of exoskeleton devices, relieving the human from the task load. Besides upper limbs and lower limbs exoskeletons, back-support exoskeletons have been also investigated, proposing both passive and active solutions. While passive solutions cannot empower the human's capabilities, common active devices are rigid, without the possibility to track the human's spine kinematics while executing the task. The here proposed paper describes a methodology to design an active back-support exoskeleton with backbone-based kinematics. On the basis of the (easily implementable) scissor hinge mechanism, a one-degree of freedom device has been designed. In particular, the resulting device allows tracking the motion of a reference vertebra, i.e., the vertebrae in the correspondence of the connection between the scissor hinge mechanism and the back of the operator. Therefore, the proposed device is capable to adapt to the human posture, guaranteeing the support while relieving the person from the task load. In addition, the proposed mechanism can be easily optimized and realized for different subjects, involving a subject-based design procedure, making possible to adapt its kinematics to track the spine motion of the specific user. A prototype of the proposed device has been 3D-printed to show the achieved kinematics. Preliminary tests for discomfort evaluation show the potential of the proposed methodology, foreseeing extensive subjects-based optimization, realization and testing of the device
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