1,721,069 research outputs found
Gradient Descent-Based Task-Orientation Robot Control Enhanced With Gaussian Process Predictions
No full textThis letter proposes a novel force-based task-orientation controller for interaction tasks with environmental orientation uncertainties. The main aim of the controller is to align the robot tool along the main task direction (e.g., along screwing, insertion, polishing, etc.) without the use of any external sensors (e.g., vision systems), relying only on end-effector wrench measurements/estimations. We propose a gradient descent-based orientation controller, enhancing its performance with the orientation predictions provided by a Gaussian Process model. Derivation of the controller is presented, together with simulation results (considering a probing task) and experimental results involving various re-orientation scenarios, i.e., i) a task with the robot in interaction with a soft environment, ii) a task with the robot in interaction with a stiff and inclined environment, and iii) a task to enable the assembly of a gear into its shaft. The proposed controller is compared against a state-of-the-art approach, highlighting its ability to re-orient the robot tool even in complex tasks (where the state-of-the-art method fails)
Sensorless environment stiffness and interaction force estimation for impedance control tuning in robotized interaction tasks
Full text linkIndustrial robots are increasingly used to perform tasks requiring an interaction with the surrounding environment (e.g., assembly tasks). Such environments are usually (partially) unknown to the robot, requiring the implemented controllers to suitably react to the established interaction. Standard controllers require force/torque measurements to close the loop. However, most of the industrial manipulators do not have embedded force/torque sensor(s) and such integration results in additional costs and implementation effort. To extend the use of compliant controllers to sensorless interaction control, a model-based methodology is presented in this paper. Relying on sensorless Cartesian impedance control, two Extended Kalman Filters (EKF) are proposed: an EKF for interaction force estimation and an EKF for environment stiffness estimation. Exploiting such estimations, a control architecture is proposed to implement a sensorless force loop (exploiting the provided estimated force) with adaptive Cartesian impedance control and coupling dynamics compensation (exploiting the provided estimated environment stiffness). The described approach has been validated in both simulations and experiments. A Franka EMIKA panda robot has been used. A probing task involving different materials (i.e., with different - unknown - stiffness properties) has been considered to show the capabilities of the developed EKFs (able to converge with limited errors) and control tuning (preserving stability). Additionally, a polishing-like task and an assembly task have been implemented to show the achieved performance of the proposed methodology
Enhancing Disassembly Practices for Electric Vehicle Battery Packs: A Narrative Comprehensive Review
Full text linkIn the context of current societal challenges, such as climate neutrality, industry digitization, and circular economy, this paper addresses the importance of improving recycling practices for electric vehicle (EV) battery packs, with a specific focus on lithium–ion batteries (LIBs). To achieve this, the paper conducts a systematic review (using Google Scholar, Scopus, and Web of Science as search engines), considering the last 10 years, to examine existing recycling methods, robotic/collaborative disassembly cells, and associated control techniques. The aim is to provide a comprehensive and detailed review that can serve as a valuable resource for future research in the industrial domain. By analyzing the current state of the field, this review identifies emerging needs and challenges that need to be addressed for the successful implementation of automatic robotic disassembly cells for end-of-life (EOL) electronic products, such as EV LIBs. The findings presented in this paper enhance our understanding of recycling practices and lay the groundwork for more precise research directions in this important area
Robot control parameters auto-tuning in trajectory tracking applications
No full textAutonomy is increasingly demanded to industrial manipulators. Robots have to be capable to regulate their behavior to different operational conditions, adapting to the specific task to be executed without requiring high time/resource-consuming human intervention. Achieving an automated tuning of the control parameters of a manipulator is still a challenging task, which involves modeling/identification of the robot dynamics. This usually results in an onerous procedure, both in terms of experimental and data-processing time. This paper addresses the problem of automated tuning of the manipulator controller for trajectory tracking, optimizing control parameters based on the specific trajectory to be executed. A Bayesian optimization algorithm is proposed to tune both the low-level controller parameters (i.e., the equivalent link-masses of the feedback linearizator and the feedforward controller) and the high-level controller parameters (i.e., the joint PID gains). The algorithm adapts the control parameters through a data-driven procedure, optimizing a user-defined trajectory-tracking cost. Safety constraints ensuring, e.g., closed-loop stability and bounds on the maximum joint position error are also included. The performance of proposed approach is demonstrated on a torque-controlled 7-degree-of-freedom FRANKA Emika robot manipulator. The 25 robot control parameters (i.e., 4 link-mass parameters and 21 PID gains) are tuned in less than 130 iterations, and comparable results with respect to the FRANKA Emika embedded position controller are achieved. In addition, the generalization capabilities of the proposed approach are shown exploiting the proper reference trajectory for the tuning of the control parameters
One-Stage Auto-Tuning Procedure of Robot Dynamics and Control Parameters for Trajectory Tracking Applications
No full textAutonomy is increasingly demanded by industrial manipulators. Robots have to be capable to regulate their behavior to different operational conditions, without requiring high time/resource-consuming human intervention. Achieving an automated tuning of the control parameters of a manipulator is still a challenging task. This paper addresses the problem of automated tuning of the manipulator controller for trajectory tracking. A Bayesian optimization algorithm is proposed to tune both the low-level controller parameters (i.e., robot dynamics compensation) and the high-level controller parameters (i.e., the joint PID gains). The algorithm adapts the control parameters through a data-driven procedure, optimizing a userdefined trajectory-tracking cost. Safety constraints ensuring, e.g., closed-loop stability and bounds on the maximum joint position errors, are also included. The performance of the proposed approach is demonstrated on a torque-controlled 7degree-of-freedom FRANKA Emika robot manipulator. The 25 robot control parameters (i.e., 4 link-mass parameters and 21 PID gains) are tuned in 125 iterations, and comparable results with respect to the FRANKA Emika embedded position controller are achieved
Combining Sampling- and Gradient-based Planning for Contact-rich Manipulation
No full textPlanning for contact-rich manipulation involves discontinuous dynamics, which presents challenges to planning methods. Sampling-based planners have higher sample complexity in high-dimensional problems and cannot efficiently handle state constraints such as force limits. Gradient-based solvers can suffer from local optima and their convergence rate is often worse on non-smooth problems. We propose a planning method that is both sampling- and gradient-based, using the Cross-entropy Method to initialize a gradient-based solver, providing better initialization to the gradient-based method and allowing explicit handling of state constraints. The sampling-based planner also allows direct integration of a particle filter, which is here used for online contact mode estimation. The approach is shown to improve performance in MuJoCo environments and the effects of problem stiffness and planing horizon are investigated. The estimator and planner are then applied to an impedance-controlled robot, showing a reduction in solve time in contact transitions to only gradient-based
Two-stage robot controller auto-tuning methodology for trajectory tracking applications
No full textAutonomy is increasingly demanded of industrial manipulators. Robots have to be capable of regulating their behavior to different operational conditions, without requiring high time/resource-consuming human intervention. Achieving an automated tuning of the control parameters of a manipulator is still a challenging task. This paper addresses the problem of automated tuning of the manipulator controller for trajectory tracking. A Bayesian optimization algorithm is proposed to tune firstly the low-level controller parameters (i.e., robot dynamics compensation), then the high-level controller parameters (i.e., the joint PID gains), providing a two-stage robot controller auto-tuning methodology. In both the optimization phases, the algorithm adapts the control parameters through a data-driven procedure, optimizing a user-defined trajectory tracking cost. Safety constraints ensuring, e.g., closed-loop stability and bounds on the maximum joint position errors, are also included. The performance of the proposed approach is demonstrated on a torque-controlled 7-degree-of-freedom FRANKA Emika robot manipulator. The 4 robot dynamics parameters (i.e., 4 link-mass parameters) are tuned in 40 iterations, while the robot control parameters (i.e., 21 PID gains) are tuned in 90 iterations. Comparable trajectory tracking-errors results with respect to the FRANKA Emika embedded position controller are achieved
Robust state dependent Riccati equation variable impedance control for robotic force-tracking tasks
No full textIndustrial robots are increasingly used in highly flexible interaction tasks, where the intrinsic variability makes difficult to pre-program the manipulator for all the different scenarios. In such applications, interaction environments are commonly (partially) unknown to the robot, requiring the implemented controllers to take in charge for the stability of the interaction. While standard controllers are sensor-based, there is a growing need to make sensorless robots (i.e., most of the commercial robots are not equipped with force/torque sensors) able to sense the environment, properly reacting to the established interaction. This paper proposes a new methodology to sensorless force control manipulators. On the basis of sensorless Cartesian impedance control, an Extended Kalman Filter (EKF) is designed to estimate the interaction exchanged between the robot and the environment. Such an estimation is then used in order to close a robust high-performance force loop, designed exploiting a variable impedance control and a State Dependent Riccati Equation (SDRE) force controller. The described approach has been validated in simulations. A Franka EMIKA panda robot has been considered as a test platform. A probing task involving different materials (i.e., with different stiffness properties) has been considered to show the capabilities of the developed EKF (able to converge with limited errors) and controller (preserving stability and avoiding overshoots). The proposed controller has been compared with an LQR controller to show its improved performance
High-accuracy robotized industrial assembly task control schema with force overshoots avoidance
No full textThe presented paper proposes an analytical force overshoots free control architecture for standard industrial manipulators involved in high-accuracy industrial assembly tasks (i.e., with tight mounting tolerances). As in many industrial scenarios, the robot manipulates components through (compliant) external grippers and interacts with partially unknown compliant environments. In such a context, a force overshoot may result in task failures (e.g., gripper losses the component, component damages), representing a critical control issue. To face such problem, the proposed control architecture makes use of the force measurements as a feedback (obtained using a force/torque sensor at the robot end-effector) and of the estimation of the equivalent interacting elastic system stiffness (i.e., force sensor– compliant gripper–compliant environment equivalent stiffness) defining two control levels: (i) an internal impedance controller with inner position and orientation loop and (ii) an external impedance shaping force tracking controller. A theoretical analysis of the method has been performed. Then, the method has been experimentally validated in an industrial-like assembly task with tight mounting tolerances (i.e., H7/h6 mounting). A standard industrial robot (a Universal Robot UR 10 manipulator) has been used as a test-platform, equipped with an external force/torque sensor Robotiq FT 300 at the robot end-effector and with a Robotiq Adaptive Gripper C-Model to manipulate target components. ROS framework has been adopted to implement the proposed control architecture. Experimental results show the avoidance of force overshoots and the achieved target dynamic performance
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