CONTROL AND PLANNING FOR MOBILE MANIPULATORS USED IN LARGE SCALE MANUFACTURING PROCESSES

Sanding operations in industry is one of the few manufacturing tasks that has yet to achieve automation. Sanding tasks require skilled operators that have developed a sense of when a work piece is sufficiently sanded. In order to achieve automation in sanding with robotic systems, this developed sense, or intelligence, that human operators have needs to be understood and implemented in order to achieve, at the minimum, the same quality of work. The system will also need to have the equivalent reach of a human operator and not be constrained to a single, small workspace. This thesis developed solutions for a control scheme and a path planning algorithm to provide the next steps into sanding automation. The control scheme uses found insights on how vibration forces evolve over time during sanding operations to estimate the quality of the surface and adapt the velocity of a sander, akin to how human operators do, with a robotic manipulator. The path planning algorithm was developed to allow for the use of mobile manipulators to perform the sanding tasks and giving the manipulator an equivalent reach to that of an operator

Sensorless Physical Human-robot Interaction Using Deep-Learning

Physical human-robot interaction has been an area of interest for decades. Collaborative tasks, such as joint compliance, demand high-quality joint torque sensing. While external torque sensors are reliable, they come with the drawbacks of being expensive and vulnerable to impacts. To address these issues, studies have been conducted to estimate external torques using only internal signals, such as joint states and current measurements. However, insufficient attention has been given to friction hysteresis approximation, which is crucial for tasks involving extensive dynamic to static state transitions. In this paper, we propose a deep-learning-based method that leverages a novel long-term memory scheme to achieve dynamics identification, accurately approximating the static hysteresis. We also introduce modifications to the well-known Residual Learning architecture, retaining high accuracy while reducing inference time. The robustness of the proposed method is illustrated through a joint compliance and task compliance experiment.Comment: 7 pages, ICRA 2024 Submissio

Towards Human-Robot Collaboration with Parallel Robots by Kinetostatic Analysis, Impedance Control and Contact Detection

Parallel robots provide the potential to be lever-aged for human-robot collaboration (HRC) due to low collision energies even at high speeds resulting from their reduced moving masses. However, the risk of unintended contact with the leg chains increases compared to the structure of serial robots. As a first step towards HRC, contact cases on the whole parallel robot structure are investigated and a disturbance observer based on generalized momenta and measurements of motor current is applied. In addition, a Kalman filter and a second-order sliding-mode observer based on generalized momenta are compared in terms of error and detection time. Gearless direct drives with low friction improve external force estimation and enable low impedance. The experimental validation is performed with two force-torque sensors and a kinetostatic model. This allows a new identification method of the motor torque constant of an assembled parallel robot to estimate external forces from the motor current and via a dynamics model. A Cartesian impedance control scheme for compliant robot-environmental dynamics with stiffness from 0.1-2N/mm and the force observation for low forces over the entire structure are validated. The observers are used for collisions and clamping at velocities of 0.4-0.9 m/s for detection within 9–58 ms and a reaction in the form of a zero-g mode.© 2023 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works

Contact force and torque estimation for collaborative manipulators based on an adaptive Kalman filter with variable time period.

Contact force and torque sensing approaches enable manipulators to cooperate with humans and to interact appropriately with unexpected collisions. In this thesis, various moving averages are investigated and Weighted Moving Averages and Hull Moving Average are employed to generate a mode-switching moving average to support force sensing. The proposed moving averages with variable time period were used to reduce the effects of measured motor current noise and thus provide improved confidence in joint output torque estimation. The time period of the filter adapts continuously to achieve an optimal trade-off between response time and precision of estimation in real-time. An adaptive Kalman filter that consists of the proposed moving averages and the conventional Kalman filter is proposed. Calibration routines for the adaptive Kalman filter interpret the measured motor current noise and errors in the speed data from the individual joints into. The combination of the proposed adaptive Kalman filter with variable time period and its calibration method facilitates force and torque estimation without direct measurement via force/torque sensors. Contact force/torque sensing and response time assessments from the proposed approach are performed on both the single Universal Robot 5 manipulator and the collaborative UR5 arrangement (dual-arm robot) with differing unexpected end effector loads. The combined force and torque sensing method leads to a reduction of the estimation errors and response time in comparison with the pioneering method (55.2% and 20.8 %, respectively), and the positive performance of the proposed approach is further improved as the payload rises. The proposed method can potentially be applied to any robotic manipulators as long as the motor information (current, joint position, and joint velocities) are available. Consequently the cost of implementation will be significantly lower than methods that require load cells

Advanced Strategies for Robot Manipulators

Amongst the robotic systems, robot manipulators have proven themselves to be of increasing importance and are widely adopted to substitute for human in repetitive and/or hazardous tasks. Modern manipulators are designed complicatedly and need to do more precise, crucial and critical tasks. So, the simple traditional control methods cannot be efficient, and advanced control strategies with considering special constraints are needed to establish. In spite of the fact that groundbreaking researches have been carried out in this realm until now, there are still many novel aspects which have to be explored

Autonomous Visual Servo Robotic Capture of Non-cooperative Target

This doctoral research develops and validates experimentally a vision-based control scheme for the autonomous capture of a non-cooperative target by robotic manipulators for active space debris removal and on-orbit servicing. It is focused on the final capture stage by robotic manipulators after the orbital rendezvous and proximity maneuver being completed. Two challenges have been identified and investigated in this stage: the dynamic estimation of the non-cooperative target and the autonomous visual servo robotic control. First, an integrated algorithm of photogrammetry and extended Kalman filter is proposed for the dynamic estimation of the non-cooperative target because it is unknown in advance. To improve the stability and precision of the algorithm, the extended Kalman filter is enhanced by dynamically correcting the distribution of the process noise of the filter. Second, the concept of incremental kinematic control is proposed to avoid the multiple solutions in solving the inverse kinematics of robotic manipulators. The proposed target motion estimation and visual servo control algorithms are validated experimentally by a custom built visual servo manipulator-target system. Electronic hardware for the robotic manipulator and computer software for the visual servo are custom designed and developed. The experimental results demonstrate the effectiveness and advantages of the proposed vision-based robotic control for the autonomous capture of a non-cooperative target. Furthermore, a preliminary study is conducted for future extension of the robotic control with consideration of flexible joints

ROBOTIC INTERACTION AND COOPERATION. Industrial and rehabilitative applications

The main goal of the thesis is the development of human-robotic interaction control strategies, which enable close collaboration between human and robot. In this framework we studied two di erent aspects, with applications respectively in industrial and rehabilitation domains. In the rst part safety issues are examined on a scenario in which a robot manipulator and a human perform the same task and in the same workspace. During the task execution the human should be able to get into contact with the robot and in this case an estimation algorithm of both interaction forces and contact point is proposed in order to guarantee safety conditions. At the same time, all the unintended contacts have to be avoided, and a suitable post collision strategy has been studied to move away the robot from the collision area or to reduce the impact e orts. However, the second part of the thesis focus on the cooperation between an orthesis and a patient. Indeed, in order to support a rehabilitation process, gait parameters, such as hip and knee angles or the beginning of a gait phase, have been estimated. For this purpose a sensor system, consisting of accelerometers and gyroscopes, and algorithms, developed in order to avoid the error accumulation due to the gyroscopes drift and the vibrations related to the beginning of the stance phase due to the accelerometers, have been proposed.The main goal of the thesis is the development of human-robotic interaction control strategies, which enable close collaboration between human and robot. In this framework we studied two di erent aspects, with applications respectively in industrial and rehabilitation domains. In the rst part safety issues are examined on a scenario in which a robot manipulator and a human perform the same task and in the same workspace. During the task execution the human should be able to get into contact with the robot and in this case an estimation algorithm of both interaction forces and contact point is proposed in order to guarantee safety conditions. At the same time, all the unintended contacts have to be avoided, and a suitable post collision strategy has been studied to move away the robot from the collision area or to reduce the impact e orts. However, the second part of the thesis focus on the cooperation between an orthesis and a patient. Indeed, in order to support a rehabilitation process, gait parameters, such as hip and knee angles or the beginning of a gait phase, have been estimated. For this purpose a sensor system, consisting of accelerometers and gyroscopes, and algorithms, developed in order to avoid the error accumulation due to the gyroscopes drift and the vibrations related to the beginning of the stance phase due to the accelerometers, have been proposed

Neural network based patient recovery estimation of a PAM-based rehabilitation robot

Rehabilitation robots have shown a promise in aiding patient recovery by supporting them in repetitive, systematic training sessions. A critical factor in the success of such training is the patient’s recovery progress, which can guide suitable treatment plans and reduce recovery time. In this study, a neural network-based approach is proposed to estimate the patient’s recovery, which can aid in the development of an assist-as-needed training strategy for the gait training system. Experimental results show that the proposed method can accurately estimate the external torques generated by the patient to determine their recovery. The estimated patient recovery is used for an impedance control of a 2-DOF robotic orthosis powered by pneumatic artificial muscles, which improves the robot joint compliance coefficients and makes the patient more comfortable and confident during rehabilitation exercises