Modelling and Control of a 2-DOF Robot Arm with Elastic Joints for Safe Human-Robot Interaction.
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ABSTRACT:
SUBMITTER: Tuan HM
PROVIDER: S-EPMC8416520 | biostudies-literature |
REPOSITORIES: biostudies-literature
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Project description:This article provides a perspective on estimation and control problems in cyberphysical human systems (CPHSs) that work at the intersection of cyberphysical systems and human systems. The article also discusses solutions to some of the problems in CPHSs. One example of a CPHS is a close-proximity human-robot collaboration (HRC) in a manufacturing setting. The issue of the joint operation's efficiency and human factors, such as safety, attention, mental states, and comfort, naturally arise in the HRC context. By considering human factors, robots' actions can be controlled to achieve objectives, including safe operations and human comfort. Alternately, questions arise when robot factors are considered. For example, can we provide direct inputs and information to humans about an environment and the robots in the area such that the objectives of safety, efficiency, and comfort can be satisfied by considering the robots' current capabilities? The article discusses specific problems involved in HRC related to controlling a robot's motion by taking the current actions of the human in the loop with the robot's control system. To this end, two main challenges are discussed: 1) inferring the intention behind human actions by analyzing a person's motion as observed through skeletal tracking and gaze data and 2) a controller design that keeps robot motion constrained to a boundary in a 3D space by using control barrier functions. The intention inference method fuses skeleton-joint tracking data obtained using the Microsoft Kinect sensor and human gaze data gathered from red-green-blue Kinect images. The direction of a human's hand-reaching motion and a goal-reaching point is estimated while performing a joint pick-and-place task. The trajectory of the hand is estimated forward in time based on the gaze and hand motion data at the current time instance. A barrier function method is applied to generate safe robot trajectories along with forecast hand movements to complete the collaborative displacement of an object by a person and a robot. An adaptive controller is then used to track the reference trajectories using the Baxter robot, which is tested in a Gazebo simulation environment.
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Project description:BackgroundRehabilitation robotics is progressing towards developing robots that can be used as advanced tools to augment the role of a therapist. These robots are capable of not only offering more frequent and more accessible therapies but also providing new insights into treatment effectiveness based on their ability to measure interaction parameters. A requirement for having more advanced therapies is to identify how robots can 'adapt' to each individual's needs at different stages of recovery. Hence, our research focused on developing an adaptive interface for the GENTLE/A rehabilitation system. The interface was based on a lead-lag performance model utilising the interaction between the human and the robot. The goal of the present study was to test the adaptability of the GENTLE/A system to the performance of the user.MethodsPoint-to-point movements were executed using the HapticMaster (HM) robotic arm, the main component of the GENTLE/A rehabilitation system. The points were displayed as balls on the screen and some of the points also had a real object, providing a test-bed for the human-robot interaction (HRI) experiment. The HM was operated in various modes to test the adaptability of the GENTLE/A system based on the leading/lagging performance of the user. Thirty-two healthy participants took part in the experiment comprising of a training phase followed by the actual-performance phase.ResultsThe leading or lagging role of the participant could be used successfully to adjust the duration required by that participant to execute point-to-point movements, in various modes of robot operation and under various conditions. The adaptability of the GENTLE/A system was clearly evident from the durations recorded. The regression results showed that the participants required lower execution times with the help from a real object when compared to just a virtual object. The 'reaching away' movements were longer to execute when compared to the 'returning towards' movements irrespective of the influence of the gravity on the direction of the movement.ConclusionsThe GENTLE/A system was able to adapt so that the duration required to execute point-to-point movement was according to the leading or lagging performance of the user with respect to the robot. This adaptability could be useful in the clinical settings when stroke subjects interact with the system and could also serve as an assessment parameter across various interaction sessions. As the system adapts to user input, and as the task becomes easier through practice, the robot would auto-tune for more demanding and challenging interactions. The improvement in performance of the participants in an embedded environment when compared to a virtual environment also shows promise for clinical applicability, to be tested in due time. Studying the physiology of upper arm to understand the muscle groups involved, and their influence on various movements executed during this study forms a key part of our future work.
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