Abstract
Assistive robotic arms (ARAs) that provide care to the elderly and people with disabilities, are a significant part of Human-Robot Interaction (HRI). Presently available ARAs provide non-intuitive interfaces such as joysticks for control and thus, lacks the autonomy to perform daily activities. This study proposes that, for inducing autonomous behavior in ARAs, visual sensors integration is vital, and visual servoing in the direct Cartesian control mode is the preferred method. Generally, ARAs are designed in a configuration where its end-effector’s position is defined in the fixed base frame while orientation is expressed in the end-effector frame. We denoted this configuration as ‘mixed frame robotic arms’. Consequently, conventional visual servo controllers which operate in a single frame of reference are incompatible with mixed frame ARAs. Therefore, we propose a mixed-frame visual servo control framework for ARAs. Moreover, we enlightened the task space kinematics of a mixed frame ARAs, which led us to the development of a novel “mixed frame Jacobian matrix”. The proposed framework was validated on a mixed frame JACO-2 7 DoF ARA using an adaptive proportional derivative controller for achieving image-based visual servoing (IBVS), which showed a significant increase of 31% in the convergence rate, outperforming conventional IBVS joint controllers, especially in the outstretched arm positions and near the base frame. Our Results determine the need for the mixed frame controller for deploying visual servo control on modern ARAs, that can inherently cater to the robotic arm’s joint limits, singularities, and self-collision problems.
Highlights
Accepted: 4 January 2022The ultimate goal of science and engineering is to serve humanity and humans by creating ease in their daily lives
Robotic vision has emerged as the vital tool for robots to perceive the environment and acquire autonomy to perform their tasks in human-centric environments in human-robot interactions (HRIs) [4]
Law-Kam Cio et al (2019) [17], integrated a vision sensor to Assistive robotic arms (ARAs) using two Kinect depth cameras, one to identify the user’s face, and another for guiding the robotic arm to grab an object using the look and move method, Sensors 2022, 22, 642 the application was promising; we argue that the method was computationally expensive and an abundance of hardware was mounted on the wheelchair by using two Kinect cameras on a wheelchair, which reduces the user autonomy and mobility of the wheelchair in narrow areas around the house
Summary
Accepted: 4 January 2022The ultimate goal of science and engineering is to serve humanity and humans by creating ease in their daily lives. Large volumes of robots are brought into the industry, but are introduced in dynamic environments that were originally designed for humans, such as in homes, schools, and hospitals [2]. As these environments are dynamic in nature, such workplaces demand a high level of autonomy and dexterity, which needs to be developed in robots to perform their task autonomously [3]. Robotic vision has emerged as the vital tool for robots to perceive the environment and acquire autonomy to perform their tasks in human-centric environments in human-robot interactions (HRIs) [4]
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