Mix Frame Visual Servo Control Framework for Autonomous Assistive Robotic Arms

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.

Customizing skills for assistive robotic manipulators, an inverse reinforcement learning approach with error-related potentials

Robotic assistance via motorized robotic arm manipulators can be of valuable assistance to individuals with upper-limb motor disabilities. Brain-computer interfaces (BCI) offer an intuitive means to control such assistive robotic manipulators. However, BCI performance may vary due to the non-stationary nature of the electroencephalogram (EEG) signals. It, hence, cannot be used safely for controlling tasks where errors may be detrimental to the user. Avoiding obstacles is one such task. As there exist many techniques to avoid obstacles in robotics, we propose to give the control to the robot to avoid obstacles and to leave to the user the choice of the robot behavior to do so a matter of personal preference as some users may be more daring while others more careful. We enable the users to train the robot controller to adapt its way to approach obstacles relying on BCI that detects error-related potentials (ErrP), indicative of the user’s error expectation of the robot’s current strategy to meet their preferences. Gaussian process-based inverse reinforcement learning, in combination with the ErrP-BCI, infers the user’s preference and updates the obstacle avoidance controller so as to generate personalized robot trajectories. We validate the approach in experiments with thirteen able-bodied subjects using a robotic arm that picks up, places and avoids real-life objects. Results show that the algorithm can learn user’s preference and adapt the robot behavior rapidly using less than five demonstrations not necessarily optimal.

Living With Assistive Robotics: Exploring the Everyday Use of Exoskeleton for Persons With Spinal Cord Injury

Background: Recent advancements in sensor technology and artificial intelligence mechanisms have led to a rapid increase in research and development of robotic orthoses or “exoskeletons” to support people with mobility problems. The purpose of this case study was to provide insight into the lived reality of using the assistive robotic exoskeleton ReWalk.Method: We used ethnographic techniques to explore the everyday experience and use of the assistive robotic device.Results: We found that the appropriation and integration of the technology within the patient's everyday lives required a social and collaborative effort, which continued into use. The decisions to utilise the technology (or not) was closely tied to physical, social, cultural, environmental, and psychological factors. Consequently, there was much variation in patients' perception of the technology and opportunities for support. Four themes emerged:(a) Meaning of mobility—physical mobility represents more than functional ability. Its present socio-cultural meaning is associated with an individual's self-identity and life priorities.(b) Accomplishing body-technique—integration with the body requires a long process of skill acquisition and re-embodiment.(c) Adaptation and adjustment in use—successful use of the technology was characterised by ongoing adjustment and adaptation of the technology and ways of using it.(d) Human element—introduction and sustained use of the exoskeleton demand a social and collaborative effort across the user's professional and lay resources.Conclusions: This study highlights that the development and implementation of the technology need to be grounded in a deep understanding of the day-to-day lives and experiences of the people that use them.

Embedding ROS and AI-Based Perception Capabilities in a Novel Low-Cost Assistive Robotic Platform

This paper describes how we developed a novel low-cost assistive robotic platform, with AI-based perception capabilities, able to navigate autonomously using Robot Operating System (ROS). The platform is a differential wheeled robot, equipped with two motors and encoders, which are controlled with an Arduino board. It also includes a Jetson Xavier processing board on which we deploy all AI processes, and the ROS architecture. As a result of the work, we have a fully functional platform, able to recognize actions online, and navigate autonomously through environments whose map has been preloaded.

Quadrupedal Human-Assistive Robotic Platform (Q-HARP): Design, Control, and Preliminary Testing

With the rapid expansion of older adult populations around the world, mobility impairment is becoming an increasingly challenging issue. For the assistance of individuals with mobility impairments, there are two major types of tools in the current practice, including the passive (unpowered) walking aids (canes, walkers, rollators, etc.) and wheelchairs (powered and unpowered). Despite their extensive use, there are significant weaknesses that affect their effectiveness in daily use, especially when challenging uneven terrains are encountered. To address these issues, the authors developed a novel robotic platform intended for the assistance of mobility-challenged individuals. Unlike the existing assistive robots serving similar purposes, the proposed robot, namely, quadrupedal human-assistive robotic platform (Q-HARP), utilizes legged locomotion to provide an unprecedented potential to adapt to a wide variety of challenging terrains, many of which are common in people’s daily life (e.g., roadside curbs and the few steps leading to a front door). In this paper, the design of the robot is presented, including the overall structure of the robot and the design details of the actuated robotic leg joints. For the motion control of the robot, a joint trajectory generator is formulated, with the purpose of generating a stable walking gait to provide reliable support to its human user in the robot’s future application. The Q-HARP robot and its control system were experimentally tested, and the results demonstrated that the robot was able to provide a smooth gait during walking.