Augmented Reality Remote Operation of Dual Arm Manipulators in Hot Boxes

In nuclear isotope and chemistry laboratories, hot cells and gloveboxes provide scientists with a controlled and safe environment to perform experiments. Working on experiments in these isolated containment cells requires scientists to be physically present. For hot cell work today, scientists manipulate equipment and radioactive material inside through a bilateral mechanical control mechanism. Motions produced outside the cell with the master control levers are mechanically transferred to the internal grippers inside the shielded containment cell. There is a growing need to have the capability to conduct experiments within these cells remotely. A simple method to enable remote manipulations within hot cell and glovebox cells is to mount two robotic arms inside a box to mimic the motions of human hands. An AR application was built in this work to allow a user wearing a Microsoft HoloLens 2 headset to teleoperate dual arm manipulators by grasping robotic end-effector digital replicas in AR from a remote location. In addition to the real-time replica of the physical robotic arms in AR, the application enables users to view a live video stream attached to the robotic arms and parse a 3D point cloud of 3D objects in their remote AR environment for better situational awareness. This work also provides users with virtual fixture to assist in manipulation and other teleoperation tasks.

Design and Control of a Quasi-Direct Drive Soft Hybrid Knee Exoskeleton for Injury Prevention during Squatting

This paper presents a new design approach of wearable robots that tackle the three barriers to mainstay practical use of exoskeletons, namely discomfort, weight of the device, and symbiotic control of the exoskeleton-human co-robot system. The hybrid exoskeleton approach, demonstrated in a soft knee industrial exoskeleton case, mitigates the discomfort of wearers as it aims to avoid the drawbacks of rigid exoskeletons and textile-based soft exosuits. Quasi-direct drive actuation using high-torque density motors minimizes the weight of the device and presents high backdrivability that does not restrict natural movement. We derive a biomechanics model that is generic to both squat and stoop lifting motion. The control algorithm symbiotically detects posture using compact inertial measurement unit (IMU) sensors to generate an assistive profile that is proportional to the biological torque generated from our model. Experimental results demonstrate that the robot exhibits 1.5 Nm torque when it is unpowered and 0.5 Nm torque with zero-torque tracking control. The efficacy of injury prevention is demonstrated with one healthy subject. Root mean square (RMS) error of torque tracking is less than 0.29 Nm (1.21% of 24 Nm peak torque) for 50% assistance of biological torque. Comparing to the squat without exoskeleton, the maximum amplitude of the knee extensor muscle activity (rectus femoris) measured by Electromyography (EMG) sensors is reduced by 30% with 50% assistance of biological torque.

Task scheduling system for UAV operations in indoor environment

Application of UAV in indoor environment is emerging nowadays due to the advancements in technology. UAV brings more space-flexibility in an occupied or hardly-accessible indoor environment, e.g., shop floor of manufacturing industry, greenhouse, nuclear powerplant. UAV helps in creating an autonomous manufacturing system by executing tasks with less human intervention in time-efficient manner. Consequently, a scheduler is one essential component to be focused on; yet the number of reported studies on UAV scheduling has been minimal. This work proposes a methodology with a heuristic (based on Earliest Available Time algorithm) which assigns tasks to UAVs with an objective of minimizing the makespan. In addition, a quick response towards uncertain events and a quick creation of new high-quality feasible schedule are needed. Hence, the proposed heuristic is incorporated with Particle Swarm Optimization (PSO) algorithm to find a quick near optimal schedule. This proposed methodology is implemented into a scheduler and tested on a few scales of datasets generated based on a real flight demonstration. Performance evaluation of scheduler is discussed in detail and the best solution obtained from a selected set of parameters is reported.