Upper limbs motion tracking for collaborative robotic applications

4noIn the perspective of Industry 4.0, the contemporary presence of workers and robots in the same workspace requires the development of human motion prediction algorithms for a safe and efficient interaction. In this context, the purpose of the present study was to perform an operation of sensor fusion, by creating a collection of spatial and inertial variables of human upper limbs kinematics of typical industrial movements. Spatial and inertial data of ten healthy young subjects performing three pick and place gestures at different heights were measured with a stereophotogrammetric system and Inertial Measurement Units, respectively. Elbow and shoulder angles estimated from both instruments according to a multibody approach showed very similar trends. Moreover, two variables of the database were identified as distinctive features able to differentiate among the three gestures of pick and place.partially_openembargoed_20210806Digo E.; Antonelli M.; Pastorelli S.; Gastaldi L.Digo, E.; Antonelli, M.; Pastorelli, S.; Gastaldi, L

Reachability-based Trajectory Design with Neural Implicit Safety Constraints

Generating safe motion plans in real-time is a key requirement for deploying robot manipulators to assist humans in collaborative settings. In particular, robots must satisfy strict safety requirements to avoid self-damage or harming nearby humans. Satisfying these requirements is particularly challenging if the robot must also operate in real-time to adjust to changes in its environment.This paper addresses these challenges by proposing Reachability-based Signed Distance Functions (RDFs) as a neural implicit representation for robot safety. RDF, which can be constructed using supervised learning in a tractable fashion, accurately predicts the distance between the swept volume of a robot arm and an obstacle. RDF's inference and gradient computations are fast and scale linearly with the dimension of the system; these features enable its use within a novel real-time trajectory planning framework as a continuous-time collision-avoidance constraint. The planning method using RDF is compared to a variety of state-of-the-art techniques and is demonstrated to successfully solve challenging motion planning tasks for high-dimensional systems faster and more reliably than all tested methods

Improving Efficiency of Human-Robot Coexistence While Guaranteeing Safety: Theory and User Study

Guaranteeing safety for humans in shared workspaces is not trivial. Not only must all possible situations be provably safe, but the human must feel safe as well. While robots are gradually leaving their cages, due to strict safety requirements, engineers often only replace physical cages with static safety zones - when the safety zone is entered, the robot is forced to stop. This can lead to excessive robot downtime. We present a concept for guaranteeing non-collision between humans and robots whilst maximising robot uptime and staying on-path. We evaluate how users react to this approach, in a trial over three non-consecutive days, compared to a control approach of static safety zones. We measure working efficiency as well as human factors such as trust, understanding of the robot, and perceived safety. Using our approach, the robot is indeed more efficient compared to static safety zones and the effect persists over multiple trials on separate days. We also observed that understanding of the robot's movement increased for our method over the course of trials, and the perceived safety of the robot increased for both our method and the control

Human tracking from quantised sensors: An application to safe human–robot collaboration

The proliferation of cage-less robotic applications is justifying this research which proposes a method to process the output of safety sensors with the aim of maximising the productivity of the robot in a collaborative scenario. Particularly, the Speed and Separation Monitoring (SSM) strategy, which prescribes the robot to reduce its speed proportionally to the vicinity of the human, will be investigated. In state-of-the-art industrial implementations, SSM is implemented in a very conservative way, without exploiting the capabilities of modern sensing devices. This work proposes a methodology to improve the performance of SSM algorithms while dealing finite and quantised 2D cost-effective sensing capabilities. The strategy is verified experimentally as applied on a palletising application with a COMAU SMARTSix industrial robot, showing slightly improved performance with respect to standard practice

Emerging research fields in safety and ergonomics in industrial collaborative robotics: A systematic literature review

Abstract Human–robot collaboration is a main technology of Industry 4.0 and is currently changing the shop floor of manufacturing companies. Collaborative robots are innovative industrial technologies introduced to help operators to perform manual activities in so called cyber-physical production systems and combine human inimitable abilities with smart machines strengths. Occupational health and safety criteria are of crucial importance in the implementation of collaborative robotics. Therefore, it is necessary to assess the state of the art for the design of safe and ergonomic collaborative robotic workcells. Emerging research fields beyond the state of the art are also of special interest. To achieve this goal this paper uses a systematic literature review methodology to review recent technical scientific bibliography and to identify current and future research fields. Main research themes addressed in the recent scientific literature regarding safety and ergonomics (or human factors) for industrial collaborative robotics were identified and categorized. The emerging research challenges and research fields were identified and analyzed based on the development of publications over time (annual growth)

Collection and analysis of human upper limbs motion features for collaborative robotic applications

Background: The technologies of Industry 4.0 are increasingly promoting an operation of human motion prediction for improvement of the collaboration between workers and robots. The purposes of this study were to fuse the spatial and inertial data of human upper limbs for typical industrial pick and place movements and to analyze the collected features from the future perspective of collaborative robotic applications and human motion prediction algorithms. (2) Methods: Inertial Measurement Units and a stereophotogrammetric system were adopted to track the upper body motion of 10 healthy young subjects performing pick and place operations at three different heights. From the obtained database, 10 features were selected and used to distinguish among pick and place gestures at different heights. Classification performances were evaluated by estimating confusion matrices and F1-scores. (3) Results: Values on matrices diagonals were definitely greater than those in other positions. Furthermore, F1-scores were very high in most cases. (4) Conclusions: Upper arm longitudinal acceleration and markers coordinates of wrists and elbows could be considered representative features of pick and place gestures at different heights, and they are consequently suitable for the definition of a human motion prediction algorithm to be adopted in effective collaborative robotics industrial applications

A Time of Flight on-Robot Proximity Sensing System for Collaborative Robotics

The sensor system presented in this work demonstrates the results of designing an industrial grade exteroceptive sensing device for proximity sensing for collaborative robots. The intention of this design\u27s application is to develop an on-robot small footprint proximity sensing device to prevent safety protected stops from halting a robot during a manufacturing process. Additionally, this system was design to be modular and fit on an size or shape robotic link expanding the sensor system\u27s use cases vastly. The design was assembled and put through a number of benchmark tests to validate the performance of the time of flight (ToF) sensor system when used in proximity sensing: Single Sensor Characterization, Sensor Overlap Characterization, and Sensor Ranging Under Motion. Through these tests, the ToF sensor ring achieves real time data throughput while minimizing blind spots. Lastly, the sensor system was tested at a maximum throughput load of 32 ToF sensors and maintained a stable throughput of data from all sensors in real time

Towards Safe Autonomy in Assistive Robots

Robots have the potential to support older adults and persons with disabilities on a direct and personal level. For example, a wearable robot may help a person stand up from a chair, or a robotic manipulator may aid a person with meal preparation and housework. Assistive robots can autonomously make decisions about how best to support a person. However, this autonomy is potentially dangerous; robots can cause collisions or falls which may lead to serious injury. Therefore, guaranteeing that assistive robots operate safely is imperative. This dissertation advances safe autonomy in assistive robots by developing a suite of tools for the tasks of perception, monitoring, manipulation and all prevention. Each tool provides a theoretical guarantee of its correct performance, adding a necessary layer of trust and protection when deploying assistive robots. The topic of interaction, or how a human responds to the decisions made by assistive robots, is left for future work. Perception: Assistive robots must accurately perceive the 3D position of a person's body to avoid collisions and build predictive models of how a person moves. This dissertation formulates the problem of 3D pose estimation from multi-view 2D pose estimates as a sum-of-squares optimization problem. Sparsity is leveraged to efficiently solve the problem, which includes explicit constraints on the link lengths connecting any two joints. The method certifies the global optimality of its solutions over 99 percent of the time, and matches or exceeds state-of-the-art accuracy while requiring less computation time and no 3D training data. Monitoring: Assistive robots may mitigate fall risk by monitoring changes to a person’s stability over time and predicting instabilities in real time. This dissertation presents Stability Basins which characterize stability during human motion, with a focus on sit-to-stand. An 11-person experiment was conducted in which subjects were pulled by motor-driven cables as they stood from a chair. Stability Basins correctly predicted instability (stepping or sitting) versus task success with over 90 percent accuracy across three distinct sit-to-stand strategies. Manipulation: Robotic manipulators can support many common activities like feeding, dressing, and cleaning. This dissertation details ARMTD (Autonomous Reachability-based Manipulator Trajectory Design) for receding-horizon planning of collision-free manipulator trajectories. ARMTD composes reachable sets of the manipulator through workspace from low dimensional trajectories of each joint. ARMTD creates strict collision-avoidance constraints from these sets, which are enforced within an online trajectory optimization. The method is demonstrated for real-time planning in simulation and on hardware on a Fetch Mobile Manipulator robot, where it never causes a collision. Fall Prevention: Wearable robots may prevent falls by quickly reacting when a user trips or slips. This dissertation presents TRIP-RTD (Trip Recovery in Prostheses via Reachability-based Trajectory Design), which extends the ARMTD framework to robotic prosthetic legs. TRIP-RTD uses predictions of a person’s response to a trip to plan recovery trajectories of a prosthetic leg. TRIP-RTD creates constraints for an online trajectory optimization which ensure the prosthetic foot is placed correctly across a range of plausible human responses. The approach is demonstrated in simulation using data of non-amputee subjects being tripped.PHDMechanical EngineeringUniversity of Michigan, Horace H. Rackham School of Graduate Studieshttp://deepblue.lib.umich.edu/bitstream/2027.42/169822/1/pdholmes_1.pd

Safety Awareness for Rigid and Elastic Joint Robots: An Impact Dynamics and Control Framework

This thesis aims at making robots with rigid and elastic joints aware of human collision safety. A framework is proposed that captures human injury occurrence and robot inherent safety properties in a unified manner. It allows to quantitatively compare and optimize the safety characteristics of different robot designs and is applied to stationary and mobile manipulators. On the same basis, novel motion control schemes are developed and experimentally validated