Safe Physical Human-robot Interaction Research Articles

Modelling human postural stability and muscle activation augmented by a supernumerary robotic tail.

Wearable robots have promising characteristics for human augmentation; however, the the design and specification stage needs to consider biomechanical impact. In this work, musculoskeletal software is used to assess the biomechanical implications of having a two-degrees-of-freedom supernumerary robotic tail mounted posterior to the human trunk. Forward and backward tilting motions were assessed to determine the optimal design specification. Specifically; the key criteria utilised included the centre of pressure, the dynamic wrench exerted by the tail onto the human body and a global muscle activation index. Overall, it was found that use of a supernumerary tail reduced lower limb muscle activation in quiet stance. Furthermore, the optimal design specification required a trade-off between the geometric and inertial characteristics, and the amount of muscle assistance provided by the tail to facilitate safe physical Human-Robot interaction. &#xD.

Safe physical human–robot interaction through variable impedance control based on ISO/TS 15066

Safe physical human–robot interaction through variable impedance control based on ISO/TS 15066

Passive Model-Predictive Impedance Control for Safe Physical Human–Robot Interaction

Various cognitive systems have been designed to model the position and stiffness profiles of human behavior and then to drive robots by mimicking the human’s behavior to accomplish physical human-robot interaction tasks through a properly designed impedance controller. However, some studies have shown that variable stiffness parameters of the impedance controller can cause the violation of the passivity constraint of the robot states, and make the robot’s stored energy exceed the external energy injected from the human user, thus leading to the unsafe human-robot interaction. To solve this problem, this paper proposes a novel passive model predictive impedance control method including two control loops. In the bottom-loop of the proposed controller, the robot is driven by a variable impedance controller to achieve the desired compliant interaction behavior. In the top-loop of the proposed controller, the model predictive control (MPC) is used to ensure that the robot states satisfy the passivity constraint by calculating a complementary torque to limit the stored energy of the robot. The passivity of the closed-loop robot system and the feasibility of MPC are guaranteed by theoretical analysis, ensuring the safety of the robotic movement in the human-robot interaction. The effectiveness of the proposed method is demonstrated by the simulation and experiment on the Franka Emika Panda robot.

Variable admittance control for safe physical human–robot interaction considering intuitive human intention

The trajectory planning of the arm is greatly facilitated by the physical direct teaching technology. Variable admittance control is a promising technology when a robot is interacting with a variable environment, such as a human whose stiffness might change during the interaction. Nevertheless, a canonical admittance controller with imperfect parameters may lead to robot oscillation, which brings more challenges to a variable admittance controller. In this paper, we propose an energy-based variable admittance controller with intrinsic oscillation suppression property. During the physical human–robot interaction (pHRI), the human intention is predicted based on the robot’s state and interaction force. The admittance parameters are tuned automatically to conform the robot’s motion to human intention. When the oscillation is detected online by the proposed wavelet module, our variable admittance model reveals oscillation suppression ability because of dissipating the energy generated by high-frequency oscillation. We compared the proposed variable admittance controller with other admittance controller approaches in both simulation and actual robot experiments. The proposed method shows significant improvement in oscillation suppression and human energy conservation in the human–robot interaction application.

Stiffness-Switchable Hydrostatic Transmission Toward Safe Physical Human–Robot Interaction

Stiffness-Switchable Hydrostatic Transmission Toward Safe Physical Human–Robot Interaction

A Passivity-Based Framework for Safe Physical Human–Robot Interaction

In this paper, the problem of making a safe compliant contact between a human and an assistive robot is considered. Users with disabilities have a need to utilize their assistive robots for physical human–robot interaction (PHRI) during certain activities of daily living (ADLs). Specifically, we propose a hybrid force/velocity/attitude control for a PHRI system based on measurements from a six-axis force/torque sensor mounted on the robot wrist. While automatically aligning the end-effector surface with the unknown environmental (human) surface, a desired commanded force is applied in the normal direction while following desired velocity commands in the tangential directions. A Lyapunov-based stability analysis is provided to prove both the convergence as well as passivity of the interaction to ensure both performance and safety. Simulation as well as experimental results verify the performance and robustness of the proposed hybrid controller in the presence of dynamic uncertainties as well as safe physical human–robot interactions for a kinematically redundant robotic manipulator.

Deep Imitation Learning of Nonlinear Model Predictive Control Laws for a Safe Physical Human–Robot Interaction

This paper proposes motion planning algorithms for industrial manipulators in the presence of human operators based on deep neural networks (DNNs), aimed at imitating the behavior of a nonlinear model predictive control (NMPC) scheme. The proposed DNN solutions retain the safety features of NMPC in terms of speed and separation monitoring, defined according to the guidelines in the ISO/TS 15066 standard. At the same time, they improve the robot performance in terms of task completion time, and of a-posteriori evaluation of the NMPC cost function on experimental data. The reasons for this improvement are the reduced computational delay of running a DNN compared to solving the nonlinear programs associated to NMPC, and the ability to implicitly learn how to predict the human operator's motion from the training set.

Passive Decoupled Multitask Controller for Redundant Robots

Kinematic redundancy in robots makes it possible to execute several control tasks simultaneously. As some tasks are usually more important than others, it is reasonable to dynamically decouple them in order to ensure their execution in a hierarchical way or even without any interference at all. The most widely used technique is to decouple the system by feedback linearization. However, this requires actively shaping the inertia and consequently modifying the natural dynamics of the robot. Here, we propose a passivity-based multitask tracking controller that preserves these inertial properties but fully compensates for task-space cross couplings using external-force feedback. In addition, three formal proofs are provided: uniform exponential stability for trajectory tracking, passivity during physical interaction, and input-to-state stability. The controller is validated in simulations and experiments and directly compared with the hierarchical PD+ approach and the feedback linearization. The proposed approach is well suited for safe physical human–robot interaction and dynamic trajectory tracking if measurements or estimations of the external forces are available.

Design and analysis of a novel hybrid-driven continuum robot with variable stiffness

• Hybrid-driven variable stiffness continuum robot with dual-plane constraint design. • Independent robot stiffness regulation and endpoint positioning. • Stiffness independent of PMA radius, making design extensible to multiple sections. • Static model for describing the robot's behavior. Compliance and lower inertia are two essential features for safe physical human-robot interaction (pHRI). Continuum robots exhibit great potential for such situations due to their intrinsic compliance and lightweight links. However, there is a trade-off between compliance and positioning accuracy, of which variable stiffness is a potential method to address this conflict. This paper presents a novel hybrid-driven continuum robot with a dual-plane backbone structure, in which the stiffness regulation is independent from the positioning. Through the dual-plane constraint, the robot's stiffness is made independent of the pneumatic muscle actuations’ (PMAs) radius, making the design easily extensible to multiple sections. Then, a static model based on virtual work is further constructed to characterize the robot's behavior. Experiments and simulations show that the proposed stiffness regulation mechanism has stiffness range of over 278%, and the ratio of positioning error at the endpoint of continuum robot to the total robot length is 2.54%. The modeling simplicity and the decoupling between positioning and stiffness regulation provide a solution for the conflict between positioning accuracy and safety.

Fuzzy variable impedance-based adaptive neural network control in physical human–robot interaction

This article focus on the problems of trajectory tracking and motion constraint for physical human–robot interaction, and a compliant adaptive control method is proposed for stable and safe physical human–robot interaction during the interaction. First, a fuzzy variable impedance control strategy is given to make the robot to use suitable impedance parameters in different motion states, which can improve positioning accuracy and reduce the interaction force. Second, by transforming the safety interaction constraint into output constraint, a tan-type barrier Lyapunov function is presented to guarantee the safety of human partner in physical human–robot interaction. Third, an adaptive neural network is employed to design the adaptive controller to handle with the dynamic uncertainties and improve the robustness of the system. Finally, simulation results of a 2-degree-of-freedom manipulator are presented to show the effectiveness of the proposed method.

Towards Safe Physical Human-Robot Interaction by Exploring the Rapid Stiffness Switching Feature of Discrete Variable Stiffness Actuation

Safety holds the prime importance in direct physical human-robot interaction (pHRI) tasks. Robots should have the ability to handle unexpected collisions in unstructured environments. Collision avoidance based on exteroceptive sensors can work in these scenarios. However, it may not be sufficient, especially considering that the relative motion between robots and humans can be fast and hardly predictable. This highlights the importance of fast and reliable detection and reaction techniques for the collisions. Rapid switching to intrinsic complaint mode upon collisions is a promising solution for this requirement. Recently, we have proposed a new design of the discrete variable stiffness actuator (DVSA), which has the capability of instantaneously switching its stiffness between different predefined levels. We believe that this rapid stiffness switching feature can significantly improve safety during collisions. In this letter, we combined a software-based collision detection method with a hardware-based rapid stiffness switching technique. The proposed strategy has been implemented on a DVSA-based manipulator to evaluate its safety performance in the sudden dynamic collision and static near-singular clamping collision scenarios. The results clearly indicate that the proposed strategy can significantly mitigate the impact of unexpected collisions and improve safety during pHRI.

MOCA-S: A Sensitive Mobile Collaborative Robotic Assistant Exploiting Low-Cost Capacitive Tactile Cover and Whole-Body Control

Safety is one of the most fundamental aspects of robotics, especially when it comes to collaborative robots (cobots) that are expected to physically interact with humans. Although a large body of literature has focused on safety-related aspects for fixed-based cobots, a low effort has been put into developing collaborative mobile manipulators. In response to this need, this work presents MOCA-S, i.e., Sensitive Mobile Collaborative Robotic Assistant, that integrates a low-cost, capacitive tactile cover to measure interaction forces applied to the robot base. The tactile cover comprises a set of 11 capacitive large-area tactile sensors distributed as a 1-D tactile array around the base. Characterization of the tactile sensors with different materials is included. Moreover, two expanded whole-body controllers that exploit the platform’s tactile cover and the loco-manipulation features are proposed. These controllers are tested in two experiments, demonstrating the potential of MOCA-S for safe physical Human-Robot Interaction (pHRI). Finally, an experiment is carried out in which an undesired collision occurs between MOCA-S and a human during a loco-manipulation task. The results demonstrate the intrinsic safety of MOCA-S and the proposed controllers, suggesting a new step towards creating safe mobile manipulators.

Safe physical human-robot interaction: A quasi whole-body sensing method based on novel laser-ranging sensor ring pairs

Non-contact detection is a competitive method for ensuring safety in close human-robot interaction (HRI). However, existing sensor configuration methods have difficulty satisfying the requirements for covering a wide range of sensing zones with fewer spots and a low-cost, low-complexity processing system. In this study, we developed a novel laser-ranging sensor configuration comprising sensors mounted on ring pairs, thereby implementing quasi whole-body sensing and cooperative location with fewer sensors. Each sensor ring pair was composed of double sensor rings mounted at opposite ends of robot arms. Based on the cone detection zone of each laser-ranging sensor, an optimisation of the configuration parameters showed that the maximum sensing volume coverage could be obtained when the laser-ranging sensors on each sensor ring were mounted in a tilted and biased form at certain angles. According to detection data and robot arm kinematics, a cooperative location model was also established to estimate the intervention position. Furthermore, we presented a self-occlusion geometric detection method for the robot, so as to identify cases in which the robot body entered the detection zone of the sensor ring pairs. To validate the efficacy of the proposed methods, experiments on quasi whole-body sensing, cooperative location, and self-occlusion detection were conducted with a UR10 robot. The experimental results show that the proposed sensor ring pair and detection method can effectively detect an approaching human and effectively estimate the intervention position and self-occlusion, allowing for efficient and safe cooperative performance with humans.

Optical Force/Tactile Sensors for Robotic Applications

Nowadays, robotic systems use tactile sensing as a key enabling technology to implement complex tasks. For example, manipulation and grasping problems strongly depend on the physical and geometrical characteristics of the objects; in fact, objects may be deformable or change their shape when in contact with the robot or the environment. For this reason, often, robots end effectors are equipped with sensorized fingers which can estimate the objects' features, forces, and contact locations. This is useful in a safe and efficient physical Human-Robot Interaction (pHRI) to perform cooperation and co-manipulation tasks while limiting damage from accidental impacts.

A Parametric Study of Compliant Link Design for Safe Physical Human–Robot Interaction

SUMMARYRobots of next-generation physically interact with the world rather than be caged in a controlled area, and they need to make contact with the open-ended environment to perform their task. Compliant robot links offer intrinsic mechanical compliance for addressing the safety issue for physical human–robot interactions (pHRI). However, many important research questions are yet to be answered. For instance, how do system parameters, for example, mechanical compliance, motor torque, impact velocities, and so on, affect the impact force? how to formulate system impact dynamics of compliant robots, and how to size their geometric dimensions to maximize impact force reduction. In this paper, we present a parametric study of compliant link (CL) design for safe pHRI. We first present a theoretical model of the pHRI system that is comprised of robot dynamics, an impact contact model, and dummy head dynamics. After experimentally validating the theoretical model, we then systematically study the effects of CL parameters on the impact force in more detail. Specifically, we explore how the design and actuation parameters affect the impact force of pHRI system. Based on the parametric studies of the CL design, we propose a step-by-step process and a list of concrete guidelines for designing CL with safety constraints in pHRI. We further conduct a simulation case study to validate this design process and design guidelines.

Design and Control of a Discrete Variable Stiffness Actuator With Instant Stiffness Switch for Safe Human-Robot Interaction

Variable Stiffness Actuators (VSA) have been proposed as an alternative actuation system for manipulators that are utilized for safe physical Human-Robot Interaction (pHRI). However, in the incidents of collision, the need of a fast response in stiffness tuning would rise to ensure safety. In this paper, we present a novel Discrete Variable Stiffness Actuator (DVSA) to be used in a compliant robotic manipulator for safe physical Human-Robot Interaction (pHRI). The novelty of this actuator lies in its design topology which allows the stiffness level to change swiftly among predefined levels without the need of complex stiffness tuning mechanism. Through this topology, three springs in parallel are connected serially between the motor and the link via gear train. The stiffness of the actuator is altered by adding/subtracting the number of involved springs, which can be realized through engagement/disengagement electromagnetic clutches on two of these spring’s shafts. The working principle, and the detailed design of the actuator are illustrated. Moreover, the stiffness model and the dynamic model are presented and discussed thoroughly. In order to validate these mathematical models and achieve optimal control, system identification for the dynamic parameters was performed experimentally on the physical model. Furthermore, the system’s ability of tracking desired trajectory was achieved through the implementation of different control techniques including PID (Proportional-Integral-Derivative), LQR (Linear Quadratic Regulator) and pole placement. The results show the high potential of utilizing the actuator in compliant manipulators. Moreover, DVSA is also characterized for safety in pHRI through Head-Injury Criterion (HIC). Finally, an application of DVSA in human augmentation task (Weight Bearing Task) is presented.

A Comparative Study on the Effect of Mechanical Compliance for a Safe Physical Human–Robot Interaction

Abstract In this paper, we study the effects of mechanical compliance on safety in physical human–robot interaction (pHRI). More specifically, we compare the effect of joint compliance and link compliance on the impact force assuming a contact occurred between a robot and a human head. We first establish pHRI system models that are composed of robot dynamics, an impact contact model, and head dynamics. These models are validated by Simscape simulation. By comparing impact results with a robotic arm made of a compliant link (CL) and compliant joint (CJ), we conclude that the CL design produces a smaller maximum impact force given the same lateral stiffness as well as other physical and geometric parameters. Furthermore, we compare the variable stiffness joint (VSJ) with the variable stiffness link (VSL) for various actuation parameters and design parameters. While decreasing stiffness of CJs cannot effectively reduce the maximum impact force, CL design is more effective in reducing impact force by varying the link stiffness. We conclude that the CL design potentially outperforms the CJ design in addressing safety in pHRI and can be used as a promising alternative solution to address the safety constraints in pHRI.

Implicit force control approach for Safe physical Robot-to-Human object handover

This research focuses on the development of the conceptual frameworks of human-human interaction applied for a robotic behaviour-based approach for safe physical human-robot interaction. The control has been constructed based on understanding the dynamic and kinematic behavioural characteristics of how two humans pass an object to each other. This has enabled a KR-16-KUKA robot to naturally interact with a human so as to facilitate the dexterous transfer of an object in an effective manner. Implicit force control based on Proportional Integral and Fuzzy Logic Control which allows the robot end effector’s trajectory to be moderated based on the applied force in real-time was adopted. The experimental results have confirmed that the quantitative performance of the force-controlled robot is close to that of the human and can be considered acceptable for human-robot interaction. Furthermore, the control based Fuzzy Logic Control was shown to be slightly superior performance compared to Proportional Integral control.

Estimation of human impedance and motion intention for constrained human–robot interaction

In this paper, a complete framework for safe and efficient physical human-robot interaction (pHRI) is developed for robot by considering both issues of adaptation to the human partner and ensuring the motion constraints during the interaction. We consider the robot's learning of not only human motion intention, but also the human impedance. We employ radial basis function neural networks (RBFNNs) to estimate human motion intention in real time, and least square method is utilized in robot learning of human impedance. When robot has learned the impedance information about human, it can adjust its desired impedance parameters by a simple tuning law for operative compliance. An adaptive impedance control integrated with RBFNNs and full-state constraints is also proposed in our work. We employ RBFNNs to compensate for uncertainties in the dynamics model of robot and barrier Lyapunov functions are chosen to ensure that full-state constraints are not violated in pHRI. Results in simulations and experiments show the better performance of our proposed framework compared with traditional methods.

Design and Modeling of a Continuously Tunable Stiffness Arm for Safe Physical Human–Robot Interaction

Abstract To reduce injury in physical human–robot interactions (pHRIs), a common practice is to introduce compliance to joints or arm of a robotic manipulator. In this paper, we present a robotic arm made of parallel guided beams whose stiffness can be continuously tuned by morphing the shape of the cross section through two four-bar linkages actuated by servo motors. An analytical lateral stiffness model is derived based on the pseudo-rigid-body model and validated by experiments. A physical prototype of a three-armed manipulator is built. Extensive stiffness and impact tests are conducted, and the results show that the stiffness of the robotic arm can be changed up to 3.6 times at a morphing angle of 37 deg. At an impact velocity of 2.2 m/s, the peak acceleration has a decrease of 19.4% and a 28.57% reduction of head injury criteria (HIC) when the arm is tuned from the high stiffness mode to the low stiffness mode. These preliminary results demonstrate the feasibility to reduce impact injury by introducing compliance into the robotic link and that the compliant link solution could be an alternative approach for addressing safety concerns of physical human–robot interactions.