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.

In recent years, robotic sorting is widely used in the industry, which is driven by necessity and opportunity. In this paper, a novel neuromorphic vision-based tactile sensing approach for robotic sorting application is proposed. This approach has low latency and low power consumption when compared to conventional vision-based tactile sensing techniques. Two Machine Learning (ML) methods, namely, Support Vector Machine (SVM) and Dynamic Time Warping-K Nearest Neighbor (DTW-KNN), are developed to classify material hardness, object size, and grasping force. An Event-Based Object Grasping (EBOG) experimental setup is developed to acquire datasets, where 243 experiments are produced to train the proposed classifiers. Based on predictions of the classifiers, objects can be automatically sorted. If the prediction accuracy is below a certain threshold, the gripper re-adjusts and re-grasps until reaching a proper grasp. The proposed ML method achieves good prediction accuracy, which shows the effectiveness and the applicability of the proposed approach. The experimental results show that the developed SVM model outperforms the DTW-KNN model in term of accuracy and efficiency for real time contact-level classification.

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.

Loss of upper extremity motor control and function is an unremitting symptom in post-stroke patients. This would impose hardships on accomplishing their daily life activities. Supernumerary robotic limbs (SRLs) were introduced as a solution to regain the lost Degrees of Freedom (DoFs) by introducing an independent new limb. The actuation systems in SRL can be categorized into rigid and soft actuators. Soft actuators have proven advantageous over their rigid counterparts through intrinsic safety, cost, and energy efficiency. However, they suffer from low stiffness, which jeopardizes their accuracy. Variable Stiffness Actuators (VSAs) are newly developed technologies that have been proven to ensure accuracy and safety. In this paper, we introduce the novel Supernumerary Robotic Limb based on Variable Stiffness Actuators. Based on our knowledge, the proposed proof-of-concept SRL is the first that utilizes Variable Stiffness Actuators. The developed SRL would assist post-stroke patients in bi-manual tasks, e.g., eating with a fork and knife. The modeling, design, and realization of the system are illustrated. The proposed SRL was evaluated and verified for its accuracy via predefined trajectories. The safety was verified by utilizing the momentum observer for collision detection, and several post-collision reaction strategies were evaluated through the Soft Tissue Injury Test. The assistance process is qualitatively verified through standard user-satisfaction questionnaire.

This article addresses the crucial aspect of safety in collaborative robotics by introducing a new continuous wavelet transform-convolutional neural network (CWT-CNN) for efficient robot collision detection. Unlike conventional methods, CWT-CNN exhibits superior data efficiency, requiring minimal collision data for robust training without relying on a dynamic model. The network’s adaptability extends to varying internal stiffness levels, offering robustness to changes in robotic system characteristics. Through comprehensive experimental studies, we investigate the impact of input signal types, wavelet types, wavelet scale ranges, and time-moving window sizes on collision detection performance, offering critical insights for optimal CWT parameter selection. Additionally, our transferability analysis demonstrates that the CWT-CNN can seamlessly adapt from one joint to another, requiring only minimal free-motion data from the new joint. This adaptability is validated through extensive experiments on an industrial robot and the robot equipped with variable stiffness actuators. In conclusion, the CWT-CNN is highly generalizable and data-efficient, making it a reliable solution for real-time collision detection in human-robot interactions, addressing a key aspect of safety in collaborative environments.