Human–robot interaction (HRI) is a broad research topic, which is defined as understanding, designing, developing, and evaluating the robotic system to be used with or by humans. This paper presents a survey on the control, safety, and perspectives for HRI systems. The first part of this paper reviews the variable admittance (VA) control for human–robot co-manipulation tasks, where the virtual damping, inertia, or both are adjusted. An overview of the published research for the VA control approaches, their methods, the accomplished collaborative co-manipulation tasks and applications, and the criteria for evaluating them are presented and compared. Then, the performance of various VA controllers is compared and investigated. In the second part, the safety of HRI systems is discussed. The various methods for detection of human–robot collisions (model-based and data-based) are investigated and compared. Furthermore, the criteria, the main aspects, and the requirements for the determination of the collision and their thresholds are discussed. The performance measure and the effectiveness of each method are analyzed and compared. The third and final part of the paper discusses the perspectives, necessity, influences, and expectations of the HRI for future robotic systems.

SUMMARY: In this paper, a multilayer feedforward neural network (NN) is designed and trained, for human–robot collisions detection, using only the intrinsic joint position sensors of a manipulator. The topology of one NN is designed considering the coupled dynamics of the robot and trained, with and without external contacts, by Levenberg–Marquardt algorithm to detect unwanted collisions of the human operator with the manipulator and the link that is collided. The proposed approach could be applied to any industrial robot, where only the joint position signals are available. The designed NN is compared quantitatively and qualitatively with an NN, where both the intrinsic joint position and the torque sensors of the manipulator are used. The proposed method is evaluated experimentally with the KUKA LWR manipulator, which is considered as an example of the collaborative robots, using two of its joints in a planar horizontal motion. The results illustrate that the developed system is efficient and fast to detect the collisions and identify the collided link.

In apparel industries, the handling of fabrics still remains a manual work and its automation is a real challenge. In this paper, a three finger gripper is developed for grasping (pinching and clamping) fabrics under different ways. It is inspired by the human fingers movements in order to grasp a piece of fabric that is laid on a table. The conceptualization, the design and the prototype of the gripper are presented, along with the kinematic and static analysis of its mechanism. The proposed versatile gripper is based on a simple mechanism, where the two of the three fingers (pointer and middle) are underactuated by a tendon. Also, the plan for the sequence and the synchronization of the movements of the fingers is defined according to the grasping task. A prototype of the gripper is produced using 3D printing technique, which keeps the total cost very low. The prototype has been tested experimentally under several grasping tasks, where its agility is demonstrated.