Human robot interaction (HRI) tasks requires robots to have safe sharing of work space and to demonstrate adaptable compliant behavior enabling eminent collision safety as well as maintaining high position accuracy. Robot compliance control normally can be achieved by using active compliance control of actuators based on various sensor data. Alternatively, passive devices allow controllable compliance motion but usually are mechanically complex. We proposed a unique method using semi-active compliant actuation mechanism having magneto-rheological (MR) fluid based actutaor that introduces reconfigurable compliance characteristics into the robot joints. This enables high intrinsic safety coming from fluid mechanics as well as, it offers simpler interaction control strategy compared to other concurrent approaches. In this studies, we have described three essential modes of motions required for physical human system interaction. Furthermore, we have demonstarted robot collision safety in terms of static collision and experimentally validates the performance of robot manipulator enabling safe human robot interaction.

Semi-active compliant robot enabling collision safety for human robot interaction

Preliminary Investigation of an Innovative Digital Motion Analysis Device for Badminton Athlete Performance Evaluation

This paper investigates the stability and performance of distributed multirate control of direct touch in networked haptic systems that provide users with a remote dynamic proxy of their peer and w...

On Distributed Multirate Control of Direct User-to-User Touch in Networked Haptic Systems with Passive Wave-Domain Communications


 Mechanism with few degrees-of-freedom is an economical solution for rehabilitation robots because it does not need a complicated structure and control system. To provide motion data to design the robot with few degrees-of-freedom, a user motion data acquisition, and processing method on the mobile platform is proposed. The data from the monocular vision system have been used on the smartphone and the KCF algorithm to track the knee and hip joints motion. To increase the stability and the precision, the maker with texture is designed to carry out the tracking. Furthermore, the transformation from the image coordinate to the world coordinate is implemented by the cross-ratio invariant in the controlled tracking condition with fixed dimension makers. By this mean, the motion path in the image coordinate is converted to that in the world coordinate. With the low limb motion path that provides the input of the Fourier descriptor, the mechanical parameters of the robot with few degrees-of-freedom are calculated, and the 3D models of the mechanism are constructed. Finally, the prototype of the rehabilitation robot is established which can assist the users to stand from sitting posture, or work as a wheelchair with obstacle avoidance function by infrared and ultrasonic sensors.

A User Motion Data Acquisition and Processing Method for the Design of Rehabilitation Robot With Few Degrees-of-Freedom

Problem statement: Human ping-pong players determine the stroke trajectory according to their experience before the ball enters their court  However, to enable a humanoid robot to select the appropriate stroke motion based on skills learned f rom 3D motion, important patterns must be generated to simplify the complex 3D motion Approach: This study developed an effective strategy for teaching ping-pong skills to a humanoid robot An optical/inertial motion-capture system that retrieves the stroke motion was constructed, along with the retrieved stroke motion trajectories analyzed to obtain the desired stroke patterns of t he robot Results: A motion capture system was implemented mainly to orient the robot on the strok e motion trajectory This system was applied directly to a ping-pong game between a human player and a pitching machine to enable the robot to learn backhand strokes through human demonstration The ball was continuously struck to the opponent so that it hit the anticipated region on t he opposite side of the court while the pitching machine served the ball The data were then classif ied using the proposed stopping detector and then processed by Principal Components Analysis (PCA) to generate the stroke patterns after collecting 50 datasets for stroke trajectories Conclusion: The right arm of the humanoid robot was successfull y instructed to perform the actual ping-pong stroke u sing the generated trajectory

Stroke Motion Learning for a Humanoid Robotic Ping-Pong Player Using a Novel Motion Capture System

In this paper we show how a motion capture system and feedback mechanism can be integrated into a virtual ping- pong game to create a multi-player platform. To trace the motion of each player, optical markers are attached to different places on each player's paddle. For tactile feedback, we designed a controller for a DC (Direct Current) motor, which is also attached to the paddle. This controller communicates with the game server through wireless Bluetooth technology. When the game server detects a collision between the paddle and ball, the controller receives the message from the game server and then triggers one of the respective paddle's DC motors to vibrate depending on the position of the impact on the paddle. During an exhibition many people positively responded to the game.

/pdf/multi-player-virtual-ping-pong-game-3zveezl3ze.pdf

Seung-Hoon Han

Papers

Multi-Player Virtual Ping-Pong Game