Comparison of trajectory generation methods for a human-robot interface based on motion tracking in the Int2Bot

• Published in: The 23rd IEEE International Symposium on Robot and Human Interactive Communication pp. 710 - 715
• Main Authors: Schultje, F., Beckerle, P., Grimmer, M., Wojtusch, J., Rinderknecht, S.
• Format: Conference Proceeding
• Language: English
• Published: IEEE 01.08.2014
• Subjects: Joints; Kalman filters; Kinematics; Robot sensing systems; Trajectory; Vectors
• ISBN: 9781479967636; 1479967637
• ISSN: 1944-9445; 1944-9437
• DOI: 10.1109/ROMAN.2014.6926336

The acceptance of artificial devices like prostheses or other wearable robots requires their integration into the body schemas of the users. Different factors induce, influence and support the integration and acceptance of the device that substitutes or augments a part of the body. Previous studies have shown that the inducing and maintaining factors are visual, tactile and proprioceptive informations as well as their multi-sensory integration. This paper describes the vision-based part of the human-robot interface in the Int 2 Bot, which is a robot for the investigation of lower limb body schema integration during postural movements. The psychological approach and the technical setup of the robot, which is designed to imitate postural movements in the sagittal plane to imitate the human subject while performing squats, are outlined. To realize the imitation, an RGB-D sensor, in form of a Microsoft Kinect, is used to capture the subjects motions without contact and thereby avoid disturbances of body schema integration. For generation of the desired joint trajectories to be tracked by the control algorithm, different methods like an extended Kalman filter, inverse kinematics, an inverse kinematics algorithm using Jacobian transpose and approaches based on kinematic assumptions are presented, evaluated and compared based on human data. Benchmarking the results with data acquired using a professional motion capturing system shows that best overall joint angle estimations are achieved with the extended Kalman filter. Finally, the practical implementation within the robot is presented and the tracking behavior using the trajectories generated with the extended Kalman filter are analyzed.