Requirements for effective collision detection on industrial serial manipulators. Schroeder, K. A. Ph.D. Thesis, The University of Texas at Austin, August, 2013.
Requirements for effective collision detection on industrial serial manipulators [link]Paper  abstract   bibtex   
Human-robot interaction (HRI) is the future of robotics. It is essential in the expanding markets, such as surgical, medical, and therapy robots. However, existing industrial systems can also benefit from safe and effective HRI. Many robots are now being fitted with joint torque sensors to enable effective human-robot collision detection. Many existing and off-the-shelf industrial robotic systems are not equipped with these sensors. This work presents and demonstrates a method for effective collision detection on a system with motor current feedback instead of joint torque sensors. The effectiveness of this system is also evaluated by simulating collisions with human hands and arms. Joint torques are estimated from the input motor currents. The joint friction and hysteresis losses are estimated for each joint of an SIA5D 7 Degree of Freedom (DOF) manipulator. The estimated joint torques are validated by comparing to joint torques predicted by the recursive application of Newton-Euler equations. During a pick and place motion, the estimation error in joint 2 is less than 10 Newton meters. Acceleration increased the estimation uncertainty resulting in estimation errors of 20 Newton meters over the entire workspace. When the manipulator makes contact with the environment or a human, the same technique can be used to estimate contact torques from motor current. Current-estimated contact torque is validated against the calculated torque due to a measured force. The error in contact force is less than 10 Newtons. Collision detection is demonstrated on the SIA5D using estimated joint torques. The effectiveness of the collision detection is explored through simulated collisions with the human hands and arms. Simulated collisions are performed both for a typical pick and place motion as well as trajectories that transverse the entire workspace. The simulated forces and pressures are compared to acceptable maximums for human hands and arms. During pick and place motions with vertical and lateral end effector motions at 10mm/s and 25mm/s, the maximum forces and pressures remained below acceptable levels. At and near singular configurations some collisions can be difficult to detect. Fortunately, these configurations are generally avoided for kinematic reasons.
@phdthesis{schroeder_requirements_2013,
	type = {Dissertation},
	title = {Requirements for effective collision detection on industrial serial manipulators},
	url = {https://repositories.lib.utexas.edu/handle/2152/21585},
	abstract = {Human-robot interaction (HRI) is the future of robotics.  It is essential in the expanding markets, such as surgical, medical, and therapy robots.  However, existing industrial systems can also benefit from safe and effective HRI.  Many robots are now being fitted with joint torque sensors to enable effective human-robot collision detection.  Many existing and off-the-shelf industrial robotic systems are not equipped with these sensors.  This work presents and demonstrates a method for effective collision detection on a system with motor current feedback instead of joint torque sensors.  The effectiveness of this system is also evaluated by simulating collisions with human hands and arms. Joint torques are estimated from the input motor currents.  The joint friction and hysteresis losses are estimated for each joint of an SIA5D 7 Degree of Freedom (DOF) manipulator.  The estimated joint torques are validated by comparing to joint torques predicted by the recursive application of Newton-Euler equations.  During a pick and place motion, the estimation error in joint 2 is less than 10 Newton meters.  Acceleration increased the estimation uncertainty resulting in estimation errors of 20 Newton meters over the entire workspace. When the manipulator makes contact with the environment or a human, the same technique can be used to estimate contact torques from motor current.  Current-estimated contact torque is validated against the calculated torque due to a measured force.  The error in contact force is less than 10 Newtons.  Collision detection is demonstrated on the SIA5D using estimated joint torques. The effectiveness of the collision detection is explored through simulated collisions with the human hands and arms.  Simulated collisions are performed both for a typical pick and place motion as well as trajectories that transverse the entire workspace.  The simulated forces and pressures are compared to acceptable maximums for human hands and arms.  During pick and place motions with vertical and lateral end effector motions at 10mm/s and 25mm/s, the maximum forces and pressures remained below acceptable levels.  At and near singular configurations some collisions can be difficult to detect.  Fortunately, these configurations are generally avoided for kinematic reasons.},
	language = {en\_US},
	urldate = {2017-11-12},
	school = {The University of Texas at Austin},
	author = {Schroeder, Kyle Anthony},
	month = aug,
	year = {2013},
	keywords = {Dissertation},
}

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