An approach to integrate human motion prediction into local obstacle avoidance in close human-robot collaboration

Dinh, Khoi Hoang; Oguz, Ozgur S.; Huber, Gerold; Gabler, Volker; Wollherr, Dirk

doi:10.1109/arso.2015.7428221

Cited by 19 publications

(9 citation statements)

References 19 publications

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“…Therefore, a cobot should be enhanced with additional intelligence and perception abilities to be fully safe. A lot of work has been done on collision avoidance [32,37,40,41], human motion prediction [42,43], risk assessment through simulation and VR [44] and other safety enabling technologies. However, what might be holding back the implementation of collaborative systems, in terms of safety, is that many of the safety enabling systems have not been officially certified.…”

Section: Safety Measures For Hrcmentioning

confidence: 99%

Cobot programming for collaborative industrial tasks: An overview

Zaatari

Marei

et al. 2019

Robotics and Autonomous Systems

332

147

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Section: Safety Measures For Hrcmentioning

confidence: 99%

Cobot programming for collaborative industrial tasks: An overview

Zaatari

Marei

et al. 2019

Robotics and Autonomous Systems

332

147

View full text Add to dashboard Cite

“…In this aspect, potential field (Khatib, 1985 ) is a very popular and widely used approach due to its simplicity and real-time capability. Flacco et al ( 2012 ) and Dinh et al ( 2015 ) utilize the potential field idea in their works to provide obstacle avoidance behavior on the end-effector of an articulated robot. In the work of Park et al ( 2008 ), the authors introduce the dynamic potential field to adapt robot trajectories while avoiding obstacles in mid-motion.…”

Section: Related Workmentioning

confidence: 99%

“…Whenever an obstacle is inside a threshold region of the end-effector, a repulsive force vector F ext according to (12) is generated. Here, we use the same idea of repulsive vectors (Flacco et al, 2012 ; Dinh et al, 2015 ) to generate a smooth reaction force…”

Section: Legible Motion Framework In Human Robot Interaction In CLmentioning

confidence: 99%

Adaptation and Transfer of Robot Motion Policies for Close Proximity Human-Robot Interaction

et al. 2019

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In the context of human-robot collaboration in close proximity, safety and comfort are the two important aspects to achieve joint tasks efficiently. For safety, the robot must be able to avoid dynamic obstacles such as a human arm with high reliability. For comfort, the trajectories and avoidance behavior of the robot need to be predictable to the humans. Moreover, these two aspects might be different from person to person or from one task to another. This work presents a framework to generate predictable motions with dynamic obstacle avoidance for the robot interacting with the human by using policy improvement method. The trajectories are generated using Dynamic Motion Primitives with an additional potential field term that penalizes trajectories that may lead to collisions with obstacles. Furthermore, human movements are predicted using a data-driven approach for proactive avoidance. A cost function is defined which measures different aspects that affect the comfort and predictability of human co-workers (e.g., human response time, joint jerk). This cost function is then minimized during human-robot interaction by the means of policy improvement through black-box optimization to generate robot trajectories that adapt to human preferences and avoid obstacles. User studies are performed to evaluate the trust and comfort of human co-workers when working with the robot. In addition, the studies are also extended to various scenarios and different users to analyze the task transferability. This improves the learning performance when switching to a new task or the robot has to adapt to a different co-worker.

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“…Considering how swiftly two humans work together in a confined workspace, the challenges for a human–robot team become obvious; the robot has to take into account human partner’s intention and movement in order to control its own motion for achieving effective cooperative task executions. In essence, early prediction of the human motion allows an immediate initiation of the replanning process and an early adaptation of the robot motion (Dinh et al, 2015 ; Gabler et al, 2017 ; Oguz et al, 2017 ). Therefore, the ability of understanding and predicting human motions effectively is the key to achieving swift close human–robot collaboration.…”

Section: Introductionmentioning

confidence: 99%

A Hybrid Framework for Understanding and Predicting Human Reaching Motions

2018

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Robots collaborating naturally with a human partner in a confined workspace need to understand and predict human motions. For understanding, a model-based approach is required as the human motor control system relies on the biomechanical properties to control and execute actions. The model-based control models explain human motions descriptively, which in turn enables predicting and analyzing human movement behaviors. In motor control, reaching motions are framed as an optimization problem. However, different optimality criteria predict disparate motion behavior. Therefore, the inverse problem-finding the optimality criterion from a given arm motion trajectory-is not unique. This paper implements an inverse optimal control (IOC) approach to determine the combination of cost functions that governs a motion execution. The results indicate that reaching motions depend on a trade-off between kinematics and dynamics related cost functions. However, the computational efficiency is not sufficient for online prediction to be utilized for HRI. In order to predict human reaching motions with high efficiency and accuracy, we combine the IOC approach with a probabilistic movement primitives formulation. This hybrid model allows an online-capable prediction while taking into account motor variability and the interpersonal differences. The proposed framework affords a descriptive and a generative model of human reaching motions which can be effectively utilized online for human-in-the-loop robot control and task execution.

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An approach to integrate human motion prediction into local obstacle avoidance in close human-robot collaboration

Cited by 19 publications

References 19 publications

Cobot programming for collaborative industrial tasks: An overview

Cobot programming for collaborative industrial tasks: An overview

Adaptation and Transfer of Robot Motion Policies for Close Proximity Human-Robot Interaction

A Hybrid Framework for Understanding and Predicting Human Reaching Motions

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