Obstacle avoidance control of humanoid robot arm through tactile interaction

Tsetserukou, Dzmitry; Kawakami, Naoki; Tachi, Susumu

doi:10.1109/ichr.2008.4755981

Cited by 6 publications

(4 citation statements)

References 11 publications

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“…The group of the neurons uses a modified Widrow-Hoff learning rule [21] described in Eq. (6). The output of each neuron is computed as described in Eq.…”

Section: B Admittance Control Using Neural Learning and An Artificiamentioning

confidence: 99%

“…According to Albu-Schffer [1], robots without the sense of touch will remain far from the performance of human beings. For instance, it can be interesting to have robots capable of using their tactile information to self-calibrate the physical limits of their body [2], [3], [4], to prevent the risks of causing of its physical damage by an obstacle [5], [6] or of causing bodily harm to human using the safety-rated information on contact [7]. It is also advantageous to have robots that comply to an imposed direction that a person wants the robot to follow.…”

Section: Introductionmentioning

confidence: 99%

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Touch-based admittance control of a robotic arm using neural learning of an artificial skin

Pugach

Melnyk

Tolochko

et al. 2016

2016 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS)

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Touch perception is an important sense to model in humanoid robots to interact physically and socially with humans. We present a neural controller that can adapt the compliance of the robot arm in four directions using as input the tactile information from an artificial skin and as output the estimated torque for admittance control-loop reference. This adaption is done in a self-organized fashion with a neural system that learns first the topology of the tactile map when we touch it and associates a torque vector to move the arm in the corresponding direction. The artificial skin is based on a large area piezoresistive tactile device (ungridded) that changes its electrical properties in the presence of the contact. Our results show the self-calibration of a robotic arm (2 degrees of freedom) controlled in the four directions and derived combination vectors, by the soft touch on all the tactile surface, even when the torque is not detectable (force applied near the joint). The neural system associates each tactile receptive field with one direction and the correct force. We show that the tactile-motor learning gives better interactive experiments than the admittance control of the robotic arm only. Our method can be used in the future for humanoid adaptive interaction with a human partner.

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“…The group of the neurons uses a modified Widrow-Hoff learning rule [21] described in Eq. (6). The output of each neuron is computed as described in Eq.…”

Section: B Admittance Control Using Neural Learning and An Artificiamentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Touch-based admittance control of a robotic arm using neural learning of an artificial skin

Pugach

Melnyk

Tolochko

et al. 2016

2016 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS)

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“…In [6], tactile interaction was introduced to obstacle avoidance of a humanoid robot arm in which optical torque sensors were applied on each robot joint. Thanks to these sensors, the control system was able to follow the object outline smoothly until the robot reached the target position.…”

Section: Introductionmentioning

confidence: 99%

Reinforcement Learning-Based Algorithm to Avoid Obstacles by the Anthropomorphic Robotic Arm

Lindner

Milecki

2022

Applied Sciences

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In this paper, the application of the policy gradient Reinforcement Learning-based (RL) method for obstacle avoidance is proposed. This method was successfully used to control the movements of a robot using trial-and-error interactions with its environment. In this paper, an approach based on a Deep Deterministic Policy Gradient (DDPG) algorithm combined with a Hindsight Experience Replay (HER) algorithm for avoiding obstacles has been investigated. In order to ensure that the robot avoids obstacles and reaches the desired position as quickly and as accurately as possible, a special approach to the training and architecture of two RL agents working simultaneously was proposed. The implementation of this RL-based approach was first implemented in a simulation environment, which was used to control the 6-axis robot simulation model. Then, the same algorithm was used to control a real 6-DOF (degrees of freedom) robot. The results obtained in the simulation were compared with results obtained in laboratory conditions.

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“…For example, force-torque sensors have been used at the end-effector [2], or at the base [3]. In [4], an obstacle avoidance control through tactile perception using optical torque sensors was introduced. However, the interaction with the environment is limited due to the contact of the robotic arm with obstacles.…”

Section: Introductionmentioning

confidence: 99%

CobotGear: Interaction with Collaborative Robots using Wearable Optical Motion Capturing Systems

Heredia

Cabrera

Tirado

et al. 2020

2020 IEEE 16th International Conference on Automation Science and Engineering (CASE)

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In industrial applications, complex tasks require human collaboration since the robot doesn't have enough dexterity. However, the robots are still implemented as tools and not as collaborative intelligent systems. To ensure safety in the human-robot collaboration, we introduce a system that presents a new method that integrates low-cost wearable mocap, and an improved collision avoidance algorithm based on the artificial potential fields. Wearable optical motion capturing allows to track the human hand position with high accuracy and low latency on large working areas. To increase the efficiency of the proposed algorithm, two obstacle types are discriminated according to their collision probability. A preliminary experiment was performed to analyze the algorithm behavior and to select the best values for the obstacle's threshold angle θOBS, and for the avoidance threshold distance dAT . The second experiment was carried out to evaluate the system performance with dAT = 0.2 m and θOBS = 45 degrees. The third experiment evaluated the system in a real collaborative task. The results demonstrate the robust performance of the robotic arm generating smooth collision-free trajectories. The proposed technology will allow consumer robots to safely collaborate with humans in cluttered environments, e.g., factories, kitchens, living rooms, and restaurants.

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Obstacle avoidance control of humanoid robot arm through tactile interaction

Cited by 6 publications

References 11 publications

Touch-based admittance control of a robotic arm using neural learning of an artificial skin

Touch-based admittance control of a robotic arm using neural learning of an artificial skin

Reinforcement Learning-Based Algorithm to Avoid Obstacles by the Anthropomorphic Robotic Arm

CobotGear: Interaction with Collaborative Robots using Wearable Optical Motion Capturing Systems

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