Dynamic Collision Avoidance of Industrial Cooperating Robots Using Virtual Force Fields

This paper studies learning of reaching movements in a dynamically variable virtual environment specially designed for this purpose. Learning of reaching movements in the physical world has been extensively studied by several researchers. In most of those studies, the subjects are asked to exercise reaching movements while being exposed to real force fields exerted through a robotic manipulandum. Those studies have contributed to our understanding of the mechanisms used by the human cognitive system to learn reaching movements in the physical world. The question that remains to be answered is how the learning mechanism in the physical world relates to its counterpart in the virtual world where the real force fields are replaced by virtual force fields. A limited number of studies have already addressed this question and have shown that there are, actually, quite a number of relationships between the learning mechanisms in these two different environments. In this study, we are focused on gaining a more in-depth understanding of these relationships. In our experiments, the subjects are asked to guide a virtual object to a desired target on a computer screen using a mouse. The movement of the virtual object is affected by a viscous virtual force field that is sensed by the examinees through their visual system. Three groups of examinees are used for the experiments. All the examinees are first trained in the null-field condition. Then, the viscous force field is introduced either suddenly (for the two first groups) or gradually (for the last group). While the first and third groups of the examinees used their dominant arm to guide the virtual object in the second step, the second group used their nondominant arm. Generalization of the learning from the dominant to the nondominant arm and vice versa was studied in the third phase of the experiments. Finally, the force field was removed and the examinees were asked to repeat the reaching task to study the so-called aftereffects phenomenon. The results of the experiments are compared with the studies performed in the physical world. It is shown that the trends of learning and generalization are similar to what is observed in the physical world for a sudden application of the virtual force field. However, the generalization behavior of the examinees is somewhat different from the physical world if the force field is gradually applied.

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REACTIVE SELF COLLISION AVOIDANCE WITH DYNAMIC TASK PRIORITIZATION FOR HUMANOID ROBOTS

International Journal of Humanoid Robotics ◽

10.1142/s0219843610001976 ◽

2010 ◽

Vol 07 (01) ◽

pp. 31-54 ◽

Cited By ~ 8

Author(s):

HISASHI SUGIURA ◽

MICHAEL GIENGER ◽

HERBERT JANßEN ◽

CHRISTIAN GOERICK

Keyword(s):

Collision Avoidance ◽

Degrees Of Freedom ◽

Position Control ◽

Body Schema ◽

Humanoid Robots ◽

The Body ◽

Reactive System ◽

Dynamic Task ◽

Virtual Force ◽

Task Prioritization

We propose a self collision avoidance system for humanoid robots designed for interacting with the real world. It protects not only the humanoid robots' hardware but also expands its working range while keeping smooth motions. It runs in real-time in order to handle unpredictable reactive tasks such as reaching to moving targets tracked by vision during dynamic motions like e.g. biped walking. The collision avoidance is composed of two important elements. The first element is reactive self collision avoidance which controls critical segments in only one direction — as opposed to other methods which use 3D position control. The virtual force for the collision avoidance is applied to this direction and therefore the system has more redundant degrees of freedom which can be used for other criteria. The other second element is a dynamic task prioritization scheme which blends the priority between target reaching and collision avoidance motions in a simple way. The priority between the two controllers is changed depending on current risk. We test the algorithm on our humanoid robot ASIMO and works while the robot is standing and walking. Reaching motions from the front to the side of the body without the arm colliding with the body are possible. Even if the target is inside the body, the arm stops at the closest point to the target outside the body. The collision avoidance is working as one module of a hierarchical reactive system and realizes reactive motions. The proposed scheme can be used for other applications: We also apply it to realizing a body schema and occlusion avoidance.

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Potential flow field navigation with virtual force field for UAS collision avoidance

2016 International Conference on Unmanned Aircraft Systems (ICUAS) ◽

10.1109/icuas.2016.7502641 ◽

2016 ◽

Cited By ~ 2

Author(s):

Edward Burgos ◽

Subodh Bhandari

Keyword(s):

Flow Field ◽

Force Field ◽

Collision Avoidance ◽

Potential Flow ◽

Virtual Force

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Collision avoidance and task planning using impedance force fields

10.1117/12.369276 ◽

1999 ◽

Author(s):

Greg R. Luecke ◽

Kok-Leong Tan ◽

Naci Zafer

Keyword(s):

Collision Avoidance ◽

Force Fields ◽

Task Planning ◽

And Task

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Using haptic feedbacks for obstacle avoidance in helicopter flight

Progress in Flight Dynamics, Guidance, Navigation, and Control – Volume 10 ◽

10.1051/eucass/201810047 ◽

2018 ◽

Author(s):

L. Binet ◽

T. Rakotomamonjy

Keyword(s):

Obstacle Avoidance ◽

Haptic Feedback ◽

Force Fields ◽

Geometric Approach ◽

Simulation Environment ◽

Avoidance Procedure ◽

Helicopter Flight ◽

Virtual Force ◽

Congested Area ◽

Rotary Wing

An obstacle avoidance function based on haptic feedback has been developed and tested on a simulation environment at ONERA. The objective was to calculate and provide effcient haptic feedback through active (motorized) sidesticks for the piloting task of a rotary wing (RW) aircraft, in the vicinity of visible and known obstacles, corresponding to emergency avoidance procedure, or navigation in a congested area. Two different methods have been designed to generate the force bias based on virtual force fields (VFF) surrounding obstacles and on a geometric approach (GA) combined with T-theory, respectively. Piloted simulations were performed in order to evaluate the benefits for obstacle avoidance.

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A segmentation algorithm for collision avoidance in telerobotics applications

Robotica ◽

10.1017/s0263574700018476 ◽

1996 ◽

Vol 14 (6) ◽

pp. 603-610

Author(s):

Stephen Strenn ◽

T.C. Hsia ◽

Karl Wilhelmsen

Keyword(s):

Real Time ◽

Collision Avoidance ◽

Segmentation Algorithm ◽

Brute Force ◽

Hierarchical Segmentation ◽

System Parameters ◽

Virtual Force ◽

On Line ◽

Simulation Results ◽

Force Reflection

This paper proposes a new algorithm, known as the Segmentation Algorithm, which provides model-based, real-time, whole-arm collision avoidance for telerobotic applications. The work presented here is an extension and modification of potential field theory. Novel aspects of the algorithm include the application of a hierarchical segmentation technique to minimize on-line processing and the development of procedures which account for workspace object translation, rotation, and grasping. The'SA outputs torques, which, when applied to the control arm, prevent the teleoperator from driving the remote arm into a collision. The teleoperator actually feels workspace objects that are spatially close to the remote arm—an experience known as virtual force-reflection. The SA's performance has been analyzed in terms of its speed and efficiency vis a vis various system parameters, including workspace object distribution, size, and number. Simulation results show that the SA succeeds in providing real-time collision avoidance where less elegant brute force algorithms fail.

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The effect of force feedback delay on stiffness perception and grip force modulation during tool-mediated interaction with elastic force fields

Journal of Neurophysiology ◽

10.1152/jn.00229.2014 ◽

2015 ◽

Vol 113 (9) ◽

pp. 3076-3089 ◽

Cited By ~ 20

Author(s):

Raz Leib ◽

Amir Karniel ◽

Ilana Nisky

Keyword(s):

Grip Force ◽

Force Feedback ◽

Force Fields ◽

Precision Grip ◽

Internal Representation ◽

Force Sensor ◽

Elastic Force ◽

Load Force ◽

Perceptual Task ◽

Virtual Force

During interaction with objects, we form an internal representation of their mechanical properties. This representation is used for perception and for guiding actions, such as in precision grip, where grip force is modulated with the predicted load forces. In this study, we explored the relationship between grip force adjustment and perception of stiffness during interaction with linear elastic force fields. In a forced-choice paradigm, participants probed pairs of virtual force fields while grasping a force sensor that was attached to a haptic device. For each pair, they were asked which field had higher level of stiffness. In half of the pairs, the force feedback of one of the fields was delayed. Participants underestimated the stiffness of the delayed field relatively to the nondelayed, but their grip force characteristics were similar in both conditions. We analyzed the magnitude of the grip force and the lag between the grip force and the load force in the exploratory probing movements within each trial. Right before answering which force field had higher level of stiffness, both magnitude and lag were similar between delayed and nondelayed force fields. These results suggest that an accurate internal representation of environment stiffness and time delay was used for adjusting the grip force. However, this representation did not help in eliminating the bias in stiffness perception. We argue that during performance of a perceptual task that is based on proprioceptive feedback, separate neural mechanisms are responsible for perception and action-related computations in the brain.

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