Dynamic and Interactive Path Planning and Collision Avoidance for an Industrial Robot Using Artificial Potential Field Based Method

Csiszar, Akos; Drust, Manuel; Dietz, Thomas; Verl, Alexander; Brişan, Cornel

doi:10.1007/978-3-642-23244-2_50

Cited by 13 publications

(11 citation statements)

References 6 publications

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“…To illustrate the priority of the proposed scheme, a group of comparisons are carried out. As shown in Table 1, all the controllers in Zhang and Wang (2004); Csiszar et al (2011); Guo and Zhang (2012); Krzysztof and Joanna (2016) achieve the avoidance of obstacles. Comparing to APF method in Csiszar et al (2011); Krzysztof and Joanna (2016) of JP based method in Csiszar et al (2011); Krzysztof and Joanna (2016), the proposed controller can realize a secondary task, at the same time, we present a more general formulation of the obstacle avoidance strategy, which is helpful to gain a deeper understanding of the mechanism for avoidance of obstacles.…”

Section: Numerical Resultsmentioning

confidence: 99%

“…At the same time, artificial potential field methods have shown great ability in tracking dynamic targets as well as avoiding dynamic obstacles. In Csiszar et al (2011), a modified method is proposed, which describes the obstacles by different geometrical forms, both theoretical conduction and experimental tests validate the proposed method. Considering the local minimum problem that may be caused by multi-link structures, in Badawy (2016), a two minima is introduced to construct potential field, such that a dual attraction between links enables faster maneuvers comparing with traditional methods.…”

Section: Introductionmentioning

confidence: 91%

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Deep Recurrent Neural Networks Based Obstacle Avoidance Control for Redundant Manipulators

Zhou

2019

Front. Neurorobot.

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Obstacle avoidance is an important subject in the control of robot manipulators, but is remains challenging for robots with redundant degrees of freedom, especially when there exist complex physical constraints. In this paper, we propose a novel controller based on deep recurrent neural networks. By abstracting robots and obstacles into critical point sets respectively, the distance between the robot and obstacles can be described in a simpler way, then the obstacle avoidance strategy is established in form of inequality constraints by general class-K functions. Using minimal-velocity-norm (MVN) scheme, the control problem is formulated as a quadratic-programming case under multiple constraints. Then a deep recurrent neural network considering system models is established to solve the QP problem online. Theoretical conduction and numerical simulations show that the controller is capable of avoiding static or dynamic obstacles, while tracking the predefined trajectories under physical constraints.

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Section: Numerical Resultsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 91%

Deep Recurrent Neural Networks Based Obstacle Avoidance Control for Redundant Manipulators

Zhou

2019

Front. Neurorobot.

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“…When sharing workspaces the CPS need to be aware of each other's positions, motions and actions. This could be realized by sensors on the CPS [15]. Another way would be to have each CPS communicate all that information of their own state and action to the other CPS sharing their workspace.…”

Section: Shared Workpacesmentioning

confidence: 99%

Applications Enabled by a Deterministic Wireless Communication System Such as Industrial LTE

2017

Proceedings of 2017 the 7th International Workshop on Computer Science and Engineering

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Current industrial communication systems rely heavily on communication cables to transmit information and control signals. Wireless networks are emerging on the factory level with non-real-time requirements. The needed cable for deterministic real-time communication hinders the development and deployment of various applications in the production environment. Some of these applications might be an integral part in solving the challenges of future production. Along with a description of a wireless communication system, various examples for enabled applications are presented in this paper.

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“…Among the others, the Artificial Potential Field approach [16] is the most famous and feasible method for collisions avoidance. The idea comes from permanent magnet: obstacle generates a repulsive pressure and the target an attractive pressure on all the surface of the robot.…”

Section: B Collision Avoidance Algorithmmentioning

confidence: 99%

Safer hybrid workspace using human-robot interaction while sharing production activities

Meziane

Otis

et al. 2014

2014 IEEE International Symposium on Robotic and Sensors Environments (ROSE) Proceedings

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Abstract-In a near future, human and industrial manipulator will work together sharing a common workspace and production activities leading to a potential increase of accident. The research project concerns the adaptation of industrial robot already installed in a flexible manufacturing system in order to make it more interactive with human. The aim concerns the reduction of potential risk of injuries while working with an industrial robot. This paper presents a new inexpensive, non-intrusive, non-invasive, and non-vision-based system, for human detection and collision avoidance. One method investigated for improving safety concerns planning of safe path. This system recognizes human activities and locates operator's position in real time through an instrumented safety helmet. This safety helmet includes an IMU (Inertial Measurement Unit) and an indoor localization system such as RSSI (Received Signal Strength Indication) using industrial wireless equipment. A hybrid workspace including a flexible manufacturing system has been designed in order to practice experiments in an industrial-like environment.

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Dynamic and Interactive Path Planning and Collision Avoidance for an Industrial Robot Using Artificial Potential Field Based Method

Cited by 13 publications

References 6 publications

Deep Recurrent Neural Networks Based Obstacle Avoidance Control for Redundant Manipulators

Deep Recurrent Neural Networks Based Obstacle Avoidance Control for Redundant Manipulators

Applications Enabled by a Deterministic Wireless Communication System Such as Industrial LTE

Safer hybrid workspace using human-robot interaction while sharing production activities

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