The dynamic wave expansion neural network model for robot motion planning in time-varying environments

Лебедев, Д. В.; Steil, Jochen J.; Ritter, Helge

doi:10.1016/j.neunet.2005.01.004

Cited by 48 publications

(20 citation statements)

References 37 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Additionally, planning takes place in a redundant 6D configuration space as described in the previous section. While up to date there are no real-time solutions available for such high-dimensional planning problems, which can dynamically integrate obstacle information, the dynamic wave expansion neural network has been proven to efficiently solve such problems in up to three dimensions [24]. This motivates to decompose the originally 6D planning problem into two 3D problems, which, however, interact in a highly non-trivial way because both 3D planners refer to a common real-world situation.…”

Section: Distributed Dynamic Path Planningmentioning

confidence: 99%

“…Each subplanner is an instance of the original dynamic wave expansion neural network (DWENN [24]), an efficient tool for path planning in time-varying and highly dynamic environments. The main feature of this grid based algorithm is that its "local" per-node complexity does not directly depend on the dimensionality of the configuration space.…”

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Dynamic Path Planning for a 7-DOF Robot Arm

Klanke

Лебедев

Haschke

et al. 2006

2006 IEEE/RSJ International Conference on Intelligent Robots and Systems

View full text Add to dashboard Cite

Abstract-We present an on-line, robust, and efficient path planner for the redundant Mitsubishi PA-10 arm with 7 degrees of freedom (DOF) in non-stationary environments. Because of the specific kinematic model of the arm, path planning can be first reduced to a redundant 6-DOF problem in a 5D configuration space, which can be further decomposed into two problems: (i) 3D position planning in Cartesian space and (ii) planning in a 3D space composed of two orientation angles and an explicit parameterization of the arms redundancy. Position and orientation planning are interweaving and performed "on-thefly" without explicit global knowledge of the environment using two instances of the dynamic wave expansion neural network (DWENN), an effective method for path generation in arbitrarily changing environments. The dynamic and explorative nature of the DWENN algorithm allows to treat stationary and dynamic obstacles in a unified manner. Through a number of simulative tests, we show that the planner is capable of reaching both a satisfactory robustness level and real-time performance, as required by many practical applications.

show abstract

Section: Distributed Dynamic Path Planningmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Dynamic Path Planning for a 7-DOF Robot Arm

Klanke

Лебедев

Haschke

et al. 2006

2006 IEEE/RSJ International Conference on Intelligent Robots and Systems

View full text Add to dashboard Cite

show abstract

“…The short lines emanating from the free spaces point to the neighbor through whom the information for updating y was obtained, that is, they point in the direction of greatest decrease in y. The safety margin is 1 + √ 2 and the robot remains outside these margins until t = 71, after which it remains within the safety margin of the third obstacle until catching the target at (20,19).…”

Section: Simulation Studiesmentioning

confidence: 99%

“…It enters the safety margin at that point since the target is very close by at (19,15), and all other safer paths have larger distance to the target. Thereafter, the robot remains close to the obstacle as it chases down the target, finally reaching it at (20,19).…”

Section: A Target Chasingmentioning

confidence: 99%

See 1 more Smart Citation

Real-Time Robot Path Planning via a Distance-Propagating Dynamic System with Obstacle Clearance

Willms

Yang

2008

IEEE Trans. Syst., Man, Cybern. B

View full text Add to dashboard Cite

Abstract-An efficient grid-based distance-propagating dynamic system is proposed for real-time robot path planning in dynamic environments which incorporates safety margins around obstacles using local penalty functions. The path through which the robot travels minimizes the sum of the current known distance to a target and the cumulative local penalty functions along the path. The algorithm is similar to D * but does not maintain a sorted queue of points to update. The resulting gain in computational speed is offset by the need to update all points in turn. Consequently, in situations where many obstacles and targets are moving at substantial distances from the current robot location, this algorithm is more efficient than D * . The properties of the algorithm are demonstrated through a number of simulations. A sufficient condition for capture of a target is provided.

show abstract

Study on Method of Cutting Trajectory Planning Based on Improved Particle Swarm Optimization for Roadheader

Wang

Ren

et al. 2019

Lecture Notes in Computer Science

View full text Add to dashboard Cite

The dynamic wave expansion neural network model for robot motion planning in time-varying environments

Cited by 48 publications

References 37 publications

Dynamic Path Planning for a 7-DOF Robot Arm

Dynamic Path Planning for a 7-DOF Robot Arm

Real-Time Robot Path Planning via a Distance-Propagating Dynamic System with Obstacle Clearance

Study on Method of Cutting Trajectory Planning Based on Improved Particle Swarm Optimization for Roadheader

Contact Info

Product

Resources

About