Parallel On-Line Motion Planning for Industrial Robots

Wurll, Christian; Henrich, Dominik; Wörn, Heinz

doi:10.1061/40337(205)46

Cited by 15 publications

(11 citation statements)

References 10 publications

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“…The multigoal motion planning problem was previously introduced in Danner and Kavraki (2000), Spitz and Requicha (2000), and Wurll, Henrich, and Wörn (1999). We review these works below and relate them to ours.…”

Section: Relation To Previous Workmentioning

confidence: 98%

“…(b) The multigoal planner described in Wurll, Henrich, and Wörn (1999) tries to avoid computing a collision-free path between every pair of goals. However, unlike ours, it uses neither lower bounds on path lengths, nor the information contained in intermediate tours computed by the TSP algorithm to guide the selection of the pairs of goals between which paths are computed.…”

Section: Relation To Previous Workmentioning

confidence: 99%

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Planning Tours of Robotic Arms among Partitioned Goals

Saha

Roughgarden

Latombe

et al. 2006

The International Journal of Robotics Research

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In this paper we consider a motion planning problem that occurs in tasks such as spot welding, car painting, inspection, and measurement, where the end-effector of a robotic arm must reach successive goal placements given as inputs. The problem is to compute a nearoptimal path of the arm so that the end-effector visits each goal once. It combines two notoriously hard subproblems: the collisionfree shortest-path and the traveling-salesman problems. It is further complicated by the fact that each goal placement of the end-effector may be achieved by several configurations of the arm (distinct solutions of the arm's inverse kinematics). This leads to considering a set of goal configurations of the robot that are partitioned into groups. The planner must compute a robot path that visits one configuration in each group and is near optimal over all configurations in every goal group and over all group orderings. The algorithm described in this paper operates under the assumption that finding a good tour in a graph with edges of given costs takes much less time than computing good paths between all pairs of goal configurations from different groups. So, the algorithm balances the time spent in computing paths between goal configurations and the time spent in computing tours. Although the algorithm still computes a quadratic number of such paths in the worst case, experimental results show that it is much faster in practice.

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Section: Relation To Previous Workmentioning

confidence: 98%

Section: Relation To Previous Workmentioning

confidence: 99%

Planning Tours of Robotic Arms among Partitioned Goals

Saha

Roughgarden

Latombe

et al. 2006

The International Journal of Robotics Research

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show abstract

“…The real-time [46,58] and parallel processing techniques [84] were used, where both teams react adaptively to any stimulus produced by their partners' decisions, with the model visualization in real time. The study of smart multirobot behavior in a single global environment enables concepts related to the use of local perception for reactive agents [5,44].…”

Section: Evolutionary Decisionmentioning

confidence: 99%

Nanorobotics Control Design: A Practical Approach Tutorial

Cavalcanti

Freitas

Kretly

2004

Volume 2: 28th Biennial Mechanisms and Robotics Conference, Parts a and B

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The authors present a new approach using genetic algorithms, neural networks and nanorobotics concepts applied to the problem of control design for nanoassembly automation and its application in medicine. As a practical approach to validate the proposed design, we have elaborated and simulated a virtual environment focused on control automation for nanorobotics teams that exhibit collective behavior. This collective behavior is a suitable way to perform a large range of tasks and positional assembly manipulation in a complex 3D workspace. We emphasize the application of such techniques as a feasible approach for the investigation of nanorobotics system design in nanomedicine. Theoretical and practical analyses of control modelling is one important aspect that will enable rapid development in the emerging field of nanotechnology.

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“…For parallel processing, the hypercubes are cyclically mapped blockwise on the p available processors. For details, see [Wurll98a].…”

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confidence: 99%

Multi-directional search with goal switching for robot path planning

Henrich

Wurll

Wörn

1998

Tasks and Methods in Applied Artificial Intelligence

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We present a parallel path planning method that is able to automatically handle multiple goal configurations as input. There are two basic approaches, goal switching and bidirectional search, which are combined in the end. Goal switching dynamically selects a favourite goal depending on some distance function. The bi-directional search supports the backward search direction from the goal to the start configuration, which is probably faster. The multi-directional search with goal switching combines the advantages of goal switching and bi-directional search. Altogether, the planning system is enabled to select one of the preferable goal configuration by itself. All concepts are experimentally validated for a set of benchmark problems consisting of an industrial robot arm with six degrees of freedom in a 3D environment.

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Parallel On-Line Motion Planning for Industrial Robots

Cited by 15 publications

References 10 publications

Planning Tours of Robotic Arms among Partitioned Goals

Planning Tours of Robotic Arms among Partitioned Goals

Nanorobotics Control Design: A Practical Approach Tutorial

Multi-directional search with goal switching for robot path planning

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