Multiple-goals path planning for coordinate measuring machines

Spitz, Steven; Requicha, Aristides A. G.

doi:10.1109/robot.2000.846373

Cited by 38 publications

(32 citation statements)

References 21 publications

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“…Test results confirmed that proposed methodology was efficient resulted in saving about 30% time as compared to conventional method. Moreover, Spitz and Requicha [70] proposed a methodology based on heuristics to generate efficient collision free path for CMMs. Lu et al [71] applied artificial neural network (ANN) as well as genetic algorithm (GA) to develop intelligent inspection plan for CMMs.…”

Section: -P6mentioning

confidence: 99%

New developments in coordinate measuring machines for manufacturing industries

Mian

Al-Ahmari

2014

Int. J. Metrol. Qual. Eng.

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Abstract. There have been substantial improvements in measurement systems in order to meet fluctuating market demands. This rapid change and development in measurement technology has primarily been governed by demands of accuracy and precision from aerospace, automotive and other manufacturing industries. Coordinate measuring machines (CMMs) available with different technologies and configurations has efficiently been fulfilling customer demands for more than a decade. Though, current CMMs can meet needs of rapid and increased demands of customers to a greater extent but still there is lot of scope for improvement and development in CMMs. The globalization of manufacturing has resulted in development of variety of complex products and miniaturization of mechanical components. The technology of micro/nanoscale 3D measurement is still a bottleneck for industries. Therefore, precise and accurate system which is flexible enough to deal with complexities of parts and micro & nano range products has to be investigated. In this paper, comprehensive review concerning CMMs with capabilities to measure micro/nano features has been presented. This work has also discussed different methods to estimate measurement uncertainty, as well as performance evaluation of CMMs. Moreover, novel concepts such as intelligent CMM, multi-sensor CMM, virtual CMM have been presented.

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Section: -P6mentioning

confidence: 99%

New developments in coordinate measuring machines for manufacturing industries

Mian

Al-Ahmari

2014

Int. J. Metrol. Qual. Eng.

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“…The algorithm is based on [8], in which a probabilistic roadmap (PRM) [22] is iteratively constructed until all goals from the planning mission are attached to a single connected component. Allpairs shortest paths are then computed among the goals, so each goal-to-goal cost reflects the cost of the shortestknown feasible path from goal i to goal j among obstacles.…”

Section: Exhaustive All-pairs Algorithmmentioning

confidence: 99%

“…The goal-to-goal costs are stored in a weighted adjacency matrix, and any TSP algorithm may be used to find a tour among the goals. No specific TSP algorithm is adopted by [8], but the best polynomial-time upper bound on the TSP is achieved using Christofides' algorithm [23]. This algorithm provides a 3/2 upper bound on the cost of the optimal TSP solution by adding together a MST and a minimum-cost perfect matching that pairs up the odd-degree vertices of the MST.…”

Section: Exhaustive All-pairs Algorithmmentioning

confidence: 99%

“…Surveillance applications are also solved by mapping mission goals to graph nodes and modeling the multi-goal planning problem as some variant of the traveling salesman problem (TSP) [4], [5]. Multigoal problems in industrial manufacturing applications have also been solved using the TSP, including spot welding [6], [7] and measuring fabricated parts to ensure tolerances have been met [8]. Additionally, TSP solutions have been developed for robotic systems with kinodynamic constraints [9], [10].…”

Section: Introductionmentioning

confidence: 99%

“…In Section II the first comparison algorithm is introduced, an exhaustive all-pairs search for feasible paths, followed by the use of the best-known heuristic upper bound on the TSP [8]. In Section III the second comparison algorithm is introduced, a fast algorithm designed to require as little collision checking as possible by iteratively computing and checking the minimum spanning tree (MST) [6].…”

Section: Introductionmentioning

confidence: 99%

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Multi-goal feasible path planning using ant colony optimization

Englot

Hover

2011

2011 IEEE International Conference on Robotics and Automation

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Abstract-A new algorithm for solving multi-goal planning problems in the presence of obstacles is introduced. We extend ant colony optimization (ACO) from its well-known application, the traveling salesman problem (TSP), to that of multi-goal feasible path planning for inspection and surveillance applications. Specifically, the ant colony framework is combined with a sampling-based point-to-point planning algorithm; this is compared with two successful sampling-based multi-goal planning algorithms in an obstacle-filled two-dimensional environment. Total mission time, a function of computational cost and the duration of the planned mission, is used as a basis for comparison. In our application of interest, autonomous undewater inspections, the ACO algorithm is found to be the best-equipped for planning in minimum mission time, offering an interior point in the tradeoff between computational complexity and optimality.

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