The Ariadne's Clew Algorithm

Mazer, Emmanuel; Ahuactzin, Juan-Manuel; Bessìère, Pierre

doi:10.1613/jair.468

Cited by 94 publications

(62 citation statements)

References 21 publications

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“…Ariadne's Clew algorithm This approach grows a search tree that is biased to explore as much new territory as possible in each iteration [129,128]. There are two modes, search and explore, which alternate over successive iterations.…”

Section: Other Methodsmentioning

confidence: 99%

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Combinatorial Motion Planning

LaValle

2006

Planning Algorithms

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Section: Other Methodsmentioning

confidence: 99%

“…One disadvantage of Ariadne's Clew algorithm is that it is very difficult to solve the optimization problem for placing a new vertex in the explore mode. Genetic algorithms were used in [128], which are generally avoided for motion planning because of the required problem-specific parameter tuning.…”

Section: Other Methodsmentioning

confidence: 99%

Combinatorial Motion Planning

LaValle

2006

Planning Algorithms

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“…A given query is quantized, and the bitmap can then be searched using a classical AI or graph search algorithm, such as dynamic programming, A * , best-first, or bidirectional search, to connect q init to q goal . In fact, the bitmap could also be searched using recent path planning methods that are based on incremental search, such as randomized potential fields [4], Ariadne's clew [27,26], RRTs [22], and the planner in [16,31]. It is well-known that only resolution completeness can be obtained, and that for a fixed resolution, the number of samples (bitmap size) increases exponentially in d. The original PRM The Probabilistic Roadmap (PRM) was introduced in [19] as a way to overcome the well-known curse of dimensionality that exists in grid search.…”

Section: Grids and Prmsmentioning

confidence: 99%

“…The resulting planner is sometimes very efficient in comparison to the original PRM. This represents a shift from the multiple query philosophy of the original PRM [19], and returns to the single query philosophy which was used in some earlier planners [4,13,27]. The key idea in the Lazy PRM is to build the roadmap initially without the use of a collision detector.…”

Section: Grids and Prmsmentioning

confidence: 99%

On the Relationship between Classical Grid Search and Probabilistic Roadmaps

LaValle¹,

Branicky

Lindemann

2004

Springer Tracts in Advanced Robotics

110

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Abstract. We present, implement, and analyze a spectrum of closely-related planners, designed to gain insight into the relationship between classical grid search and probabilistic roadmaps (PRMs). Building on quasi-Monte Carlo sampling literature, we have developed deterministic variants of the PRM that use low-discrepancy and low-dispersion samples, including lattices. Classical grid search is extended using subsampling for collision detection and also the optimal-dispersion Sukharev grid, which can be considered as a kind of lattice-based roadmap to complete the spectrum. Our experimental results show that the deterministic variants of the PRM offer performance advantages in comparison to the original PRM and the recent Lazy PRM. This even includes searching using a grid with subsampled collision checking. Our theoretical analysis shows that all of our deterministic PRM variants are resolution complete and achieve the best possible asymptotic convergence rate, which is shown superior to that obtained by random sampling. Thus, in surprising contrast to recent trends, there is both experimental and theoretical evidence that some forms of grid search are superior to the original PRM.

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“…They apply a similar technique, coding the search space in terms of a list of "rotate" and "move" commands for the individual joints to plan paths for holonomic mobile robots. Many years later, this author with others extended this work through the development of the Ariadne's Clew algorithm, Mazer et al 1998, which utilizes both an explore function to build a representation of accessible space and a search function which looks for the target end state. This algorithm proved capable of planning collision-free paths for a six degree of freedom manipulator allowing it to avoid a separate six degrees of freedom manipulator driven by random trajectory commands.…”

Section: Trajectory and Path Planning Using Genetic Algorithm: State mentioning

confidence: 99%

Trajectory planning for industrial robot using genetic algorithms

Abu‐Dakka¹

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En el nombre de Allah, el Misericordioso, el Compasivo(1) ¡Lee en el nombre de su Señor que ha creado! (2) Ha creado al hombre de un coágulo. ABSTRACTIn the last decades, many researches have been proposed concerning the path and trajectory planning for manipulators. Path and trajectory planning have important applications in many areas, for example industrial robotics, autonomous systems, virtual prototyping, and computer-aided drug design. On the other hand, the evolutionary algorithms have been applied in this plethora of fields, which motivates the author's interest on its application to the path and trajectory planning for industrial robots.In this work, an exhaustive search of the existing literature related to the thesis has been carried out, which has served to create a comprehensive database used to perform a detailed historical review of developments since its origins to the current state of the art and the latest trends.This thesis presents a new methodology that uses genetic algorithms to develop and evaluate path and trajectory planning algorithms. Problem-specific knowledge and heuristic knowledge are incorporated into encoding, evaluation and genetic operators of the genetic algorithm.This methodology introduces new approaches that aim at solve the problem of path planning and trajectory planning for industrial robotic systems operating in 3D environments with static obstacles. Therefore, two algorithms (somehow, they are similar, but with some variations) are created to solve the mentioned planning problems.Obstacles modeling have been done by using combinations of simple geometric objects (spheres, cylinders, and plans) which provide an efficient algorithm for collision avoidance. Trajectory Planning for Industrial Robots Using Genetic AlgorithmsPath planning algorithm is based on global genetic algorithms optimization techniques, which aim to minimize the sum of the distances between significant points of the robot along the path considering the restrictions to avoid collisions with obstacles. The path is composed of adjacent configurations obtained by an optimization technique using genetic algorithms, seeking to minimize a multiobjective function that involves the distance between significant points of the two adjacent configurations, and the distance from the points of the current configuration to the final one. An evaluation method is designed according to the problem presentation by defining individuals and genetic operators capable of providing efficient solutions to the problem. The result of this algorithm is the shortest path between two configurations given by the user.Trajectory planning algorithm is also based on genetic algorithms optimization techniques using the direct procedure. The algorithm is similar to the mentioned previously algorithm for path planning problem, but with some differences in the objective function and some details related to the conceptual difference between path and trajectory planning. The objective of this algorithm is to minimize the time required to mo...

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The Ariadne's Clew Algorithm

Cited by 94 publications

References 21 publications

Combinatorial Motion Planning

Combinatorial Motion Planning

On the Relationship between Classical Grid Search and Probabilistic Roadmaps

Trajectory planning for industrial robot using genetic algorithms

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