Y. K. Hwang and N. Ahuja. Gross motion planning -- a survey. ACM Comput. Surv., 24(3):219--291, 1992. Home/Search Document Not in Database Summary Related Articles Check |
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Deterministic vs. Probabilistic Roadmaps - Branicky, LaValle, Olson, Yang (2002) (4 citations) (Correct)
....I. Introduction Over two decades of path planning research have led to two primary trends. By the 1980s, deterministic approaches provided both elegant, complete algorithms for solving the problem (e.g. 10] 38] 41] and also useful approximate or incomplete algorithms (e.g. 14] 15] [23], 28] The curse of dimensionality due to high dimensional con guration spaces motivated researchers from the 1990s to the present time to develop randomized approaches which are incomplete, but capable of eciently solving many challenging, high dimensional problems [4] 22] 26] 28] 33] ....
Y. K. Hwang and N. Ahuja. Gross motion planning{A survey. ACM Computing Surveys, 24(3):219-291, September 1992.
Adaptive Strategies for Probabilistic Roadmap Construction - Isto, Tuominen, Mäntylä (2003) (Correct)
....planners are a family of randomized motion planning algorithms that have been a subject of intensive theoretical and experimental research. The roadmap approach is a global approach that aims to build a representation of the connectivity of the configuration space (cspace) as a network of curves [3]. Probabilistic roadmap (PRM) planners use randomized sampling of the cspace and local planning between the samples to produce a probabilistic approximation of the connectivity of the cspace. PRMs can be used in two modes. In the first mode, a separate learning or preprocessing stage builds the ....
....for the query. In the latter mode, the roadmap is usually discarded immediately after the query is answered. A particular algorithm is often tuned for one or the other mode, but many algorithmic techniques can be used in both types of PRM planning. Since the motion planning problem is PSPACE hard [3], it is difficult to design practical planners that have a consistently good performance over a range of different tasks. Many techniques have been presented to improve the performance of motion planners, but many tend to rely on assumptions on the structure of the task, which are easy to violate. ....
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Y. K. Hwang and N. Ahuja, Gross Motion Planning - A Survey, ACM Computing Surveys, Vol. 24, No. 3, Sep. 1992, 219-291.
Motion Planning for a Humanoid Walking in a 3D Space - Li, Huang (Correct)
....results for three example scenarios. Finally, we will con clude our work in the last section. 2. Related Work The gross motion planning problem was originally brought up in the context of robotics to generate collision free path for robots. A survey of approaches to the problem can be found in [6] and [12] According to [2] most planners solve this problem with two phases. The first phase, called preprocessing phase, converts the geometric problem into a problem with abstract data structure (ADT) such as a graph. This ADT will then be searched for a feasible path in the query phase. ....
Y.K. Hwang and N. Ahuja, "Gross Motion Planning - a Survey," ACM Comp. Surveys, 24(3):219-291, 1992.
Parallel Search Algorithms for Robot Motion Planning - Gini (1993) (24 citations) (Correct)
....1 Introduction Motion planning is the process of computing paths that will allow a robot to move to different positions in its environment without hitting obstacles. Many algorithms have been developed [Latombe, 1991] but most are never used in practice because of their computational complexity [Hwang and Ahuja, 1992]. The intent of this paper is to show that plans for multi jointed dexterous robot arms which operate in realistic environments can be synthesized very quickly by parallel algorithms. The ability to plan paths quickly is important to make motion planning useful in application areas, such as ....
....Design and Control of the University of Minnesota. To obtain acceptable performance, some methods do a significant amount of preprocessing of the config uration space (C space) Kavraki, 1994] or place landmarks in C space that are then used by a local planner [Bessiere et at. 1993, Chen and Hwang, 1992]. Other methods make assumptions on the type of robot (for instance, Adolphs and Tolle, 1992] takes advantage of the symmetry of the workspace with respect to the first axis of the robot) or use a coarse discretization of C Space. Real time has been achieved in detection of imminent collisions ....
Y.K. Hwang and N. Ahuja. Gross motion planning - a survey. ACM Computing Surveys, 24(3):219-291, 1992.
Value-Driven Behavior Generation for an Autonomous Mobile - Ground Robot Stephen (Correct)
.... criterion allows connections at a wider variety of angles the a set 4 or 8 way connection strategy) while the rule base Where gross motion planning may be defined as being concerned with problems involving free space much wider than the objects sizes plus the positional error of the robot [11] p. 220. implements mandatory constraints on the plan (for example a node to node transition that will not conform to a proper formation will not be connected) It should be noted that each pair of connected nodes is connected by two edges, one for each direction of state transition. The reason ....
Hwang, Y. K. and Ahuja, N., "Gross Motion Planning - A Survey," ACM Computing Surveys,Vol.24,No.3, 1992, pp. 219-291.
SCSE Report 9403 - March Motion Planning (Correct)
....object. Normally, trajectory planning based on local constraints does not change a path computed by FINDPATH, since the latter would have considered all such constraints before computing the path. Freespacedecomposition is one of the geometric methods employed to solve the path planning problem [HWA92]. The freespace method works in two stages. In the first stage, an appropriate representation scheme that captures the essential properties of the problem environment is devised# the second stage utilizes this representation to solve the problem at hand. In other words, this method imposes an ....
Hwang, Y.K., N. Ahuja, 'Gross motion planning- A survey', ACM Computing Reviews, 24(3), pp. 219-291, 1992.
ERPP: An Experience-based Randomized Path Planner - Caselli, Reggiani (2000) (2 citations) (Correct)
....planner over the parallel implementation of a well known probabilistic motion planning algorithm. 1 Introduction Motion planning has been the focus of considerable research, owing to its importance in a number of application areas, including robotics, automation, and virtual reality [17, 13, 10]. Unfortunately, motion planning has proven highly complex [20, 17] The existing general complete algorithms, which guarantee to find a path if such a path exists, are exponential in the number of degrees of freedom of the problem [4] It is generally agreed that complete algorithms cannot be ....
....solve many important applications within acceptable time constraints. Like most of the research in motion planning, this paper addresses the basic planning problem, searching for a path between initial and final configurations in a known, static workspace while avoiding collision with obstacles [17, 13]. This problem directly arises in a number of real applications, such as robot path planning in industrial workcells and design for maintainability of complex mechanical systems [7, 10] Moreover, effectively solving the basic motion planning problem is a key issue also faced in solving more ....
Y. K. Hwang and N. Ahuja. Gross motion planning - a survey. ACM Computing Surveys, 24(3):219--291, September 1992.
An Obstacle-Based Probabilistic Roadmap Method For Path Planning - Wu (1996) (1 citation) (Correct)
.... several decades, but most of them are impractical since they are computationally infeasible except for some restricted cases, e.g. when the robot has a small number of degrees of freedom (dof) the dof of a robot is the minimum number of parameters needed to specify a configuration of the robot [20, 27]. On the other hand, the rapid development of computer technology provides a promising future for achieving better solutions to this problem. In the past ten years, both the processor speed and main memory size have been improved by a factor of nearly 100. However, even with such dramatic growth ....
....these cases. B. General Classes of Path Planning Methods A large number of methods have been developed for path planning; most of them work for specific cases and have certain limitations. In general, most path planning methods can be categorized into a few general classes (see Hwang and Ahuja [20] and Latombe [27] 1. Roadmap methods Roadmap (or skeleton) methods represent the free C space by a roadmap (or network) In particular, the idea is to extract the connectivity information of the free C space into a graph of one dimensional curves. Then, after connecting the start and goal ....
Y. Hwang and N. Ahuja, "Gross motion planning -- a survey," ACM Computing Surveys, vol.24, no.3, pp.219--291, Sept., 1992.
Parallel Path Planning with Multiple Evasion Strategies - Caselli, Reggiani, Sbravati (2002) (Correct)
....with the random motion in RPP (labeled as R in the following tables) on the four problems shown in Figures 1, all referring to a 7 d.o.f. robot consisting of 2 links with a free base. These planning problems have been designed to cover the workspace classification proposed by Hwang and Ahuja [17]. Problem 1 (Figure 1(a) belongs to the set of easy problems, and fits the first class in Hwang and Ahuja s classification. The space among obstacles is large compared to robot dimensions and no narrow passage is present. Problems 2 and 3 (Figures 1(b) and 1(c) belong to the second class, i.e. ....
Y. K. Hwang and N. Ahuja. Gross motion planning - a survey. ACM Computing Surveys, 24(3):219--291, 1992.
Heuristic Methods for Randomized Path Planning in.. - Caselli, Reggiani, Rocchi (2001) (Correct)
....motion in RPP (labeled as BR in the following tables) on the four problems shown in Figure 3, all referring to a 2 link, 7 d.o.f. robot. The choice of these planning problems as testbed for our algorithms has been motivated by the classi cation of workspaces proposed by Hwang and Ahuja in [12]. The rst workspace belongs to the set of easy problems, rst class in Hwang and Ahuja s classi cation. The space among obstacles is large compared to robot dimensions and no narrow passage is present. The second and third problems are in the second class, i.e. medium level diculty problems. The ....
Y. K. Hwang and N. Ahuja. Gross motion planning - a survey. ACM Computing Surveys, 24(3):219-291, 1992.
On Probabilistic Completeness and Expected Complexity of.. - Svestka (1996) (1 citation) (Correct)
....running time of PPP grows only logarithmically with the complexity of the problem that it solves. 1 Introduction Robot path planning, which asks for the computation of collision free paths in environments containing obstacles, has received a great deal of attention in the last decades [Lat91, HA92] We consider the basic problem, where there is one robot present in a static and known environment, and the task is to compute a collision free path describing a motion that brings the robot from its current position to some desired goal position. The space where the robot and the obstacles are ....
Y. Hwang and N. Ahuja. Gross motion planning---a survey. ACM Comput. Surv., 24(3):219--291, 1992.
Path Planning Using Lazy PRM - Bohlin, Kavraki (2000) (44 citations) (Correct)
....called Lazy PRM, is described in detail in Section 3, and experimentally evaluated in Section 4 using a real industrial environment. 2 Probabilistic Techniques The path planning problem has been extensively studied in the last two decades, and a number of dif ferent approaches are proposed; see [10, 14, 20] for overviews. An algorithm is called complete if it always will find a solution or determine that none exists. How ever, due to the complexity of the path planning prob lem [8] complete planners are far too slow to be useful in practice. Trading completeness for speed, probabilistic planners ....
Y.K. Hwang and N. Ahuja. Gross motion planning - a survey. ACM Comp. Surveys, 24(3):219-291, 1992.
On the Complexity of Randolph's Robot Game - Engels, Kamphans (2005) (Correct)
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Y. K. Hwang and N. Ahuja. Gross motion planning -- a survey. ACM Comput. Surv., 24(3):219--291, 1992.
Incremental Low-Discrepancy Lattice Methods for Motion Planning - Lindemann, LaValle (2003) (4 citations) (Correct)
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Y. K. Hwang and N. Ahuja. Gross motion planning-- A survey. ACM Computing Surveys, 24(3):219--291, September 1992.
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Y. K. Hwang and N. Ahuja. Gross motion planning--a survey. ACM Computing Surveys, 24(3):219--291, September 1992.
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Y. H. Hwang and N. Ahuja. Gross motion planning { a survey. ACM Computing Surveys, 24(3):219-291, Sept. 1992.
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Y. K. Hwang and N. Ahuja. Gross motion planning -- a survey. ACM Computing Surveys, 24(3):221--291, Sept. 1992.
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Y. K. Hwang and N. Ahuja. Gross motion planning - a survey. ACM Computing Surveys, 24(3):219--291, Sep. 1992.
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Hwang Y. K. and Ahuja, N.: Gross motion planning -- a survey, ACM Computing Surveys 24(3) (1992).
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Y. K. Hwang and N. Ahuja. Gross motion planning--A survey. ACM Computing Surveys, 24(3):219--291, September 1992.
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Y. K. Hwang and N. Ahuja. Gross motion planning { a survey. ACM Computing Surveys, 24(3):219-291, 1992.
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Y. K. Hwang and N. Ahuja 1992, "Gross motion planning --- a survey," ACM Computing Surveys 24(3), 219--291.
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Y.K. Hwang and N. Ahuja, Gross Motion Planning - A Survey, ACM Computing Surveys, vol. 24(3), 1992, pp. 219-291.
A Parallel Motion Planner for Systems with Many Degrees of Freedom - Isto (2001) (Correct)
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Y. K. Hwang, N. Ahuja, Gross Motion Planning - A Survey, ACM Computing Surveys, vol. 24, no. 3, Sep. 1992, 219-291.
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Y. K. Hwang, N. Ahuja, Gross Motion Planning - A Survey, ACM Computing Surveys, Vol. 24, No. 3, Sep. 1992, 219-291.
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