Quasi-Randomized Path Planning (2001)

by M S Branicky, S M LaValle, K Olson, L Yang
Venue:In Proceedings of the 1999 IEEE International Conference on Robotics and Automation, Seoul, Korea
Add To MetaCart
Results 1 - 10 of 74

On the Relationship Between Classical Grid Search and Probabilistic Roadmaps

by Steven M. LaValle, Michael S. Branicky
"... 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 ..."
Abstract - Cited by 134 (10 self) - Add to MetaCart
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.

Motion Planning for Humanoid Robots

by James Kuffner, Koichi Nishiwaki, Satoshi Kagami, Masayuki Inaba, Hirochika Inoue , 2003
"... Humanoid robotics hardware and control techniques have advanced rapidly during the last five years. Presently, several companies have announced the commercial availability of various humanoid robot prototypes. In order to improve the autonomy and overall functionality of these robots, reliable senso ..."
Abstract - Cited by 83 (5 self) - Add to MetaCart
Humanoid robotics hardware and control techniques have advanced rapidly during the last five years. Presently, several companies have announced the commercial availability of various humanoid robot prototypes. In order to improve the autonomy and overall functionality of these robots, reliable sensors, safety mechanisms, and general integrated software tools and techniques are needed. We believe that the development of practical motion planning algorithms and obstacle avoidance software for humanoid robots represents an important enabling technology. This paper gives an overview of some of our recent efforts to develop motion planning methods for humanoid robots for application tasks involving navigation, object grasping and manipulation, footstep placement, and dynamically-stable full-body motions. We show experimental results obtained by implementations running within a simulation environment as well as on actual humanoid robot hardware.

A comparative study of probabilistic roadmap planners

by Roland Geraerts, Mark H. Overmars - IN: WORKSHOP ON THE ALGORITHMIC FOUNDATIONS OF ROBOTICS , 2002
"... The probabilistic roadmap approach is one of the leading motion planning techniques. Over the past eight years the technique has been studied by many different researchers. This has led to a large number of variants of the approach, each with its own merits. It is difficult to compare the different ..."
Abstract - Cited by 69 (10 self) - Add to MetaCart
The probabilistic roadmap approach is one of the leading motion planning techniques. Over the past eight years the technique has been studied by many different researchers. This has led to a large number of variants of the approach, each with its own merits. It is difficult to compare the different techniques because they were tested on different types of scenes, using different underlying libraries, implemented by different people on different machines. In this paper we provide a comparative study of a number of these techniques, all implemented in a single system and run on the same test scenes and on the same computer. In particular we compare collision checking techniques, basic sampling techniques, and node adding techniques. The results should help future users of the probabilistic roadmap planning approach to choose the correct techniques.
(Show Context)

Current Issues in Sampling-Based Motion Planning

by Stephen R. Lindemann , Steven M. LaValle , 2003
"... In this paper, we discuss the field of sampling-based motion planning. In contrast to methods that construct boundary representations of configuration space obstacles, sampling-based methods use only information from a collision detector as they search the configuration space. The simplicity of this ..."
Abstract - Cited by 38 (1 self) - Add to MetaCart
In this paper, we discuss the field of sampling-based motion planning. In contrast to methods that construct boundary representations of configuration space obstacles, sampling-based methods use only information from a collision detector as they search the configuration space. The simplicity of this approach, along with increases in computation power and the development of efficient collision detection algorithms, has resulted in the introduction of a number of powerful motion planning algorithms, capable of solving challenging problems with many degrees of freedom. First, we trace how samplingbased motion planning has developed. We then discuss a variety of important issues for sampling-based motion planning, including uniform and regular sampling, topological issues, and search philosophies. Finally, we address important issues regarding the role of randomization in sampling-based motion planning.

Sampling-based roadmap of trees for parallel motion planning

by Erion Plaku, Kostas E. Bekris, Brian Y. Chen, Andrew M. Ladd, Lydia E. Kavraki - IEEE Trans. Robot , 2005
"... Abstract — This paper shows how to effectively combine a sampling-based method primarily designed for multiple query motion planning (Probabilistic Roadmap Method- PRM) with sampling-based tree methods primarily designed for single query motion planning (Expansive Space Trees, Rapidly-Exploring Rand ..."
Abstract - Cited by 36 (11 self) - Add to MetaCart
Abstract — This paper shows how to effectively combine a sampling-based method primarily designed for multiple query motion planning (Probabilistic Roadmap Method- PRM) with sampling-based tree methods primarily designed for single query motion planning (Expansive Space Trees, Rapidly-Exploring Random Trees, and others) in a novel planning framework that can be efficiently parallelized. Our planner not only achieves a smooth spectrum between multiple query and single query planning but it combines advantages of both. We present experiments which show that our planner is capable of solving problems that cannot be addressed efficiently withPRM or single-query planners. A key advantage of our planner is that it is significantly more decoupled thanPRM and sampling-based tree planners. Exploiting this property, we designed and implemented a parallel version of our planner. Our experiments show that our planner distributes well and can easily solve high-dimensional problems that exhaust resources available to single machines and cannot be addressed with existing planners. Index Terms — Motion planning, sampling-based planning, parallel
(Show Context)

Using Workspace Information as a Guide to Non-Uniform Sampling in Probabilistic Roadmap Planners

by Jur P. van den Berg, Mark H. Overmars , 2005
"... The probabilistic roadmap (PRM) planner is a popular method for robot motion planning problems with many degrees of freedom. However, it has been shown that the method performs less well in situations where the robot has to pass through a narrow passage in the scene. This is mainly due to the unifor ..."
Abstract - Cited by 34 (2 self) - Add to MetaCart
The probabilistic roadmap (PRM) planner is a popular method for robot motion planning problems with many degrees of freedom. However, it has been shown that the method performs less well in situations where the robot has to pass through a narrow passage in the scene. This is mainly due to the uniformity of the sampling used in the planner; it places many samples in large open regions and too few in tight passages. Cell decomposition methods do not have this disadvantage, but are only applicable in low-dimensional configuration spaces. In this paper, a hybrid technique is presented that combines the strengths of both methods. It is based on a robot independent cell decomposition of the free workspace guiding the probabilistic sampling more toward the interesting regions in the configuration space. The regions of interest (narrow passages) are identified in the cell decomposition using a method we call watershed labeling. It is shown that this leads to improved performance on difficult planning problems in two- and three-dimensional workspaces.

Useful Cycles in Probabilistic Roadmap Graphs

by Dennis Nieuwenhuisen, Mark H. Overmars , 2004
"... Over the last decade, the probabilistic road map method (prm) has become one of the dominant motion planning techniques. Due to its random nature, the resulting paths tend to be much longer than the optimal path despite the development of numerous smoothing techniques. Also, the path length varies a ..."
Abstract - Cited by 31 (5 self) - Add to MetaCart
Over the last decade, the probabilistic road map method (prm) has become one of the dominant motion planning techniques. Due to its random nature, the resulting paths tend to be much longer than the optimal path despite the development of numerous smoothing techniques. Also, the path length varies a lot every time the algorithm is executed. In this paper we present a new technique that results in higher quality (shorter) paths with much less variation between the executions. The technique is based on adding useful cycles to the roadmap graph.

Incrementally Reducing Dispersion by Increasing Voronoi Bias in RRTs

by Stephen R. Lindemann, Steven M. LaValle , 2004
"... We discuss theoretical and practical issues related to using Rapidly-Exploring Random Trees (RRTs) to incrementally reduce dispersion in the configuration space. The original RRT planners use randomization to create Voronoi bias, which causes the search trees to rapidly explore the state space. We i ..."
Abstract - Cited by 31 (4 self) - Add to MetaCart
We discuss theoretical and practical issues related to using Rapidly-Exploring Random Trees (RRTs) to incrementally reduce dispersion in the configuration space. The original RRT planners use randomization to create Voronoi bias, which causes the search trees to rapidly explore the state space. We introduce RRT-like planners based on exact Voronoi diagram computation, as well as sampling-based algorithms which approximate their behavior. We give experimental results illustrating how the new algorithms explore the configuration space and how they compare with existing RRT algorithms.

Incremental low-discrepancy lattice methods for motion planning

by Stephen R. Lindemann, Steven M. Lavalle - In Proc. IEEE International Conference on Robotics and Automation , 2003
"... We present deterministic sequences for use in sampling-based approaches to motion planning. They simultaneously combine the qualities found in many other sequences: i) the incremental and self-avoiding tendencies of pseudo-random sequences, ii) the lattice structure provided by multiresolution grids ..."
Abstract - Cited by 29 (7 self) - Add to MetaCart
We present deterministic sequences for use in sampling-based approaches to motion planning. They simultaneously combine the qualities found in many other sequences: i) the incremental and self-avoiding tendencies of pseudo-random sequences, ii) the lattice structure provided by multiresolution grids, and iii) lowdiscrepancy and low-dispersion measures of uniformity provided by quasi-random sequences. The resulting sequences can be considered as multiresolution grids in which points may be added one at a time, while satisfying the sampling qualities at each iteration. An efficient, recursive algorithm for generating the sequences is presented and implemented. Early experiments show promising performance by using the samples in search algorithms to solve motion planning problems. 1
(Show Context)

Deterministic Sampling Methods for Spheres and SO(3)

by Anna Yershova, Steven M. LaValle , 2004
"... This paper addresses the problem of generating uniform deterministic samples over the spheres and the three-dimensional rotation group, SO(3). The target applications include motion planning, optimization, and verification problems in robotics and in related areas, such as graphics, control theory a ..."
Abstract - Cited by 25 (4 self) - Add to MetaCart
This paper addresses the problem of generating uniform deterministic samples over the spheres and the three-dimensional rotation group, SO(3). The target applications include motion planning, optimization, and verification problems in robotics and in related areas, such as graphics, control theory and computational biology. We introduce an infinite sequence of samples that is shown to achieve: 1) low-dispersion, which aids in the development of resolution complete algorithms, 2) lattice structure, which allows easy neighbor identification that is comparable to what is obtained for a grid in R , and 3) incremental quality, which is similar to that obtained by random sampling. The sequence is demonstrated in a samplingbased motion planning algorithm.