Results 1  10
of
283
On the Relationship Between Classical Grid Search and Probabilistic Roadmaps
 The International Journal of Robotics Research
, 2004
"... We present, implement, and analyze a spectrum of closelyrelated planners, designed to gain insight into the relationship between classical grid search and probabilistic roadmaps (PRMs). Building on the quasiMonte Carlo sampling literature, we have developed deterministic variants of the PRM that u ..."
Abstract

Cited by 111 (11 self)
 Add to MetaCart
We present, implement, and analyze a spectrum of closelyrelated planners, designed to gain insight into the relationship between classical grid search and probabilistic roadmaps (PRMs). Building on the quasiMonte Carlo sampling literature, we have developed deterministic variants of the PRM that use lowdiscrepancy and lowdispersion samples, including lattices. Classical grid search is extended using subsampling for collision detection and also the dispersionoptimal Sukharev grid, which can be considered as a kind of latticebased roadmap to complete the spectrum. Our experimental results show that the deterministic variants of the PRM offer performance advantages in comparison to the original, multiplequery PRM and the singlequery, Lazy PRM. Surprisingly, even some forms of grid search yield performance that is comparable to the original PRM. 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 to be superior to that obtained by random sampling. Thus, in surprising contrast to recent trends, there is both experimental and theoretical evidence that the randomization used in the original PRM is not advantageous.
A kinematicsbased probabilistic roadmap method for closed chain systems
 In Robotics:New Directions
, 2000
"... In this paper we consider the motion planning problem for arbitrary articulated structures with one or more closed kinematic chains in a workspace with obstacles. This is an important class of problems and there are applications in many areas such as robotics, closed molecular chains, graphical anim ..."
Abstract

Cited by 100 (14 self)
 Add to MetaCart
(Show Context)
In this paper we consider the motion planning problem for arbitrary articulated structures with one or more closed kinematic chains in a workspace with obstacles. This is an important class of problems and there are applications in many areas such as robotics, closed molecular chains, graphical animation, reconfigurable robots. We use the kinematicsbased probabilistic roadmap (kbprm) strategy proposed in [7] that conceptually partitions the linkage into a set of open chains and applies random generation methods to some of the chains and traditional inverse kinematics methods to the others. The efficiency of the method depends critically on how the linkage is partitioned into open chains, and the original method assumed the partition was provided as input to the problem. In this paper, we propose a fully automated method for partitioning an arbitrary linkage into open chains and for determining which should be positioned using the inverse kinematic solver. Even so, the size (number of links) of the closed loops that can be handled by this method is limited because the inverse solver can only be applied to small chains. To handle high dof closed loops, we show how we can use the Iterative Relaxation of Constraints (IRC) strategy [3] to efficiently handle large loops while still only using inverse kinematics for small chains. Our results in 3dimensional workspace both for planar and spatial linkages show that our framework performs well for general linkage. We also use our planner to simulate an adjustable lamp called Luxo. Using IRC, our planner can handle a single loop of up to 44 links.
Kinematic controllability for decoupled trajectory planning in underactuated mechanical systems
 IEEE Transactions on Robotics and Automation
, 2001
"... Abstract — We introduce the notion of kinematic controllability for secondorder underactuated mechanical systems. For systems satisfying this property, the problem of planning fast collisionfree trajectories between zero velocity states can be decoupled into the computationally simpler problems of ..."
Abstract

Cited by 65 (16 self)
 Add to MetaCart
(Show Context)
Abstract — We introduce the notion of kinematic controllability for secondorder underactuated mechanical systems. For systems satisfying this property, the problem of planning fast collisionfree trajectories between zero velocity states can be decoupled into the computationally simpler problems of path planning for a kinematic system followed by timeoptimal time scaling. While this approach is well known for fully actuated systems, until now there has been no way to apply it to underactuated dynamic systems. The results in this paper form the basis for efficient collisionfree trajectory planning for a class of underactuated mechanical systems including manipulators and vehicles in space and underwater environments.
DynamicDomain RRTs: Efficient Exploration by Controlling the Sampling Domain
 IN PROCEEDINGS IEEE INTERNATIONAL CONFERENCE ON ROBOTICS AND AUTOMATION
, 2005
"... Samplingbased planners have solved difficult problems in many applications of motion planning in recent years. In particular, techniques based on the Rapidlyexploring Random Trees (RRTs) have generated highly successful singlequery planners. Even though RRTs work well on many problems, they have w ..."
Abstract

Cited by 53 (9 self)
 Add to MetaCart
Samplingbased planners have solved difficult problems in many applications of motion planning in recent years. In particular, techniques based on the Rapidlyexploring Random Trees (RRTs) have generated highly successful singlequery planners. Even though RRTs work well on many problems, they have weaknesses which cause them to explore slowly when the sampling domain is not well adapted to the problem. In this paper we characterize these issues and propose a general framework for minimizing their effect. We develop and implement a simple new planner which shows significant improvement over existing RRTbased planners. In the worst cases, the performance appears to be only slightly worse in comparison to the original RRT, and for many problems it performs orders of magnitude better.
Dynamicallystable Motion Planning for Humanoid Robots
, 2000
"... We present an algorithm for computing stable collisionfree motions for humanoid robots given fullbody posture goals. The motion planner is part of a simulation environment under development for providing highlevel software control for humanoid robots. Given a robot's internal model of the e ..."
Abstract

Cited by 45 (5 self)
 Add to MetaCart
We present an algorithm for computing stable collisionfree motions for humanoid robots given fullbody posture goals. The motion planner is part of a simulation environment under development for providing highlevel software control for humanoid robots. Given a robot's internal model of the environment and a staticallystable desired posture, we use a randomized path planner to search the configuration space of the robot for a collisionfree path. Balance constraints are imposed on incremental search motions in order to maintain the overall dynamic stability of the computed trajectories. The algorithm is presented along with preliminary results using an experimental implementation on a dynamic model of the H5 humanoid robot.
SamplingBased Motion Planning under Kinematic LoopClosure Constraints
 In 6th International Workshop on Algorithmic Foundations of Robotics
, 2004
"... Kinematic loopclosure constraints significantly increase the di#culty of motion planning for articulated mechanisms. Configurations of closedchain mechanisms do not form a single manifold, easy to parameterize, as the configurations of open kinematic chains. In general, they are grouped into sever ..."
Abstract

Cited by 40 (8 self)
 Add to MetaCart
(Show Context)
Kinematic loopclosure constraints significantly increase the di#culty of motion planning for articulated mechanisms. Configurations of closedchain mechanisms do not form a single manifold, easy to parameterize, as the configurations of open kinematic chains. In general, they are grouped into several subsets with complex and a priori unknown topology. Samplingbased motion planning algorithms cannot be directly applied to such closedchain systems. This paper describes our recent work [7] on the extension of samplingbased planners to treat this kind of mechanisms.
Resolution complete rapidlyexploring random trees
 In Proc. IEEE Int’l Conf. on Robotics and Automation
, 2002
"... ..."
(Show Context)
Planning Paths for Elastic Objects Under Manipulation Constraints
 International Journal of Robotics Research
, 2001
"... This paper addresses the problem of planning paths for an elastic object from an initial to a final configuration in a static environment. It is assumed that the object is manipulated by two actuators and that it does not touch the obstacles in its environment at any time. The object may need to ..."
Abstract

Cited by 39 (7 self)
 Add to MetaCart
(Show Context)
This paper addresses the problem of planning paths for an elastic object from an initial to a final configuration in a static environment. It is assumed that the object is manipulated by two actuators and that it does not touch the obstacles in its environment at any time. The object may need to deform in order to achieve a collisionfree path from the initial to the final configuration. Any required deformations are automatically computed by our planner according to the principles of elasticity theory from mechanics. The problem considered in this paper differs significantly from that of planning for a rigid or an articulated object. In the first part of the paper we point out these differences and highlight the reasons that make planning for elastic objects an extremely difficult task. We then present a randomized algorithm for computing collisionfree paths for elastic objects under the abovementioned restrictions of manipulation.
Motion Planning for Humanoid Robots Under Obstacle and Dynamic Balance Constraints
 in IEEE Int. Conf. on Robotics and Automation, 2001
, 2001
"... We present an approach to path planning for humanoid robots that computes dynamicallystable, collisionfree trajectories from fullbody posture goals. Given a geometric model of the environment and a staticallystable desired posture, we search the configuration space of the robot for a collisionf ..."
Abstract

Cited by 37 (2 self)
 Add to MetaCart
(Show Context)
We present an approach to path planning for humanoid robots that computes dynamicallystable, collisionfree trajectories from fullbody posture goals. Given a geometric model of the environment and a staticallystable desired posture, we search the configuration space of the robot for a collisionfree path that simultaneously satisfies dynamic balance constraints. We adapt existing randomized path planning techniques by imposing balance constraints on incremental search motions in order to maintain the overall dynamic stability of the final path. A dynamics filtering function that constrains the ZMP (zero moment point) trajectory is used as a postprocessing step to transform staticallystable, collisionfree paths into dynamicallystable, collisionfree trajectories for the entire body. Although we have focused our experiments on biped robots with a humanoid shape, the method generally applies to any robot subject to balance constraints (legged or not). The algorithm is presented along with computed examples using the humanoid robot "H6".
Efficient Nearest Neighbor Searching for Motion Planning
, 2002
"... We present and implement an efficient algorithm for performing nearestneighbor queries in topological spaces that usually arise in the context of motion planning. Our approach extends the Kd treebased ANN algorithm, which was developed by Arya and Mount for Euclidean spaces. We argue the correctne ..."
Abstract

Cited by 33 (5 self)
 Add to MetaCart
We present and implement an efficient algorithm for performing nearestneighbor queries in topological spaces that usually arise in the context of motion planning. Our approach extends the Kd treebased ANN algorithm, which was developed by Arya and Mount for Euclidean spaces. We argue the correctness of the algorithm and illustrate its efficiency through computed examples. We have applied the algorithm to both probabilistic roadmaps (PRMs) and Rapidlyexploring Random Trees (RRTs). Substantial performance improvements are shown for motion planning examples.