A method of progressive constraints for manipulation planning

Ferbach, P.; Barraquand, Jér ocirc

doi:10.1109/70.611297

Cited by 21 publications

(18 citation statements)

References 19 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Baginski (1996) deforms links in a kinematic chain until a path is collision-free, then evolves the C-Space trajectory as the link lengths are restored. Ferbach and Barraquand (1997) formulate a series of increasingly relaxed constraints involved in a manipulation problem. By solving the least-constrained instance first and then refining the plan until the fully-constrained version is solved.…”

Section: Sampling Strategies With Fixed Biasmentioning

confidence: 99%

Real-time informed path sampling for motion planning search

Knepper

Mason

2012

The International Journal of Robotics Research

View full text Add to dashboard Cite

Mobile robot motions often originate from an uninformed path sampling process such as random or low-dispersion sampling. We demonstrate an alternative approach to path sampling that closes the loop on the expensive collision-testing process. Although all necessary information for collision-testing a path is known to the planner, that information is typically stored in a relatively unavailable form in a costmap or obstacle map. By summarizing the most salient data in a more accessible form, our process delivers a denser sampling of the free path space per unit time than open-loop sampling techniques. We obtain this result by probabilistically modeling-in real time and with minimal information-the locations of obstacles and free space, based on collision-test results. We present CALM, the combined adaptive locality model, along with an algorithm to bias path sampling based on the model's predictions. We provide experimental results in simulation for motion planning on mobile robots, demonstrating up to a 330% increase in paths surviving collision test.

show abstract

Section: Sampling Strategies With Fixed Biasmentioning

confidence: 99%

Real-time informed path sampling for motion planning search

Knepper

Mason

2012

The International Journal of Robotics Research

View full text Add to dashboard Cite

show abstract

“…These methods generate an initial path considering only some of the constraints and iteratively refine this path into a feasible one by incorporating additional constraints [13]. Foot or finger placement concepts may be explicitly used in the construction of the paths.…”

Section: Multi-step Planningmentioning

confidence: 99%

Multi-Step Motion Planning for Free-Climbing Robots

Bretl

Lall

Latombe

et al. 2005

Springer Tracts in Advanced Robotics

View full text Add to dashboard Cite

Abstract. This paper studies non-gaited, multi-step motion planning, to enable limbed robots to free-climb vertical rock. The application of a multi-step planner to a real free-climbing robot is described. This planner processes each of the many underlying one-step motion queries using an incremental, sample-based technique. However, experimental results point toward a better approach, incorporating the ability to detect when one-step motions are infeasible (i.e., to prove disconnection). Current work on a general method for doing this, based on recent advances in computational real algebra, is also presented.

show abstract

“…We have recently learned about an approach called Progressive Constraints (PC) [13] that has a very similar philosophy to IRC. In particular, PC replaces the original problem with a series of progressively constrained problems whose freespaces decrease monotonically toward the freespace of the original problem and it uses a solution for one version as a heuristic for solving the next version.…”

Section: Related Workmentioning

confidence: 99%

“…We would also like to thank to Jean-Claude Latombe for bringing Progressive Constraints [13] to our attention.…”

Section: Acknowledgmentsmentioning

confidence: 99%

Iterative relaxation of constraints: a framework for improving automated motion planning

Bayazıt

Xie

Amato

2005

2005 IEEE/RSJ International Conference on Intelligent Robots and Systems

View full text Add to dashboard Cite

This paper presents a technique for improving the efficiency of automated motion planners. Motion planning has application in many areas such as robotics, virtual reality systems, computer-aided design, and even computational biology. Although there have been steady advances in motion planning algorithms, especially in randomized approaches such as probabilistic roadmap methods (PRMs) or rapidly-exploring random trees (RRTs), there are still some classes of problems that cannot be solved efficiently using these state-of-the-art motion planners. In this paper, we suggest an iterative strategy addressing this problem where we first simplify the problem by relaxing some feasibility constraints, solve the easier version of the problem, and then use that solution to help us find a solution for the harder problem. We show how this strategy can be applied to rigid bodies and to linkages with high degrees of freedom, including both open and closed chain systems. Experimental results are presented for linkages composed of 9-98 links. Although we use PRMs as the automated planner, the framework is general and can be applied with other motion planning techniques as well.

show abstract

A method of progressive constraints for manipulation planning

Cited by 21 publications

References 19 publications

Real-time informed path sampling for motion planning search

Real-time informed path sampling for motion planning search

Multi-Step Motion Planning for Free-Climbing Robots

Iterative relaxation of constraints: a framework for improving automated motion planning

Contact Info

Product

Resources

About