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Junghwan Lee

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4 papers

1 author row

ICRA Conference 2014 Conference Paper

Cloud RRT ∗: Sampling Cloud based RRT ∗

Donghyuk Kim
Junghwan Lee
Sung-Eui Yoon

We present a novel biased sampling technique, Cloud RRT ∗, for efficiently computing high-quality collision-free paths, while maintaining the asymptotic convergence to the optimal solution. Our method uses sampling cloud for allocating samples on promising regions. Our sampling cloud consists of a set of spheres containing a portion of the C-space. In particular, each sphere projects to a collision-free spherical region in the workspace. We initialize our sampling cloud by conducting a workspace analysis based on the generalized Voronoi graph. We then update our sampling cloud to refine the current best solution, while maintaining the global sampling distribution for exploring understudied other homotopy classes. We have applied our method to a 2D motion planning problem with kinematic constraints, i. e. , the Dubins vehicle model, and compared it against the state-of-the-art methods. We achieve better performance, up to three times, over prior methods in a robust manner.

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ICRA Conference 2014 Conference Paper

PROT: Productive regions oriented task space path planning for hyper-redundant manipulators

Junghwan Lee
Sung-Eui Yoon

In this paper we propose a novel efficient sampling bias technique to improve the performance of a task space trajectory planner for hyper-redundant manipulators. We defines productive regions in the task space as a set of states that can lead effectively to a goal state. We first compute a maximum reachable area (MRA) where a robot can reach from the node by an employed local planner for a node in the task space. When the MRA of a node contains the goal state, we call it promising and bias our sampling to cover promising MRAs. When the MRA does not contain the goal state, we call it unpromising and construct a detour sampling domain for detouring operations from obstacles constraining the manipulator. The union of promising MRAs and detour sampling domains approximates our productive regions, and we bias our sampling to cover these domains more. We have applied our Productive Regions Oriented Task space planner (PROT) to various types of robots in R 2 task space and achieved up to 3. 54 times improvement over the state-of-the-art task space planner. We have additionally verified the benefits of our method by applying it to cabled mobile robot planning.

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IROS Conference 2013 Conference Paper

VLSH: Voronoi-based locality sensitive hashing

Tieu Lin Loi
Jae-Pil Heo
Junghwan Lee
Sung-Eui Yoon

We present a fast, yet accurate k-nearest neighbor search algorithm for high-dimensional sampling-based motion planners. Our technique is built on top of Locality Sensitive Hashing (LSH), but is extended to support arbitrary distance metrics used for motion planning problems and adapt irregular distributions of samples generated in the configuration space. To enable such novel characteristics our method embeds samples generated in the configuration space into a simple l 2 norm space by using pivot points. We then implicitly define Voronoi regions and use local LSHs with varying quantization factors for those Voronoi regions. We have applied our method and other prior techniques to high-dimensional motion planning problems. Our method is able to show performance improvement by a factor of up to three times even with higher accuracy over prior, approximate nearest neighbor search techniques.

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ICRA Conference 2012 Conference Paper

SR-RRT: Selective retraction-based RRT planner

Junghwan Lee
OSung Kwon
Liangjun Zhang
Sung-Eui Yoon

We present a novel retraction-based planner, selective retraction-based RRT, for efficiently handling a wide variety of environments that have different characteristics. We first present a bridge line-test that can identify regions around narrow passages, and then perform an optimization-based retraction operation selectively only at those regions. We also propose a non-colliding line-test, a dual operator to the bridge line-test, as a culling method to avoid generating samples near wide-open free spaces and thus to generate more samples around narrow passages. These two tests are performed with a small computational overhead and are integrated with a retraction-based RRT. In order to demonstrate benefits of our method, we have tested our method with different benchmarks that have varying amounts of narrow passages. Our method achieves up to 21 times and 3. 5 times performance improvements over a basic RRT and an optimization-based retraction RRT, respectively. Furthermore, our method consistently improves the performances of other tested methods across all the tested benchmarks that have or do not have narrow passages.

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