scholarly journals A Fast Algorithm for Planning Collision-Free Paths With Rotations

1997 ◽  
Author(s):  
Shiang-Fong Chen ◽  
James H. Oliver ◽  
David Fernandez-Baca
Keyword(s):  
Motion Planning ◽  
Free Path ◽  
Configuration Space ◽  
Fast Algorithm ◽  
Experimental Results ◽  
Optimal Motion ◽  
Running Time ◽  
Practical Algorithm

Abstract Motion planning is a major problem in robotics. The objective is to plan a collision-free path for a robot moving through a workspace populated with obstacles. In this paper, we present a fast and practical algorithm for moving a convex polygonal robot among a set of polygonal obstacles with translations and rotations. The running time is O(c((n + k)N + nlogn)), where c is a parameter controlling the precision of the results, n is the total number of obstacle vertices, k is the number of intersections of configuration space obstacles, and N is the number of obstacles, decomposed into convex objects. This work builds upon the slabbing method proposed by Ahrikencheikh et al. (1994), which finds an optimal motion for a point among a set of non-overlapping obstacles. Here, we extend the slabbing method to the motion planning of a convex polygonal robot with translations and rotations, which also allows overlapping configuration space obstacles. This algorithm has been fully implemented and the experimental results show that it is more robust and faster than other approaches.

scholarly journals A Fast Algorithm for Planning Collision-Free Paths With Rotations

1998 ◽  
Vol 120 (1) ◽  
pp. 52-57 ◽  
Author(s):  
S.-F. Chen ◽  
J. H. Oliver ◽  
D. Fernandez-Baca
Keyword(s):  
Motion Planning ◽  
Free Path ◽  
Configuration Space ◽  
Fast Algorithm ◽  
Experimental Results ◽  
Optimal Motion ◽  
Running Time ◽  
Practical Algorithm

Motion planning is a major problem in robotics. The objective is to plan a collision-free path for a robot moving through a workspace populated with obstacles. In this paper, we present a fast and practical algorithm for moving a convex polygonal robot among a set of polygonal obstacles with translations and rotations. The running time is O(c((n + k)N + n log n)), where c is a parameter controlling the precision of the results, n is the total number of obstacle vertices, k is the number of intersections of configuration space obstacles, and N is the number of obstacles, decomposed into convex objects. This work builds upon the slabbing method proposed by Ahrikencheikh et al. [2], which finds an optimal motion for a point among a set of nonoverlapping obstacles. Here, we extend the slabbing method to the motion planning of a convex polygonal robot with translations and rotations, which also allows overlapping configuration space obstacles. This algorithm has been fully implemented and the experimental results show that it is more robust and faster than other approaches.

Hardness of Motion Planning with Obstacle Uncertainty in Two Dimensions

2021 ◽  
pp. 027836492199278
Author(s):  
Luke Shimanuki ◽  
Brian Axelrod
Keyword(s):  
Motion Planning ◽  
Free Path ◽  
Configuration Space ◽  
Exact Algorithm ◽  
Two Dimensions ◽  
Planning Under Uncertainty ◽  
Constant Number ◽  
3D Motion ◽  
Practical Algorithms ◽  
Case Planning

We consider the problem of motion planning in the presence of uncertain obstacles, modeled as polytopes with Gaussian-distributed faces (PGDFs). A number of practical algorithms exist for motion planning in the presence of known obstacles by constructing a graph in configuration space, then efficiently searching the graph to find a collision-free path. We show that such an exact algorithm is unlikely to be practical in the domain with uncertain obstacles. In particular, we show that safe 2D motion planning among PGDF obstacles is [Formula: see text]-hard with respect to the number of obstacles, and remains [Formula: see text]-hard after being restricted to a graph. Our reduction is based on a path encoding of MAXQHORNSAT and uses the risk of collision with an obstacle to encode variable assignments and literal satisfactions. This implies that, unlike in the known case, planning under uncertainty is hard, even when given a graph containing the solution. We further show by reduction from [Formula: see text]-SAT that both safe 3D motion planning among PGDF obstacles and the related minimum constraint removal problem remain [Formula: see text]-hard even when restricted to cases where each obstacle overlaps with at most a constant number of other obstacles.

ADD-RRV for motion planning in complex environments

Robotica ◽  
2021 ◽  
pp. 1-18
Author(s):  
Peng Cai ◽  
Xiaokui Yue ◽  
Hongwen Zhang
Keyword(s):  
Principal Component Analysis ◽  
Motion Planning ◽  
Configuration Space ◽  
Principal Component ◽  
Component Analysis ◽  
Planning Method ◽  
Complex Environments ◽  
Local Configuration ◽  
Narrow Passage

Abstract In this paper, we present a novel sampling-based motion planning method in various complex environments, especially with narrow passages. We use online the results of the planner in the ADD-RRT framework to identify the types of the local configuration space based on the principal component analysis (PCA). The identification result is then used to accelerate the expansion similar to RRV around obstacles and through narrow passages. We also propose a modified bridge test to identify the entrance of a narrow passage and boost samples inside it. We have compared our method with known motion planners in several scenarios through simulations. Our method shows the best performance across all the tested planners in the tested scenarios.

Optimal motion planning for localization of avalanche victims by multiple UAVs

2021 ◽  
pp. 1-1
Author(s):  
Camilla Tabasso ◽  
Nicola Mimmo ◽  
Venanzio Cichella ◽  
Lorenzo Marconi
Keyword(s):  
Motion Planning ◽  
Optimal Motion ◽  
Multiple Uavs ◽  
Optimal Motion Planning

MOTION PLANNING USING PREDICTED PERCEPTIVE CAPABILITY

2009 ◽  
Vol 06 (03) ◽  
pp. 435-457 ◽  
Author(s):  
PHILIPP MICHEL ◽  
JOEL CHESTNUTT ◽  
SATOSHI KAGAMI ◽  
KOICHI NISHIWAKI ◽  
JAMES J. KUFFNER ◽  
...  
Keyword(s):  
Motion Planning ◽  
Planning Process ◽  
Humanoid Robots ◽  
Experimental Results ◽  
Success Rates ◽  
Perception System ◽  
Task Success

We present an approach to motion planning for humanoid robots that aims to ensure reliable execution by augmenting the planning process to reason about the robot's ability to successfully perceive its environment during operation. By efficiently simulating the robot's perception system during search, our planner utilizes a perceptive capability metric that quantifies the 'sensability' of the environment in each state given the task to be accomplished. We have applied our method to the problem of planning robust autonomous grasping motions and walking sequences as performed by an HRP-2 humanoid. A fast GPU-accelerated 3D tracker is used for perception, with a grasp planner and footstep planner incorporating reasoning about the robot's perceptive capability. Experimental results show that considering information about the predicted perceptive capability ensures that sensing remains operational throughout the grasping or walking sequence and yields higher task success rates than perception-unaware planning.

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