Efficient Two-phase 3D Motion Planning for Small Fixed-wing UAVs

2007 ◽  
Author(s):  
Myung Hwangbo ◽  
James Kuffner ◽  
Takeo Kanade
Keyword(s):  
Motion Planning ◽  
Two Phase ◽  
3D Motion

scholarly journals Automated 3D-motion planning for ramps and stairs in intra-logistics material flow simulations

2010 ◽  
Author(s):  
Matthias Fischer ◽  
Hendrik Renken ◽  
Christoph Laroque ◽  
Guido Schaumann ◽  
Wilhelm Dangelmaier
Keyword(s):  
Motion Planning ◽  
Material Flow ◽  
3D Motion ◽  
Flow Simulations

scholarly journals A 3D motion planning framework for snake robots

2014 ◽  
Author(s):  
Pal Liljeback ◽  
Kristin Y. Pettersen ◽  
Oyvind Stavdahl ◽  
Jan Tommy Gravdahl
Keyword(s):  
Motion Planning ◽  
3D Motion ◽  
Planning Framework ◽  
Snake Robots

Two-phase A*: A real-time global motion planning method for non-holonomic unmanned ground vehicles

2020 ◽  
pp. 095440702094839
Author(s):  
Kai Zhang ◽  
Yi Yang ◽  
Mengyin Fu ◽  
Meiling Wang
Keyword(s):  
Motion Planning ◽  
Real Time ◽  
Optimal Path ◽  
Computation Time ◽  
Planning Method ◽  
Global Motion ◽  
Two Phase ◽  
Holonomic Constraints ◽  
Planning Cycle ◽  
Indoor And Outdoor Environments

This paper presents a search-based global motion planning method, called the two-phase A*, with an adaptive heuristic weight. This method is suitable for planning a global path in real time for a car-like vehicle in both indoor and outdoor environments. In each planning cycle, the method first estimates a proper heuristic weight based on the hardness of the planning query. Then, it finds a nearly optimal path subject to the non-holonomic constraints using an improved A* with a weighted heuristic function. By estimating the heuristic weight dynamically, the two-phase A* is able to adjust the optimality level of its path based on the hardness of the planning query. Therefore, the two-phase A* sacrifices little planning optimality, and its computation time is acceptable in most situations. The two-phase A* has been implemented and tested in the simulations and real-world experiments over various task environments. The results show that the two-phase A* can generate a nearly optimal global path dynamically, which satisfies the non-holonomic constraints of a car-like vehicle and reduces the total navigation time.

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.

3D Motion Planning for Robot-Assisted Active Flexible Needle Based on Rapidly-Exploring Random Trees

2015 ◽  
pp. 360-367 ◽  
Author(s):  
Yan-Jiang Zhao ◽  
Bardia Konh ◽  
Mohammad Honarvar ◽  
Felix Orlando Maria Joseph ◽  
Tarun K. Podder ◽  
...  
Keyword(s):  
Motion Planning ◽  
Random Trees ◽  
Robot Assisted ◽  
3D Motion ◽  
Rapidly Exploring Random Trees

3D motion planning for UAVs in GPS-denied unknown forest environment

2016 ◽  
Author(s):  
Fang Liao ◽  
Shupeng Lai ◽  
Yuchao Hu ◽  
Jinqiang Cui ◽  
Jian Liang Wang ◽  
...  
Keyword(s):  
Motion Planning ◽  
Forest Environment ◽  
3D Motion
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