RRTCAP∗ - RRT∗ Controller and Planner - Simultaneous Motion and Planning

Heβ, Robin; Lindeholz, Tobias; Eck, Daniel; Schilling, Klaus

doi:10.1016/j.ifacol.2015.08.107

Cited by 1 publication

(6 citation statements)

References 9 publications

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“…x y v v are the coordinate components of the velocity vector of the robot. The angle difference is compared to an angle threshold using Equation (14): θ represents the angle threshold to determine the replanning action. While it is easy to obtain the position of the robot for the computations, it is difficult to obtain the ( , ) x y position of the obstacle in the real environment for the computations in Equations (12) and (13).…”

Section: Step 5: Path Replanningmentioning

confidence: 99%

“…x y v v are the coordinate components of the velocity vector of the robot. The angle difference is compared to an angle threshold using Equation (14): the robot to the obstacle. The sensors track obstacles during navigation.…”

Section: Figurementioning

confidence: 99%

“…Roadmap path planning methods are gaining popular-Information Technology and Control 2019/2/48 180 ity in addressing mobile robot path planning problems [20]. Notable among these methods include probabilistic roadmap (PRM) [33], voronoi diagram (VD) [4,5] and rapidly exploring random tree (RRT) path planning methods [1,7,9,13,14,24,25,34,37,39]. Consideration is given to RRT path planning in this paper.…”

Section: Introductionmentioning

confidence: 99%

“…RRT is a single query incremental algorithm [22] that generates a tree in a free configuration space until the defined target is reached [7,18]. RRT is described as a fast path planning method that performs well in complex and high dimensional workspace [14,39]. RRT is a good method for motion planning for mobile robots because of its strength in controlling inputs computation [13].…”

Section: Introductionmentioning

confidence: 99%

“…The direction vector of the obstacle , obs θ and the velocity vector of the robot , the coordinate components of the velocity vector of the robot. The angle difference is compared to an angle threshold using Equation(14):…”

mentioning

confidence: 99%

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Optimized RRT-A* Path Planning Method for Mobile Robots in Partially Known Environment

Ayawli

Mei

Shen

et al. 2019

ITC

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This paper presents optimized rapidly exploring random trees A* (ORRT-A*) method to improve the performance of RRT-A* method to compute safe and optimal path with low time complexity for autonomous mobile robots in partially known complex environments. ORRT-A* method combines morphological dilation, goal-biased RRT, A* and cubic spline algorithms. Goal-biased RRT is modified by introducing additional step-size to speed up the generation of the tree towards the goal after which A* is applied to obtain the shortest path. Morphological dilation technique is used to provide safety for the robots while cubic spline interpolation is used to smoothen the path for easy navigation. Results indicate that ORRT-A* method demonstrates improved path quality compared to goal-biased RRT and RRT-A* methods. ORRT-A* is therefore a promising method in achieving autonomous ground vehicle navigation in unknown environments

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Section: Step 5: Path Replanningmentioning

confidence: 99%

Section: Figurementioning

confidence: 99%