Goal-biased Bidirectional RRT based on Curve-smoothing

Liu, Haoyue; Zhang, Xuebo; Wen, Jian; Wang, Runhua; Chen, Xiang

doi:10.1016/j.ifacol.2019.12.417

Cited by 29 publications

(16 citation statements)

References 7 publications

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“…Bi-directional search method is a simple and effective idea to overcome the limitation that the number of states increases exponentially in large scale or complex environments [23]. The bi-directional RRT algorithm simultaneously expands two RRT trees towards each other from the start state x start and goal state x goal , respectively.…”

Section: Bi-direction Extensionmentioning

confidence: 99%

Path Planning for Automatic Guided Vehicles (AGVs) Fusing MH-RRT with Improved TEB

2021

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In this paper, an AGV path planning method fusing multiple heuristics rapidly exploring random tree (MH-RRT) with an improved two-step Timed Elastic Band (TEB) is proposed. The modified RRT integrating multiple heuristics can search a safer, optimal and faster converge global path within a short time, and the improved TEB can optimize both path smoothness and path length. The method is composed of a global path planning procedure and a local path planning procedure, and the Receding Horizon Planning (RHP) strategy is adopted to fuse these two modules. Firstly, the MH-RRT is utilized to generate a state tree structure as prior knowledge, as well as the global path. Then, a receding horizon window is established to select the local goal point. On this basis, an improved two-step TEB is designed to optimize the local path if the current global path is feasible. Various simulations both on static and dynamic environments are conducted to clarify the performance of the proposed MH-RRT and the improved two-step TEB. Furthermore, real applicative experiments verified the effectiveness of the proposed approach.

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Section: Bi-direction Extensionmentioning

confidence: 99%

Path Planning for Automatic Guided Vehicles (AGVs) Fusing MH-RRT with Improved TEB

2021

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“…Although these algorithms although provide fast convergence rates, they cannot be used if the 2-point BVP cannot be solved (or in practice when doing so is computationally expensive). The existing bidirectional algorithms [13][14] that do not require solving 2-point BVP, instead require extra computation to connect the discontinuity that exists between the two trees, e.g., using methods like point perturbation [13] or Bézier curves [14]. Our approach avoids the tree-tree connection problem entirely, by using the heuristic provided by the reverse search tree to quickly move towards the goal.…”

Section: Related Workmentioning

confidence: 99%

“…This is due to memory constraints involved in storing the maneuvers. On the other end, existing bidirectional approaches that do not require solving the two point-BVP for building the trees [13] suffer from discontinuities when connecting them and additional methods like curve-smoothing [14] have been employed to remove the discontinuities which are non-trivial.…”

Section: Introductionmentioning

confidence: 99%

Bidirectional Sampling Based Search Without Two Point Boundary Value Solution

Nayak¹,

Otte²

2020

Preprint

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Bidirectional path and motion planning approaches decrease planning time, on average, compared to their unidirectional counterparts. In the context of single-query feasible motion planning, using bidirectional search to find a continuous motion plan requires an explicit connection between the forward search tree and the reverse search tree. Such a tree-tree connection requires solving a two-point Boundary Value Problem (BVP). However, two-point BVP solution can be difficult or impossible to calculate for many types of vehicles (using numerical methods to find a solution, such as shooting approaches may be computationally expensive and is sometimes numerically unstable). To overcome this challenge, we present a generalized bidirectional search algorithm that does not require solving two-point BVP. Instead of connecting the two trees directly, our algorithm uses the cost information of the reverse tree as a guiding heuristic for forward search. This enables the forward search to quickly converge to a full feasible solution without an explicit tree-tree connection and without the solution to a two-point BVP. We run multiple software simulations in different environments and using dynamics of different vehicles along with real-world hardware experiments to show that our approach performs very close or better than existing state of the art approaches in terms of quickly converging to an initial feasible solution.

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“…e RRT algorithm is widely used because of the advantage that it is suitable to solve the path planning problem under dynamic and multiobstacle conditions. However, the basic RRT algorithm has the disadvantage that the convergence speed and the search efficiency are low and the planned path is difficult to be the optimum [10,11]. Regarding the disadvantage of the RRT algorithm, researchers have carried out a lot of improvements.…”

Section: Introductionmentioning

confidence: 99%

Improved Bidirectional RRT $^{*}$ Path Planning Method for Smart Vehicle

et al. 2021

Mathematical Problems in Engineering

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In this paper, an improved bidirectional RRT ∗ vehicle path planning method for smart vehicle is proposed. In this method, the resultant force of the artificial potential field is used to determine the search direction to improve the search efficiency. Different kinds of constraints are considered in the method, including the vehicle constraints and the vehicle driving environment constraints. The collision detection based on separating axis theorem is used to detect the collision between the vehicle and the obstacles to improve the planning efficiency. The cubic B-spline curve is used to optimize the path to make the path’s curvature continuous. Both simulation and experiment are implemented to verify the proposed improved bidirectional RRT ∗ method. In the simulation analysis, this paper’s method can generate the smoothest path and takes the shortest time compared with the other two methods and it can be adaptive to the complicated environment. In the real vehicle experiment, we can see from the test results that this paper’s method can be applied in practice on the smart electric vehicle platform; compared with others’ algorithm, this paper’s algorithm can generate shortest and smoothest path.

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Goal-biased Bidirectional RRT based on Curve-smoothing

Cited by 29 publications

References 7 publications

Path Planning for Automatic Guided Vehicles (AGVs) Fusing MH-RRT with Improved TEB

Path Planning for Automatic Guided Vehicles (AGVs) Fusing MH-RRT with Improved TEB

Bidirectional Sampling Based Search Without Two Point Boundary Value Solution

Improved Bidirectional RRT $^{*}$ Path Planning Method for Smart Vehicle

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Goal-biased Bidirectional RRT based on Curve-smoothing

Cited by 29 publications

References 7 publications

Path Planning for Automatic Guided Vehicles (AGVs) Fusing MH-RRT with Improved TEB

Path Planning for Automatic Guided Vehicles (AGVs) Fusing MH-RRT with Improved TEB

Bidirectional Sampling Based Search Without Two Point Boundary Value Solution

Improved Bidirectional RRT ∗ Path Planning Method for Smart Vehicle

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Product

Resources

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Improved Bidirectional RRT $^{*}$ Path Planning Method for Smart Vehicle