Development of an Improved Rapidly Exploring Random Trees Algorithm for Static Obstacle Avoidance in Autonomous Vehicles

Yang, Shih-Ming; Lin, Yu-Yuan

doi:10.3390/s21062244

Cited by 18 publications

(9 citation statements)

References 37 publications

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“…For a robotic arm, the redundant turning points will cause unnecessary energy loss, cause wear and reduce the service life of the robotic arm. Therefore, this article adopts an operation to remove redundant points [28,29]. The redundant node removal operation is performed on the generated path.…”

Section: Redundant Node Deletion Operationmentioning

confidence: 99%

Path planning of a manipulator based on an improved P_RRT* algorithm

Yuan

Sun

et al. 2022

Complex Intell. Syst.

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Aiming to build upon the slow convergence speed and low search efficiency of the potential function-based rapidly exploring random tree star (RRT*) algorithm (P_RRT*), this paper proposes a path planning method for manipulators with an improved P_RRT* algorithm (defined as improved P_RRT*), which is used to solve the path planning problem for manipulators in three-dimensional space. This method first adopts a random sampling method based on a potential function. Second, based on a probability value, the nearest neighbour node is selected by the nearest Euclidean distance to the random sampling point and the minimum cost function, and in the expansion of new nodes, twice expansion methods are used to accelerate the search efficiency of the algorithm. The first expansion adopts the goal-biased expansion strategy, and the second expansion adopts the strategy of random sampling in a rectangular area. Then, the parent node of the new node is reselected, and the path is rerouted to obtain a clear path from the initial point to the target point. Redundant node deletion and the maximum curvature constraint are used to remove redundant nodes and minimize the curvature on the generated path to reduce the tortuosity of the path. The Bezier curve is used to fit the processed path and obtain the trajectory planning curve for the manipulator. Finally, the improved P_RRT* algorithm is verified experimentally in Python and the Robot Operating System (ROS) and compared with other algorithms. The experimental results verify the effectiveness and superiority of the improved algorithm.

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Section: Redundant Node Deletion Operationmentioning

confidence: 99%

Path planning of a manipulator based on an improved P_RRT* algorithm

Yuan

Sun

et al. 2022

Complex Intell. Syst.

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“…It is to solve continuous and discrete optimization problem. Global and local path planning Advantages: Suitable for large datasets and dynamic environment Solving capacitated vehicle routing problem Limitation: Probability of being trapped in local minima Energy-efficient green Ant Colony Optimization (Sangeetha et al 2021 ) Collective behaviour of trail-laying ants for finding shortest path Advantages: Distributed search to avoid local minima, greedy heuristics Path planning in dynamic 3D environments Limitation: Slow convergence speed Improved Rapidly Rapidly-Exploring Random Trees (Yang and Lin 2021 ) Expand on all regions based on weights and create path Advantages: It works dynamically and does not require a prior path, can handle constraints that are non-integrable into positional constraints Static obstacle avoidance in autonomous vehicles Limitation: Low accuracy D* lite Algorithm (Yao et al 2021 ) Path planning in partially known and dynamic environment Advantages: Path cost optimization planning, where the cost changes dynamically Efficient path planning for Unmanned Surface Vehicles in complex environments Limitation: High memory consumption A* (Foead et al 2021 ) Similar to Dijkstra, but guides the agent towards the next promising node Advantages: Simpler and computationally effective Shortest path Limitation: Trade-off between speed and accuracy Dijkstra Algorithm (Akram et al 2021 ) Single source shortest path Advantages: Works well in an acyclic environment Finding shortest paths in networks Limitation: Does not keep track of all the nodes previously travelled. Single source only and it requires more memory CSO-ALO (Deb and Gao 2021 ) ...…”

Section: State-of-the Artmentioning

confidence: 99%

Optimal energy efficient path planning of UAV using hybrid MACO-MEA* algorithm: theoretical and experimental approach

Esakki

Elangovan

Tamilarasan

et al. 2022

J Ambient Intell Human Comput

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Autonomous mission capabilities with optimal path are stringent requirements for Unmanned Aerial Vehicle (UAV) navigation in diverse applications. The proposed research framework is to identify an energy-efficient optimal path to achieve the designated missions for the navigation of UAVs in various constrained and denser obstacle prone regions. Hence, the present work is aimed to develop an optimal energy-efficient path planning algorithm through combining well known modified ant colony optimization algorithm (MACO) and a variant of A*, namely the memory-efficient A* algorithm (MEA*) for avoiding the obstacles in three dimensional (3D) environment and arrive at an optimal path with minimal energy consumption. The novelty of the proposed method relies on integrating the above two efficient algorithms to optimize the UAV path planning task. The basic design of this study is, that by utilizing an improved version of the pheromone strategy in MACO, the local trap and premature convergence are minimized, and also an optimal path is found by means of reward and penalty mechanism. The sole notion of integrating the MEA* algorithm arises from the fact that it is essential to overcome the stringent memory requirement of conventional A* algorithm and to resolve the issue of tracking only the edges of the grids. Combining the competencies of MACO and MEA*, a hybrid algorithm is proposed to avoid obstacles and find an efficient path. Simulation studies are performed by varying the number of obstacles in a 3D domain. The real-time flight trials are conducted experimentally using a UAV by implementing the attained optimal path. A comparison of the total energy consumption of UAV with theoretical analysis is accomplished. The significant finding of this study is that, the MACO-MEA* algorithm achieved 21% less energy consumption and 55% shorter execution time than the MACO-A*. moreover, the path traversed in both simulation and experimental methods is 99% coherent with each other. it confirms that the developed hybrid MACO-MEA* energy-efficient algorithm is a viable solution for UAV navigation in 3D obstacles prone regions.

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“…Lu et al utilized Dubins curves to generate a path, but the curvature of the generated path is discontinuous [ 38 ]. Yang et al used path pruning to delete unnecessary path nodes without considering the included angles between line segments between path nodes, resulting in excessive curvature of the final planned path [ 39 ]. Chen et al adopted path pruning based on inserted points to solve the initial path without considering the influence of inserted points on the path length, causing the length of the final planned path may not be optimal [ 29 ].…”

Section: Introductionmentioning

confidence: 99%

Local Path Planning of Autonomous Vehicle Based on an Improved Heuristic Bi-RRT Algorithm in Dynamic Obstacle Avoidance Environment

Zhang

Zhu

et al. 2022

Sensors

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The existing variants of the rapidly exploring random tree (RRT) cannot be effectively applied in local path planning of the autonomous vehicle and solve the coherence problem of paths between the front and back frames. Thus, an improved heuristic Bi-RRT algorithm is proposed, which is suitable for obstacle avoidance of the vehicle in an unknown dynamic environment. The vehicle constraint considering the driver’s driving habit and the obstacle-free direct connection mode of two random trees are introduced. Multi-sampling biased towards the target state reduces invalid searches, and parent node selection with the comprehensive measurement index accelerates the algorithm’s execution while making the initial path gentle. The adaptive greedy step size, introducing the target direction, expands the node more effectively. Moreover, path reorganization minimizes redundant path points and makes the path’s curvature continuous, and path coherence makes paths between the frames connect smoothly. Simulation analysis clarifies the efficient performance of the proposed algorithm, which can generate the smoothest path within the shortest time compared with the other four algorithms. Furthermore, the experiments on dynamic environments further show that the proposed algorithm can generate a differentiable coherence path, ensuring the ride comfort and stability of the vehicle.

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Development of an Improved Rapidly Exploring Random Trees Algorithm for Static Obstacle Avoidance in Autonomous Vehicles

Cited by 18 publications

References 37 publications

Path planning of a manipulator based on an improved P_RRT* algorithm

Path planning of a manipulator based on an improved P_RRT* algorithm

Optimal energy efficient path planning of UAV using hybrid MACO-MEA* algorithm: theoretical and experimental approach

Local Path Planning of Autonomous Vehicle Based on an Improved Heuristic Bi-RRT Algorithm in Dynamic Obstacle Avoidance Environment

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