An Efficient RRT-Based Framework for Planning Short and Smooth Wheeled Robot Motion Under Kinodynamic Constraints

Hu, Bi Ying; Cao, Zhengcai; Zhou, MengChu

doi:10.1109/tie.2020.2978701

Cited by 50 publications

(23 citation statements)

References 36 publications

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“…Chang et al [ 9 ] proposed an improved DWA, which improved the success rate of paths in unknown environment by optimizing and adding evaluation functions. Hu et al [ 10 ] proposed an efficient RRT-based framework to generate shorter and smoother paths with less computation. Qi et al [ 11 ] proposed a MOD-RRT ∗ , which is composed of path generation and path planning, and it is suitable for robot navigation in unknown dynamic environment.…”

Section: Related Workmentioning

confidence: 99%

Intelligent Optimization Algorithm‐Based Path Planning for a Mobile Robot

Song

Yang

et al. 2021

Computational Intelligence and Neuroscience

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The purpose of mobile robot path planning is to produce the optimal safe path. However, mobile robots have poor real-time obstacle avoidance in local path planning and longer paths in global path planning. In order to improve the accuracy of real-time obstacle avoidance prediction of local path planning, shorten the path length of global path planning, reduce the path planning time, and then obtain a better safe path, we propose a real-time obstacle avoidance decision model based on machine learning (ML) algorithms, an improved smooth rapidly exploring random tree (S-RRT) algorithm, and an improved hybrid genetic algorithm-ant colony optimization (HGA-ACO). Firstly, in local path planning, the machine learning algorithms are used to train the datasets, the real-time obstacle avoidance decision model is established, and cross validation is performed. Secondly, in global path planning, the greedy algorithm idea and B-spline curve are introduced into the RRT algorithm, redundant nodes are removed, and the reverse iteration is performed to generate a smooth path. Then, in path planning, the fitness function and genetic operation method of genetic algorithm are optimized, the pheromone update strategy and deadlock elimination strategy of ant colony algorithm are optimized, and the genetic-ant colony fusion strategy is used to fuse the two algorithms. Finally, the optimized path planning algorithm is used for simulation experiment. Comparative simulation experiments show that the random forest has the highest real-time obstacle avoidance prediction accuracy in local path planning, and the S-RRT algorithm can effectively shorten the total path length generated by the RRT algorithm in global path planning. The HGA-ACO algorithm can reduce the iteration number reasonably, reduce the search time effectively, and obtain the optimal solution in path planning.

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Section: Related Workmentioning

confidence: 99%

Intelligent Optimization Algorithm‐Based Path Planning for a Mobile Robot

Song

Yang

et al. 2021

Computational Intelligence and Neuroscience

View full text Add to dashboard Cite

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“…In recent years, the most representative and common path planning algorithms in the field are mainly divided into neural network algorithm [3], artificial potential field algorithm [4], rapidly expanding random tree algorithm [5], ant colony algorithm [6], and A * algorithm [7,8]. In particular, the A * algorithm is a heuristic search algorithm; because of its strong global search ability, high search efficiency, and shortest path, it is widely used.…”

Section: Introductionmentioning

confidence: 99%

Research on Global Dynamic Path Planning Method Based on Improved A $^{*}$ Algorithm

Niu

Xin

et al. 2021

Mathematical Problems in Engineering

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Aiming at the optimal path and planning efficiency of global path planning for intelligent driving, this paper proposes a global dynamic path planning method based on improved A ∗ algorithm. First, this method improves the heuristic function of the traditional A ∗ algorithm to improve the efficiency of global path planning. Second, this method uses a path optimization strategy to make the global path smoother. Third, this method is combined with the dynamic window method to improve the real-time performance of the dynamic obstacle avoidance of the intelligent vehicle. Finally, the global dynamic path planning method of the proposed improved A ∗ algorithm is verified through simulation experiments and real vehicle tests. In the simulation analysis, compared with the modified A ∗ algorithm and the traditional A ∗ algorithm, the method in this paper shortens the path distance by 2.5%∼3.0%, increases the efficiency by 10.3%∼13.6% and generates a smoother path. In the actual vehicle test, the vehicle can avoid dynamic obstacles in real time. Therefore, the method proposed in this paper can be applied on the intelligent vehicle platform. The path planning efficiency is high, and the dynamic obstacle avoidance is good in real time.

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“…Conventional RRTs showed many limitations mainly because the resultant RRT paths were generated without reflecting nonholonomic constraints. Recently, RRTs under the consideration of kinodynamic constraints have been proposed, as in [ 15 , 16 , 17 , 18 , 19 ].…”

Section: Introductionmentioning

confidence: 99%

Trajectory Planner CDT-RRT* for Car-Like Mobile Robots toward Narrow and Cluttered Environments

Kwon

Cha

Seong

et al. 2021

Sensors

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In order to achieve the safe and efficient navigation of mobile robots, it is essential to consider both the environmental geometry and kinodynamic constraints of robots. We propose a trajectory planner for car-like robots on the basis of the Dual-Tree RRT (DT-RRT). DT-RRT utilizes two tree structures in order to generate fast-growing trajectories under the kinodynamic constraints of robots. A local trajectory generator has been newly designed for car-like robots. The proposed scheme of searching a parent node enables the efficient generation of safe trajectories in cluttered environments. The presented simulation results clearly show the usefulness and the advantage of the proposed trajectory planner in various environments.

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An Efficient RRT-Based Framework for Planning Short and Smooth Wheeled Robot Motion Under Kinodynamic Constraints

Cited by 50 publications

References 36 publications

Intelligent Optimization Algorithm‐Based Path Planning for a Mobile Robot

Intelligent Optimization Algorithm‐Based Path Planning for a Mobile Robot

Research on Global Dynamic Path Planning Method Based on Improved A $^{*}$ Algorithm

Trajectory Planner CDT-RRT* for Car-Like Mobile Robots toward Narrow and Cluttered Environments

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An Efficient RRT-Based Framework for Planning Short and Smooth Wheeled Robot Motion Under Kinodynamic Constraints

Cited by 50 publications

References 36 publications

Intelligent Optimization Algorithm‐Based Path Planning for a Mobile Robot

Intelligent Optimization Algorithm‐Based Path Planning for a Mobile Robot

Research on Global Dynamic Path Planning Method Based on Improved A ∗ Algorithm

Trajectory Planner CDT-RRT* for Car-Like Mobile Robots toward Narrow and Cluttered Environments

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Product

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Research on Global Dynamic Path Planning Method Based on Improved A $^{*}$ Algorithm