A Path Planning Method for Underground Intelligent Vehicles Based on an Improved RRT* Algorithm

Wang, Hao; Li, Guoqing; Hou, Jie; Chen, Lianyun; Hu, Nailian

doi:10.3390/electronics11030294

Cited by 49 publications

(35 citation statements)

References 37 publications

(38 reference statements)

Supporting

Mentioning

Contrasting

Order By: Relevance

“…A set of benchmarking parameters is defined to objectively compare the performance of the improved heuristic Bi-RRT and some RRT variants. The number of nodes on the mature random tree is denoted by “tree nodes.” The length of the generated path is denoted by “path length.” The searching time of path planning is denoted by “time.” In addition, the number of segments comprising the generated path is donated by “path segments”; in particular, the path segments of the improved heuristic RRT refer to the number of segments of the path after being processed by path reconnection [ 30 , 43 , 44 ].…”

Section: Simulation Results and Analysismentioning

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

View full text Add to dashboard Cite

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.

show abstract

Section: Simulation Results and Analysismentioning

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

View full text Add to dashboard Cite

show abstract

“…Therefore, we propose a tree expansion method with a dynamic step size. The dynamic step method proposed in the current literature (Wang et al, 2022a;Zhang et al, 2022a;Yang et al, 2021) only dynamically changes the step size in the expansion of the tree depending on whether the current node collides with an obstacle or not. If no collision occurs, the step size is expanded, and if a collision occurs, the new node is re-found and expanded with a small step size.…”

Section: Dynamic Variable Step Samplingmentioning

confidence: 99%

“…Therefore, we propose a tree expansion method with a dynamic step size. The dynamic step method proposed in the current literature (Wang et al. , 2022a; Zhang et al.…”

Section: Qgd-rrt Algorithm Frameworkmentioning

confidence: 99%

Research on path planning of autonomous vehicle based on RRT algorithm of Q-learning and obstacle distribution

Shang

Liu

Qin

et al. 2023

View full text Add to dashboard Cite

PurposeThe goal of this research is to develop a dynamic step path planning algorithm based on the rapidly exploring random tree (RRT) algorithm that combines Q-learning with the Gaussian distribution of obstacles. A route for autonomous vehicles may be swiftly created using this algorithm.Design/methodology/approachThe path planning issue is divided into three key steps by the authors. First, the tree expansion is sped up by the dynamic step size using a combination of Q-learning and the Gaussian distribution of obstacles. The invalid nodes are then removed from the initially created pathways using bidirectional pruning. B-splines are then employed to smooth the predicted pathways.FindingsThe algorithm is validated using simulations on straight and curved highways, respectively. The results show that the approach can provide a smooth, safe route that complies with vehicle motion laws.Originality/valueAn improved RRT algorithm based on Q-learning and obstacle Gaussian distribution (QGD-RRT) is proposed for the path planning of self-driving vehicles. Unlike previous methods, the authors use Q-learning to steer the tree's development direction. After that, the step size is dynamically altered following the density of the obstacle distribution to produce the initial path rapidly and cut down on planning time even further. In the aim to provide a smooth and secure path that complies with the vehicle kinematic and dynamical restrictions, the path is lastly optimized using an enhanced bidirectional pruning technique.

show abstract

“…In the application process, the excessively frequent charging and discharging behaviors in the V2G process will cause battery loss of EVs, thus affecting the interests of users. In order to improve user-side requirements and achieve optimization, this paper proposes a definition called battery loss satisfaction [20,21]:…”

Section: Battery Depletion Modelmentioning

confidence: 99%

A Two-Level Integrated Scheduling Strategy for Vehicle-Network Synergy considering New Energy Consumption

Gao

2022

Mathematical Problems in Engineering

View full text Add to dashboard Cite

With the substantial increase in the number of electric vehicles, the charging of electric vehicles without regulation and scale control will bring about problems, such as overloading of distribution transformers; the proportion of new energy power generation is also increasing year by year, and the access of new energy to the power grid will cause volatility. In order to solve the problems above, this paper proposed a coordinated and orderly scheduling strategy considering new energy consumption, which protects the interests of both users and the integrated power grid. First, a two-level vehicle-network interaction model considering both supply and demand sides was established. The upper-level model optimized the indicators on the distribution grid side, and a term of charge-discharge margin as well as grid-side load variance model was proposed. The lower-level optimization model was set based on the users’ condition. The average discharge rate index was defined to evaluate the battery loss satisfaction in the scheduling strategy, which fully considered users’ charging and discharging cost, and finally achieved a win-win situation between the power grid and the user. Secondly, the fast nondominated sorting genetic algorithm (NSGA-II) was used to figure out the effect of the strategy proposed in this paper, and a community is taken as an example for simulation. The results confirmed the economy and rationality of the above strategy, by rationally scheduling the charging and discharging behavior of electric vehicles, consuming new energy, restraining the fluctuation of the remaining new energy power generation, realizing the dynamic balance between the charging and discharging load and the output of new energy in a certain area, and finally effectively suppressing the fluctuation of the power grid load while improving the availability of clean energy.

show abstract

A Path Planning Method for Underground Intelligent Vehicles Based on an Improved RRT* Algorithm

Cited by 49 publications

References 37 publications

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

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

Research on path planning of autonomous vehicle based on RRT algorithm of Q-learning and obstacle distribution

A Two-Level Integrated Scheduling Strategy for Vehicle-Network Synergy considering New Energy Consumption

Contact Info

Product

Resources

About