scholarly journals A CooperativeQ-Learning Path Planning Algorithm for Origin-Destination Pairs in Urban Road Networks

2015 ◽  
Vol 2015 ◽  
pp. 1-10 ◽  
Author(s):  
Xiaoyong Zhang ◽  
Heng Li ◽  
Jun Peng ◽  
Weirong Liu
Keyword(s):  
Path Planning ◽  
Intelligent Transportation Systems ◽  
Optimal Path ◽  
Road Networks ◽  
Transportation Systems ◽  
Prediction Algorithm ◽  
Urban Road ◽  
Learning Path ◽  
Planning Algorithm ◽  
Path Planning Algorithm

As an important part of intelligent transportation systems, path planning algorithms have been extensively studied in the literature. Most of existing studies are focused on the global optimization of paths to find the optimal path between Origin-Destination (OD) pairs. However, in urban road networks, the optimal path may not be always available when some unknown emergent events occur on the path. Thus a more practical method is to calculate several suboptimal paths instead of finding only one optimal path. In this paper, a cooperativeQ-learning path planning algorithm is proposed to seek a suboptimal multipath set for OD pairs in urban road networks. The road model is abstracted to the form thatQ-learning can be applied firstly. Then the gray prediction algorithm is combined intoQ-learning to find the suboptimal paths with reliable constraints. Simulation results are provided to show the effectiveness of the proposed algorithm.

Research on Path Planning Algorithm in Intelligent Transportation

2014 ◽  
Vol 536-537 ◽  
pp. 833-836
Author(s):  
Jin Peng Tang ◽  
Ling Lin Li
Keyword(s):  
Path Planning ◽  
Intelligent Transportation Systems ◽  
Intelligent Transportation ◽  
Transportation Systems ◽  
Traffic Density ◽  
Advantages And Disadvantages ◽  
Key Factor ◽  
Planning Algorithm ◽  
Transportation Vehicle ◽  
Path Planning Algorithm

Path planning is a key factor in intelligent transportation vehicle navigation system, compared to a variety of advantages and disadvantages of the path planning optimization algorithm. According obstacle distribution characteristics similar characteristics to the grid circuit resistor, proposed a Circuit Map path planning algorithm. The city's roads each segment was mapped to the circuit network, and the resistance of the resistor were mapped to with the Weighted sum of road length and width, and traffic density. This can be achieved by solving the circuit that intelligent transportation systems path planning and navigation services.

scholarly journals Application of Angle Related Cost Function Optimization for Dynamic Path Planning Algorithm

Algorithms ◽  
2018 ◽  
Vol 11 (8) ◽  
pp. 127 ◽  
Author(s):  
Mingbin Zeng ◽  
Xu Yang ◽  
Mengxing Wang ◽  
Bangjiang Xu
Keyword(s):  
Path Planning ◽  
Real Time ◽  
Intelligent Transportation Systems ◽  
Optimal Solution ◽  
Transportation Systems ◽  
Function Optimization ◽  
Dynamic Path Planning ◽  
Planning Algorithm ◽  
Dynamic Path ◽  
Path Planning Algorithm

In recent years, Intelligent Transportation Systems (ITS) have developed a lot. More and more sensors and communication technologies (e.g., cloud computing) are being integrated into cars, which opens up a new design space for vehicular-based applications. In this paper, we present the Spatial Optimized Dynamic Path Planning algorithm. Our contributions are, firstly, to enhance the effective of loading mechanism for road maps by dividing the connected sub-net, and building a spatial index; and secondly, to enhance the effect of the dynamic path planning by optimizing the search direction. We use the real road network and real-time traffic flow data of Karamay city to simulate the effect of our algorithm. Experiments show that our Spatial Optimized Dynamic Path Planning algorithm can significantly reduce the time complexity, and is better suited for use as a real-time navigation system. The algorithm can achieve superior real-time performance and obtain the optimal solution in dynamic path planning.

scholarly journals A Novel Heuristic Emergency Path Planning Method Based on Vector Grid Map

2021 ◽  
Vol 10 (6) ◽  
pp. 370
Author(s):  
Bowen Yang ◽  
Jin Yan ◽  
Zhi Cai ◽  
Zhiming Ding ◽  
Dongze Li ◽  
...  
Keyword(s):  
Path Planning ◽  
Intelligent Transportation Systems ◽  
Road Network ◽  
Transportation Systems ◽  
The Road ◽  
Planning Framework ◽  
Planning Algorithm ◽  
Set Up ◽  
Planning Methodology ◽  
Path Planning Algorithm

Emergency path planning technology is one of the research hotspots of intelligent transportation systems. Due to the complexity of urban road networks and congested road conditions, emergency path planning is very difficult. Road congestion caused by urban emergencies directly affects the original road network structure. In this way, the static weight of the original road network is no longer suitable as the basis for path recommendation. To handle the dynamic situational road network, an equidistant grid emergency path planning framework will be designed. A novel situation grid road network model, based on situation information, is proposed and applied to an equidistant grid emergency path planning framework. A situational grid heuristic search will be proposed methodology based on this model, which can be used to detect the vehicles passing around the congestion area grid and the road to the destination in the shortest time. In the path planning methodology, a grid inspired search strategy based on quaternion function is included, which can make the algorithm converge to the target grid quickly. Three graph acceleration algorithms are proposed to improve the search efficiency of path planning algorithm. Finally, this paper will set up three experiments to verify our proposed method.

scholarly journals A 2D Optimal Path Planning Algorithm for Autonomous Underwater Vehicle Driving in Unknown Underwater Canyons

2021 ◽  
Vol 9 (3) ◽  
pp. 252
Author(s):  
Yushan Sun ◽  
Xiaokun Luo ◽  
Xiangrui Ran ◽  
Guocheng Zhang
Keyword(s):  
Path Planning ◽  
Obstacle Avoidance ◽  
Autonomous Underwater Vehicles ◽  
Optimal Path ◽  
Small Scale ◽  
Target Point ◽  
Safe Driving ◽  
Policy Gradient ◽  
Planning Algorithm ◽  
Path Planning Algorithm

This research aims to solve the safe navigation problem of autonomous underwater vehicles (AUVs) in deep ocean, which is a complex and changeable environment with various mountains. When an AUV reaches the deep sea navigation, it encounters many underwater canyons, and the hard valley walls threaten its safety seriously. To solve the problem on the safe driving of AUV in underwater canyons and address the potential of AUV autonomous obstacle avoidance in uncertain environments, an improved AUV path planning algorithm based on the deep deterministic policy gradient (DDPG) algorithm is proposed in this work. This method refers to an end-to-end path planning algorithm that optimizes the strategy directly. It takes sensor information as input and driving speed and yaw angle as outputs. The path planning algorithm can reach the predetermined target point while avoiding large-scale static obstacles, such as valley walls in the simulated underwater canyon environment, as well as sudden small-scale dynamic obstacles, such as marine life and other vehicles. In addition, this research aims at the multi-objective structure of the obstacle avoidance of path planning, modularized reward function design, and combined artificial potential field method to set continuous rewards. This research also proposes a new algorithm called deep SumTree-deterministic policy gradient algorithm (SumTree-DDPG), which improves the random storage and extraction strategy of DDPG algorithm experience samples. According to the importance of the experience samples, the samples are classified and stored in combination with the SumTree structure, high-quality samples are extracted continuously, and SumTree-DDPG algorithm finally improves the speed of the convergence model. Finally, this research uses Python language to write an underwater canyon simulation environment and builds a deep reinforcement learning simulation platform on a high-performance computer to conduct simulation learning training for AUV. Data simulation verified that the proposed path planning method can guide the under-actuated underwater robot to navigate to the target without colliding with any obstacles. In comparison with the DDPG algorithm, the stability, training’s total reward, and robustness of the improved Sumtree-DDPG algorithm planner in this study are better.

scholarly journals On Learning Path Planning Algorithm Based on Collaborative Analysis of Learning Behavior

IEEE Access ◽  
2020 ◽  
Vol 8 ◽  
pp. 119863-119879
Author(s):  
Zhaoyu Shou ◽  
Xianying Lu ◽  
Zhengzheng Wu ◽  
Hua Yuan ◽  
Huibing Zhang ◽  
...  
Keyword(s):  
Path Planning ◽  
Learning Behavior ◽  
Learning Path ◽  
Planning Algorithm ◽  
Collaborative Analysis ◽  
Path Planning Algorithm

Optimal Collision-Free Path Planning for an Autonomous Multi-Wheeled Combat Vehicle

2017 ◽  
Author(s):  
Amr Mohamed ◽  
Moustafa El-Gindy ◽  
Jing Ren ◽  
Haoxiang Lang
Keyword(s):  
Path Planning ◽  
Potential Field ◽  
Vehicle Dynamics ◽  
Autonomous Vehicle ◽  
Optimal Path ◽  
Vehicle Model ◽  
Optimal Path Planning ◽  
Combat Vehicle ◽  
Planning Algorithm ◽  
Path Planning Algorithm

This paper presents an optimal collision-free path planning algorithm of an autonomous multi-wheeled combat vehicle using optimal control theory and artificial potential field function (APF). The optimal path of the autonomous vehicle between a given starting and goal points is generated by an optimal path planning algorithm. The cost function of the path planning is solved together with vehicle dynamics equations to satisfy the vehicle dynamics constraints and the boundary conditions. For this purpose, a simplified four-axle bicycle model of the actual vehicle considering the vehicle body lateral and yaw dynamics while neglecting roll dynamics is used. The obstacle avoidance technique is mathematically modeled based on the proposed sigmoid function as the artificial potential field method. This potential function is assigned to each obstacle as a repulsive potential field. The inclusion of these potential fields results in a new APF which controls the steering angle of the autonomous vehicle to reach the goal point. A full nonlinear multi-wheeled combat vehicle model in TruckSim software is used for validation. This is done by importing the generated optimal path data from the introduced optimal path planning MATLAB algorithm and comparing lateral acceleration, yaw rate and curvature at different speeds (9 km/h, 28 km/h) for both simplified and TruckSim vehicle model. The simulation results show that the obtained optimal path for the autonomous multi-wheeled combat vehicle satisfies all vehicle dynamics constraints and successfully validated with TruckSim vehicle model.

scholarly journals NPQ-RRT ∗ : An Improved RRT ∗ Approach to Hybrid Path Planning

Complexity ◽  
2021 ◽  
Vol 2021 ◽  
pp. 1-10
Author(s):  
Zihan Yu ◽  
Linying Xiang
Keyword(s):  
Path Planning ◽  
Optimal Path ◽  
Research Direction ◽  
Random Trees ◽  
Target Point ◽  
Evaluation Function ◽  
Local Planning ◽  
Planning Algorithm ◽  
Window Approach ◽  
Path Planning Algorithm

In recent years, the path planning of robot has been a hot research direction, and multirobot formation has practical application prospect in our life. This article proposes a hybrid path planning algorithm applied to robot formation. The improved Rapidly Exploring Random Trees algorithm PQ-RRT ∗ with new distance evaluation function is used as a global planning algorithm to generate the initial global path. The determined parent nodes and child nodes are used as the starting points and target points of the local planning algorithm, respectively. The dynamic window approach is used as the local planning algorithm to avoid dynamic obstacles. At the same time, the algorithm restricts the movement of robots inside the formation to avoid internal collisions. The local optimal path is selected by the evaluation function containing the possibility of formation collision. Therefore, multiple mobile robots can quickly and safely reach the global target point in a complex environment with dynamic and static obstacles through the hybrid path planning algorithm. Numerical simulations are given to verify the effectiveness and superiority of the proposed hybrid path planning algorithm.

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