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.

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 Research on optimal path planning of indoor robot based on wireless sensor

2021 ◽  
Vol 2093 (1) ◽  
pp. 012009
Author(s):  
Shouwen Wang
Keyword(s):  
Path Planning ◽  
Wireless Sensors ◽  
Optimal Path ◽  
Wireless Sensor ◽  
Target Point ◽  
Optimal Path Planning ◽  
Work Tasks ◽  
Planning Algorithm ◽  
Simulation Results ◽  
Path Planning Algorithm

Abstract Based on the work tasks and positioning characteristics of indoor robots, the environment is divided into grids, and wireless sensors are used to detect obstacles, and the density of obstacles in each grid is given. At the same time, the path planning algorithm is combined to realize the optimal path planning of indoor robot. The simulation results show that the wireless sensor network can realize the obstacle density detection, so that the robot can achieve fast optimal path planning and reach the target point.

scholarly journals Path planning for automatic guided vehicle with multiple target points in dynamic environment

2018 ◽  
Vol 159 ◽  
pp. 02029 ◽  
Author(s):  
Chang Kyu Kim ◽  
Huy Hung Nguyen ◽  
Dae Hwan Kim ◽  
Hak Kyeong Kim ◽  
Sang Bong Kim
Keyword(s):  
Path Planning ◽  
Shortest Paths ◽  
Initial Point ◽  
Target Point ◽  
Laser Sensor ◽  
Multiple Target ◽  
Hamilton Path ◽  
Automatic Guided Vehicle ◽  
Planning Algorithm ◽  
Path Planning Algorithm

In path planning field, Automatic guided vehicle (AGV) has to move from an initial point towards a target point with capability to avoid obstacles. There are A*, D* and D* lite path planning algorithms in the path planning algorithm. This paper proposes a modified D* lite path planning algorithm using the most efficient D* lite among these algorithms. The modified D* lite path planning algorithm is to improve these D* lite path planning algorithm’s weaknesses such as traversing across obstacles sharp corners, or traversing between two obstacles. To do this task, the followings are done. First, a work space is divided into square cells. Second, cost of each edge connecting current node to neighbor nodes is calculated. Third, the shortest paths from the initial point to all multiple target points are computed and the shortest paths from any target point to remaining target points including the goal point are computed by using Hamilton path. Fourth, a cost-minimal path is re-calculated as soon as the laser sensor detects an obstacle and make an updated list of target points. Finally, the validity of the proposed modified D* lite path planning algorithm is verified through simulation and experimental results.

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 A Bi-Level Path Planning Algorithm for Multi-AGV Routing Problem

Electronics ◽  
2020 ◽  
Vol 9 (9) ◽  
pp. 1351
Author(s):  
Zhiheng Yuan ◽  
Zhengmao Yang ◽  
Lingling Lv ◽  
Yanjun Shi
Keyword(s):  
Path Planning ◽  
Random Trees ◽  
Dijkstra Algorithm ◽  
A Algorithm ◽  
Routing Problem ◽  
Agv Routing ◽  
Planning Algorithm ◽  
Local Grid ◽  
Local Path ◽  
Path Planning Algorithm

Avoiding the multi-automated guided vehicle (AGV) path conflicts is of importance for the efficiency of the AGV system, and we propose a bi-level path planning algorithm to optimize the routing of multi-AGVs. In the first level, we propose an improved A* algorithm to plan the AGV global path in the global topology map, which aims to make the path shortest and reduce the AGV path conflicts as much as possible. In the second level, we present the dynamic rapidly-exploring random trees (RRT) algorithm with kinematic constraints to obtain the passable local path with collisions in the local grid map. Compared to the Dijkstra algorithm and classic A* algorithm, the simulation results showed that the proposed bi-level path planning algorithm performed well in terms of the search efficiency, significantly reducing the incidence of multiple AGV path conflicts.

scholarly journals PATH PLANNING FOR INDOOR CONTACT INSPECTION TASKS WITH UAVS

2020 ◽  
Vol XLIII-B4-2020 ◽  
pp. 345-351
Author(s):  
L. M. González-deSantos ◽  
J. Martínez-Sánchez ◽  
H. González-Jorge ◽  
P. Arias
Keyword(s):  
Path Planning ◽  
Point Cloud ◽  
Initial Position ◽  
Target Point ◽  
Monitoring Methods ◽  
Planning Algorithm ◽  
Close Range ◽  
Inspection Tasks ◽  
Path Planning Algorithm ◽  
Contact Sensors

Abstract. UAV technology has become a useful tool for the inspection of infrastructures. Structural Health Monitoring methods are already implementing these vehicles to obtain information about the condition of the structure. Several systems based on close range remote sensing and contact sensors have been developed. In both cases, in order to perform autonomous missions in hard accessible areas or with obstacles, a path planning algorithm that calculates the trajectory to be followed by the UAV to navigate these areas is mandatory. This works presents a UAV path planning algorithm developed to navigate indoors and outdoors. This algorithm does not only calculate the waypoints of the path, but the orientation of the vehicle for each location. This algorithm will support a specific UAV-based contact inspection of vertical structures. The required input data consist of a point cloud of the environment, the initial position of the UAV and the target point of the structure where the contact inspection will be performed.

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