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 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 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 Human-Machine Cooperation and Path Planning for Complex Road Conditions

2021 ◽  
Vol 2021 ◽  
pp. 1-11
Author(s):  
Guanghong Zhou
Keyword(s):  
Path Planning ◽  
Obstacle Avoidance ◽  
Industrial Robot ◽  
Rapid Development ◽  
Industrial Robots ◽  
Path Optimization ◽  
Planning System ◽  
Target Point ◽  
Planning Algorithm ◽  
Path Planning Algorithm

With the rapid development of the information age, the development of industrial robots is also advancing by leaps and bounds. In the scenes of automobile, medicine, aerospace, and public service, we have fully enjoyed the convenience brought by industrial robots. However, with the continuous development of industrial robot-related concepts and technologies, human-computer interaction and cooperation have become the development trend of industrial robot. In this paper, the human-machine cooperation and path optimization of industrial robot in a complex road environment are studied and analyzed. At the theoretical modeling level, firstly, the industrial robot is modeled and obstacle avoidance is analyzed based on the kinematics of industrial robot; thus, an efficient and concise collision detection model of industrial robot is proposed. At the algorithm level, in view of the complex road conditions faced by industrial robots, this paper will study and analyze the obstacle avoidance strategy of human-computer cooperation and real-time path optimization algorithm of industrial robots. Based on the virtual target point algorithm, this paper further improves the problem that the goal of the traditional path planning algorithm cannot be fully covered, so as to propose the corresponding improved path planning algorithm of industrial robots. In the experimental part, based on the existing industrial robot system, the human-machine cooperation and path planning system proposed in this paper are designed. The experimental results show that the algorithm proposed in this paper improves the accuracy of obstacle avoidance by about 10 points and the corresponding convergence speed by about 5% compared with the traditional algorithm and the experimental effect is remarkable.

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 2D Lidar-Based SLAM and Path Planning for Indoor Rescue Using Mobile Robots

2020 ◽  
Vol 2020 ◽  
pp. 1-14
Author(s):  
Xuexi Zhang ◽  
Jiajun Lai ◽  
Dongliang Xu ◽  
Huaijun Li ◽  
Minyue Fu
Keyword(s):  
Path Planning ◽  
Mobile Robots ◽  
Real Time ◽  
Obstacle Avoidance ◽  
Basic System ◽  
Rescue Robot ◽  
Planning Algorithm ◽  
Local Path ◽  
Slam Algorithm ◽  
Path Planning Algorithm

As the basic system of the rescue robot, the SLAM system largely determines whether the rescue robot can complete the rescue mission. Although the current 2D Lidar-based SLAM algorithm, including its application in indoor rescue environment, has achieved much success, the evaluation of SLAM algorithms combined with path planning for indoor rescue has rarely been studied. This paper studies mapping and path planning for mobile robots in an indoor rescue environment. Combined with path planning algorithm, this paper analyzes the applicability of three SLAM algorithms (GMapping algorithm, Hector-SLAM algorithm, and Cartographer algorithm) in indoor rescue environment. Real-time path planning is studied to test the mapping results. To balance path optimality and obstacle avoidance, A ∗ algorithm is used for global path planning, and DWA algorithm is adopted for local path planning. Experimental results validate the SLAM and path planning algorithms in simulated, emulated, and competition rescue environments, respectively. Finally, the results of this paper may facilitate researchers quickly and clearly selecting appropriate algorithms to build SLAM systems according to their own demands.

Hybrid obstacle avoidance system with vision and ultrasonic sensors for multi-rotor MAVs

2018 ◽  
Vol 45 (2) ◽  
pp. 227-236 ◽  
Author(s):  
Qiang Zhou ◽  
Danping Zou ◽  
Peilin Liu
Keyword(s):  
Path Planning ◽  
Obstacle Avoidance ◽  
Moving Objects ◽  
Point Clouds ◽  
Ultrasonic Sensor ◽  
Content Type ◽  
Stereo Camera ◽  
3D Point Clouds ◽  
Planning Algorithm ◽  
Path Planning Algorithm

Purpose This paper aims to develop an obstacle avoidance system for a multi-rotor micro aerial vehicle (MAV) that flies in indoor environments which usually contain transparent, texture-less or moving objects. Design/methodology/approach The system adopts a combination of a stereo camera and an ultrasonic sensor to detect obstacles and extracts three-dimensional (3D) point clouds. The obstacle map is built on a coarse global map and updated by local maps generated by the recent 3D point clouds. An efficient layered A* path planning algorithm is also proposed to address the path planning in 3D space for MAVs. Findings The authors conducted a lot of experiments in both static and dynamic scenes. The results show that the obstacle avoidance system works reliably even when transparent or texture-less obstacles are present. The layered A* path planning algorithm is much faster than the traditional 3D algorithm and makes the system response quickly when the obstacle map has been changed because of the moving objects. Research limitations/implications The limited field of view of both stereo camera and ultrasonic sensor makes the system need to change heading first before moving side to side or moving backward. But this problem could be addressed when multiple systems are mounted toward different directions on the MAV. Practical implications The developed approach could be valuable to applications in indoors. Originality/value This paper presents a robust obstacle avoidance system and a fast layered path planning algorithm that are easy to be implemented for practical systems.

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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