A New Algorithm of Path Planning Based on Local Data

2012 ◽  
Vol 531-532 ◽  
pp. 741-745
Author(s):  
Zi Wei Zhou ◽  
Xin Yu O Yang ◽  
Wang Bao Xu ◽  
Nai Dong Cui
Keyword(s):  
Path Planning ◽  
Autonomous Navigation ◽  
Optimal Path ◽  
Sensor Data ◽  
Target Point ◽  
Local Data ◽  
Practical Applications ◽  
Set Up ◽  
Moving Obstacle ◽  
Robot Path

A novel new algorithm of path planning is proposed for the robot path planning based on the local data map in the robot autonomous navigation. The algorithm is used to search for the local optimal path from the current position of the robot to the target according to the known sensor data. If the robot cannot reach the target directly, the temporary target point which the robot can reach will be set up according to the optimal path. The algorithm is effective under the complicated unknown environment and moving obstacle situation which fast searching speed, it can be adapted into the practical applications and the multi-robot harmony easily. The simulation result shows that this method can provide a better planning path by comparing with the traditional planning algorithms such as artificial potential field and wall-following

scholarly journals Mobile Robot Path Planning Based on a Generalized Wavefront Algorithm

2020 ◽  
Vol 2020 ◽  
pp. 1-12
Author(s):  
Sifan Wu ◽  
Yu Du ◽  
Yonghua Zhang
Keyword(s):  
Path Planning ◽  
Mobile Robot ◽  
Path Length ◽  
Computation Time ◽  
Target Point ◽  
Multiple Target ◽  
Robot Path Planning ◽  
Practical Applications ◽  
Real Time Applications ◽  
Robot Path

This study develops a generalized wavefront algorithm for conducting mobile robot path planning. The algorithm combines multiple target point sets, multilevel grid costs, logarithmic expansion around obstacles, and subsequent path optimization. The planning performances obtained with the proposed algorithm, the A∗ algorithm, and the rapidly exploring random tree (RRT) algorithm optimized using a Bézier curve are compared using simulations with different grid map environments comprising different numbers of obstacles with varying shapes. The results demonstrate that the generalized wavefront algorithm generates smooth and safe paths around obstacles that meet the required kinematic conditions associated with the actual maneuverability of mobile robots and significantly reduces the planned path length compared with the results obtained with the A∗ algorithm and the optimized RRT algorithm with a computation time acceptable for real-time applications. Therefore, the generated path is not only smooth and effective but also conforms to actual robot maneuverability in practical applications.

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 COLREG-Compliant Optimal Path Planning for Real-Time Guidance and Control of Autonomous Ships

2021 ◽  
Vol 9 (4) ◽  
pp. 405
Author(s):  
Raphael Zaccone
Keyword(s):  
Path Planning ◽  
Real Time ◽  
Collision Avoidance ◽  
Autonomous Navigation ◽  
Optimal Path ◽  
Open Water ◽  
Optimal Path Planning ◽  
Guidance And Control ◽  
Real Time Applications ◽  
And Control

While collisions and groundings still represent the most important source of accidents involving ships, autonomous vessels are a central topic in current research. When dealing with autonomous ships, collision avoidance and compliance with COLREG regulations are major vital points. However, most state-of-the-art literature focuses on offline path optimisation while neglecting many crucial aspects of dealing with real-time applications on vessels. In the framework of the proposed motion-planning, navigation and control architecture, this paper mainly focused on optimal path planning for marine vessels in the perspective of real-time applications. An RRT*-based optimal path-planning algorithm was proposed, and collision avoidance, compliance with COLREG regulations, path feasibility and optimality were discussed in detail. The proposed approach was then implemented and integrated with a guidance and control system. Tests on a high-fidelity simulation platform were carried out to assess the potential benefits brought to autonomous navigation. The tests featured real-time simulation, restricted and open-water navigation and dynamic scenarios with both moving and fixed obstacles.

Reliability-Based Mission Planning of Off-Road Autonomous Ground Vehicles Using an Outcrossing Approach

2021 ◽  
Author(s):  
Chen Jiang ◽  
Yixuan Liu ◽  
Zhen Hu ◽  
Zissimos P. Mourelatos ◽  
David Gorsich ◽  
...  
Keyword(s):  
Path Planning ◽  
Optimal Path ◽  
Gaussian Random Field ◽  
Mobility Model ◽  
Outcrossing Rate ◽  
Mission Planning ◽  
Ground Vehicles ◽  
Practical Applications ◽  
Autonomous Ground Vehicles ◽  
Uncertainty Sources

Abstract Reliability-based mission planning aims to identify an optimal path for off-road autonomous ground vehicles (AGVs) under uncertain terrain environment, while satisfying specific mission mobility reliability (MMR) constraints. The evaluation of MMR during path planning poses computational challenges for practical applications. This paper presents an efficient reliability-based mission planning using an outcrossing approach that has the same computational complexity as deterministic mission planning. A Gaussian random field is employed to represent the spatially dependent uncertainty sources in the terrain environment. The latter are then used in conjunction with a vehicle mobility model to generate a stochastic mobility map. Based on the stochastic mobility map, outcrossing rate maps are generated using the outcrossing concept which is widely used in time-dependent reliability. Integration of the outcrossing rate map with a rapidly-exploring random tree (RRT*) algorithm, allows for efficient path planning of AGVs subject to MMR constraints. A reliable RRT* algorithm using the outcrossing approach (RRT*-OC) is developed to implement the proposed efficient reliability-based mission planning. Results of a case study verify the accuracy and efficiency of the proposed algorithm.

An Integrated Algorithm for Autonomous Navigation of a Mobile Robot in an Unknown Environment

2008 ◽  
Vol 12 (4) ◽  
pp. 328-335 ◽  
Author(s):  
Lee Gim Hee ◽  
◽  
Marcelo H. Ang Jr. ◽  
Keyword(s):  
Path Planning ◽  
Mobile Robot ◽  
Hybrid Method ◽  
Autonomous Navigation ◽  
Prior Information ◽  
Optimal Path ◽  
Unknown Environment ◽  
Global Path Planning ◽  
Local Navigation ◽  
Navigation Algorithms

Global path planning algorithms are good in planning an optimal path in a known environment, but would fail in an unknown environment and when reacting to dynamic and unforeseen obstacles. Conversely, local navigation algorithms perform well in reacting to dynamic and unforeseen obstacles but are susceptible to local minima failures. A hybrid integration of both the global path planning and local navigation algorithms would allow a mobile robot to find an optimal path and react to any dynamic and unforeseen obstacles during an operation. However, the hybrid method requires the robot to possess full or partial prior information of the environment for path planning and would fail in a totally unknown environment. The integrated algorithm proposed and implemented in this paper incorporates an autonomous exploration technique into the hybrid method. The algorithm gives a mobile robot the ability to plan an optimal path and does online collision avoidance in a totally unknown environment.

New Approach for Mobile Robot Path Planning

2013 ◽  
Vol 467 ◽  
pp. 475-478
Author(s):  
Feng Yun Lin
Keyword(s):  
Path Planning ◽  
Optimal Path ◽  
Dc Motor ◽  
Robot Path Planning ◽  
New Approach ◽  
Kinematic Constraints ◽  
Optimal Path Planning ◽  
Kinematic Limit ◽  
Time Optimal ◽  
Robot Path

This paper presents a method of time optimal path planning under kinematic, limit heat characteristics of DC motor and dynamic constrain for a 2-DOF wheeled. Firstly the shortest path is planned by using the geometric method under kinematic constraints. Then, in order to make full use of motors capacity we have the torque limits under limit heat characteristics of DC motor, finally the velocity limit and the boundary acceleration (deceleration) are determined to generate a time optimal path.

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 Review of Mobile Robot Path Planning Based on Deep Reinforcement Learning Algorithm

2021 ◽  
Vol 2138 (1) ◽  
pp. 012011
Author(s):  
Yanwei Zhao ◽  
Yinong Zhang ◽  
Shuying Wang
Keyword(s):  
Deep Learning ◽  
Reinforcement Learning ◽  
Path Planning ◽  
Mobile Robot ◽  
Video Game ◽  
Autonomous Navigation ◽  
Learning Algorithm ◽  
Basic Knowledge ◽  
Target Point ◽  
Reinforcement Learning Algorithm

Abstract Path planning refers to that the mobile robot can obtain the surrounding environment information and its own state information through the sensor carried by itself, which can avoid obstacles and move towards the target point. Deep reinforcement learning consists of two parts: reinforcement learning and deep learning, mainly used to deal with perception and decision-making problems, has become an important research branch in the field of artificial intelligence. This paper first introduces the basic knowledge of deep learning and reinforcement learning. Then, the research status of deep reinforcement learning algorithm based on value function and strategy gradient in path planning is described, and the application research of deep reinforcement learning in computer game, video game and autonomous navigation is described. Finally, I made a brief summary and outlook on the algorithms and applications of deep reinforcement learning.

The Study on Mobile Robot Path Planning Based on Frog Leaping Algorithm

2012 ◽  
Vol 490-495 ◽  
pp. 808-812
Author(s):  
Zheng Ran Zhang ◽  
Ji Ying Yin
Keyword(s):  
Path Planning ◽  
Mobile Robot ◽  
Optimization Problem ◽  
Optimal Path ◽  
Robot Path Planning ◽  
Unknown Environment ◽  
Simulation Results ◽  
Robot Path

We have proposed a method of robot path planning in a partially unknown environment in this paper. We regard the problem of robot path planning as an optimization problem and solve it with the SFL algorithm. The position of globally best frog in each iterative is selected, and reached by the robot in sequence. The obstacles are detected by the robot sensors are applied to update the information of the environment. The optimal path is generated until the robot reaches its target. The simulation results validate the feasibility of the proposed method.

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