scholarly journals Path planning of intelligent mobile robot based on Dijkstra algorithm

2021 ◽  
Vol 2083 (4) ◽  
pp. 042034
Author(s):  
Xueyan Li
Keyword(s):  
Path Planning ◽  
Mobile Robot ◽  
Linear Time ◽  
Optimal Path ◽  
Working Environment ◽  
Neighborhood Search ◽  
Manhattan Distance ◽  
Target Point ◽  
Dijkstra Algorithm ◽  
Source Point

Abstract Under the premise of grid environment modeling method, a relaxed Dijkstra algorithm is proposed to solve the problem of real-time path planning for mobile robot in large-scale and obstacle intensive working environment. Firstly, four neighborhood search is used to construct the Manhattan distance potential field from the source point to the global point in linear time, and then eight neighborhood search is performed from the target point to the source point to return a collision free and approximately optimal path. Matlab simulation results show that the algorithm is 10 times faster than dijksta algorithm and A-star algorithm, and the error of path length is within a reasonable range compared with the shortest path.

scholarly journals Path Planning of an Autonomous Mobile Robot using Enhanced Bacterial Foraging Optimization Algorithm

2017 ◽  
Vol 12 (4) ◽  
pp. 26-35 ◽  
Author(s):  
Nizar Hadi Abbas ◽  
Farah Mahdi Ali
Keyword(s):  
Path Planning ◽  
Mobile Robot ◽  
Optimization Algorithm ◽  
Optimal Path ◽  
Target Point ◽  
Autonomous Mobile Robot ◽  
Bacterial Foraging Optimization ◽  
Bacterial Foraging ◽  
Starting Point ◽  
Bacterial Foraging Optimization Algorithm

This paper describes the problem of online autonomous mobile robot path planning, which is consisted of finding optimal paths or trajectories for an autonomous mobile robot from a starting point to a destination across a flat map of a terrain, represented by a 2-D workspace. An enhanced algorithm for solving the problem of path planning using Bacterial Foraging Optimization algorithm is presented. This nature-inspired metaheuristic algorithm, which imitates the foraging behavior of E-coli bacteria, was used to find the optimal path from a starting point to a target point. The proposed algorithm was demonstrated by simulations in both static and dynamic different environments. A comparative study was evaluated between the developed algorithm and other two state-of-the-art algorithms. This study showed that the proposed method is effective and produces trajectories with satisfactory results.

An Improved Algorithm of Path Planning for a Mobile Robot

2013 ◽  
Vol 392 ◽  
pp. 253-256 ◽  
Author(s):  
Xue Mei Jia ◽  
Qi Yuan Sun
Keyword(s):  
Path Planning ◽  
Mobile Robot ◽  
Optimal Path ◽  
Target Point ◽  
Complex Environment ◽  
Rolling Window ◽  
Dynamic Time ◽  
Time Varying Environment ◽  
Varying Environment ◽  
Improved Algorithm

Navigation is a key technology of mobile robot. The information of path planning for navigation is always incomplete in dynamic time-varying environment. In this paper, based on grid environmental model, an improved algorithm of path planning is proposed combining ant colony optimization with rolling window for an unknown complex environment with obstacles. A sub-target point can be searched within a rolling window according to the heuristic information about a target point, the improved algorithm can plan a local optimal path for the mobile robot in a rolling window. Simulations show that the improved method is feasible for path planning in an unknown environment.

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.

Task Assignment and Path Planning of a Multi-AUV System Based on a Glasius Bio-Inspired Self-Organising Map Algorithm

2017 ◽  
Vol 71 (2) ◽  
pp. 482-496 ◽  
Author(s):  
Daqi Zhu ◽  
Yu Liu ◽  
Bing Sun
Keyword(s):  
Neural Network ◽  
Path Planning ◽  
Autonomous Underwater Vehicle ◽  
Three Dimensional ◽  
Task Assignment ◽  
Working Environment ◽  
Neuron Activity ◽  
Target Point ◽  
System Task ◽  
Self Organising Map

For multi-Autonomous Underwater Vehicle (multi-AUV) system task assignment and path planning, a novel Glasius Bio-inspired Self-Organising Map (GBSOM) neural networks algorithm is proposed to solve relevant problems in a Three-Dimensional (3D) grid map. Firstly, a 3D Glasius Bio-inspired Neural Network (GBNN) model is established to represent the 3D underwater working environment. Using this model, the strength of neural activity is calculated at each node within the GBNN. Secondly, a Self-Organising Map (SOM) neural network is used to assign the targets to a set of AUVs and determine the order of the AUVs to access the target point. Finally, according to the magnitude of the neuron activity in the GBNN, the next AUV target point can be autonomously planned when the task assignment is completed. By repeating the above three steps, access to all target points is completed. Simulation and comparison studies are presented to demonstrate that the proposed algorithm can overcome the speed jump problem of SOM algorithms and path planning in the 3D underwater environments with static or dynamic obstacles.

A Review of Path Planning Method for Mobile Robot

2014 ◽  
Vol 1030-1032 ◽  
pp. 1588-1591 ◽  
Author(s):  
Zong Sheng Wu ◽  
Wei Ping Fu
Keyword(s):  
Path Planning ◽  
Mobile Robot ◽  
Optimal Path ◽  
Grid Method ◽  
Field Method ◽  
Planning Method ◽  
Artificial Potential Field ◽  
Planning Methods ◽  
Basic Ideas ◽  
Artificial Potential Field Method

The ability of a mobile robot to plan its path is the key task in the field of robotics, which is to find a shortest, collision free, optimal path in the various scenes. In this paper, different existing path planning methods are presented, and classified as: geometric construction method, artificial intelligent path planning method, grid method, and artificial potential field method. This paper briefly introduces the basic ideas of the four methods and compares them. Some challenging topics are presented based on the reviewed papers.

Optimal path planning of mobile robot using the hybrid cuckoo–bat algorithm in assorted environment

2019 ◽  
Vol 7 (1) ◽  
pp. 35-52 ◽  
Author(s):  
Balamurali Gunji ◽  
Deepak B.B.V.L. ◽  
Saraswathi M.B.L. ◽  
Umamaheswara Rao Mogili
Keyword(s):  
Path Planning ◽  
Mobile Robot ◽  
Real World ◽  
Hybrid Algorithm ◽  
Optimal Path ◽  
Bat Algorithm ◽  
Cuckoo Search ◽  
Content Type ◽  
The Real ◽  
World Environment

Purpose The purpose of this paper is to obtain an optimal mobile robot path planning by the hybrid algorithm, which is developed by two nature inspired meta-heuristic algorithms, namely, cuckoo-search and bat algorithm (BA) in an unknown or partially known environment. The cuckoo-search algorithm is based on the parasitic behavior of the cuckoo, and the BA is based on the echolocation behavior of the bats. Design/methodology/approach The developed algorithm starts by sensing the obstacles in the environment using ultrasonic sensor. If there are any obstacles in the path, the authors apply the developed algorithm to find the optimal path otherwise reach the target point directly through diagonal distance. Findings The developed algorithm is implemented in MATLAB for the simulation to test the efficiency of the algorithm for different environments. The same path is considered to implement the experiment in the real-world environment. The ARDUINO microcontroller along with the ultrasonic sensor is considered to obtain the path length and time of travel of the robot to reach the goal point. Originality/value In this paper, a new hybrid algorithm has been developed to find the optimal path of the mobile robot using cuckoo search and BAs. The developed algorithm is tested with the real-world environment using the mobile robot.

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