Path planning of patrol robot based on improved discrete electrostatic discharge algorithm

2022 ◽  
pp. 1-12
Author(s):  
Yang Li ◽  
Simeng Chen ◽  
Ke Bai ◽  
Hao Wang
Keyword(s):  
Path Planning ◽  
Electrostatic Discharge ◽  
Fire Risk ◽  
Convergence Time ◽  
Economic Losses ◽  
Risk Level ◽  
Support Vector ◽  
Initial Population ◽  
Local Optimum ◽  
Planning Problem

Safety is the premise of the stable and sustainable development of the chemical industry, safety accidents will not only cause casualties and economic losses, but also cause panic among workers and nearby residents. Robot safety inspection based on the fire risk level in a chemical industrial park can effectively reduce process accident losses and can even prevent accidents. The optimal inspection path is an important support for patrol efficiency, therefore, in this study, the fire risk level of each location to be inspected, which is obtained by the electrostatic discharge algorithm (ESDA)–nonparallel support vector machine evaluation model, is combined with the optimisation of the inspection path; that is, the fire risk level is used to guide the inspection path planning. The inspection path planning problem is a typical travelling salesman problem (TSP). The discrete ESDA (DESDA), based on the ESDA, is proposed. In view of the shortcomings of the long convergence time and ease of falling into the local optimum of the DESDA, further improvements are proposed in the form of the IDESDA, in which the greedy algorithm is used for the initial population, the 2-opt algorithm is applied to generate new solutions, and the elite set is joined to provide the best segment for jumping out of the local optimum. In the experiments, 11 public calculation examples were used to verify the algorithm performance. The IDESDA exhibited higher accuracy and better stability when solving the TSP. Its application to chemical industrial parks can effectively solve the path optimisation problem of patrol robots.

scholarly journals Adaptive Operator Quantum-Behaved Pigeon-Inspired Optimization Algorithm with Application to UAV Path Planning

Algorithms ◽  
2018 ◽  
Vol 12 (1) ◽  
pp. 3 ◽  
Author(s):  
Chunhe Hu ◽  
Yu Xia ◽  
Junguo Zhang
Keyword(s):  
Path Planning ◽  
Nonlinear Problems ◽  
Mapping Method ◽  
Initial Population ◽  
Local Optimum ◽  
Planning Problem ◽  
Dynamic Constraints ◽  
Logistic Mapping ◽  
Global And Local ◽  
Better Than

Path planning of unmanned aerial vehicles (UAVs) in threatening and adversarial areas is a constrained nonlinear optimal problem which takes a great amount of static and dynamic constraints into account. Quantum-behaved pigeon-inspired optimization (QPIO) has been widely applied to such nonlinear problems. However, conventional QPIO is suffering low global convergence speed and local optimum. In order to solve the above problems, an improved QPIO algorithm, adaptive operator QPIO, is proposed in this paper. Firstly, a new initialization process based on logistic mapping method is introduced to generate the initial population of the pigeon-swarm. After that, to improve the performance of the map and compass operation, the factor parameter will be adaptively updated in each iteration, which can balance the ability between global and local search. In the final landmark operation, the gradual decreasing pigeon population-updating strategy is introduced to prevent premature convergence and local optimum. Finally, the demonstration of the proposed algorithm on UAV path planning problem is presented, and the comparison result indicates that the performance of our algorithm is better than that of particle swarm optimization (PSO), pigeon-inspired optimization (PIO), and its variants, in terms of convergence and accuracy.

Robot path planning based on genetic algorithm with hybrid initialization method

2021 ◽  
pp. 1-16
Author(s):  
Zhaojun Zhang ◽  
Rui Lu ◽  
Minglong Zhao ◽  
Shengyang Luan ◽  
Ming Bu
Keyword(s):  
Genetic Algorithm ◽  
Path Planning ◽  
Iteration Process ◽  
Initial Population ◽  
Local Optimum ◽  
Complex Environments ◽  
Random Initialization ◽  
Planning Problems ◽  
Complete Path

The research of path planning method based on genetic algorithm (GA) for the mobile robot has received much attention in recent years. GA, as one evolutionary computation model, mimics the process of natural evolution and genetics. The quality of the initial population plays an essential role in improving the performance of GA. However, when GA based on a random initialization method is applied to path planning problems, it will lead to the emergence of infeasible solutions and reduce the performance of the algorithm. A novel GA with a hybrid initialization method, termed NGA, is proposed to solve this problem in this paper. In the initial population, NGA first randomly selects three free grids as intermediate nodes. Then, a part of the population uses a random initialization method to obtain the complete path. The other part of the population obtains the complete path using a greedy-related method. Finally, according to the actual situation, the redundant nodes or duplicate paths in the path are deleted to avoid the redundant paths. In addition, the deletion operation and the reverse operation are also introduced to the NGA iteration process to prevent the algorithm from falling into the local optimum. Simulation experiments are carried out with other algorithms to verify the effectiveness of the NGA. Simulation results show that NGA is superior to other algorithms in convergence accuracy, optimization ability, and success rate. Besides, NGA can generate the optimal feasible paths in complex environments.

scholarly journals Multiple Swarm Fruit Fly Optimization Algorithm Based Path Planning Method for Multi-UAVs

2020 ◽  
Vol 10 (8) ◽  
pp. 2822 ◽  
Author(s):  
Kunming Shi ◽  
Xiangyin Zhang ◽  
Shuang Xia
Keyword(s):  
Path Planning ◽  
Nonlinear Optimization ◽  
Optimization Algorithm ◽  
Optimization Problem ◽  
Fruit Fly ◽  
Local Optimum ◽  
Planning Problem ◽  
Fruit Fly Optimization Algorithm ◽  
Nonlinear Optimization Problem ◽  
Fruit Fly Optimization

The path planning of unmanned aerial vehicles (UAVs) in the threat and countermeasure region is a constrained nonlinear optimization problem with many static and dynamic constraints. The fruit fly optimization algorithm (FOA) is widely used to handle this kind of nonlinear optimization problem. In this paper, the multiple swarm fruit fly optimization algorithm (MSFOA) is proposed to overcome the drawback of the original FOA in terms of slow global convergence speed and local optimum, and then is applied to solve the coordinated path planning problem for multi-UAVs. In the proposed MSFOA, the whole fruit fly swarm is divided into several sub-swarms with multi-tasks in order to expand the searching space to improve the searching ability, while the offspring competition strategy is introduced to improve the utilization degree of each calculation result and realize the exchange of information among various fruit fly sub-swarms. To avoid the collision among multi-UAVs, the collision detection method is also proposed. Simulation results show that the proposed MSFOA is superior to the original FOA in terms of convergence and accuracy.

Global Path Planning for Mobile Robot Based on Improved Ant Colony Algorithms

2013 ◽  
Vol 418 ◽  
pp. 15-19 ◽  
Author(s):  
Min Huang ◽  
Ping Ding ◽  
Jiao Xue Huan
Keyword(s):  
Path Planning ◽  
Mobile Robot ◽  
Optimal Path ◽  
Ant Colony ◽  
Mobile Robot Navigation ◽  
Local Optimum ◽  
Planning Problem ◽  
Optimal Path Planning ◽  
Global Path Planning ◽  
Global Optimal

Global optimal path planning is always an important issue in mobile robot navigation. To avoid the limitation of local optimum and accelerate the convergence of the algorithm, a new robot global optimal path planning method is proposed in the paper. It adopts a new transition probability function which combines with the angle factor function and visibility function, and at the same time, sets penalty function by a new pheromone updating model to improve the accuracy of the route searching. The results of computer emulating experiments prove that the method presented is correct and effective, and it is better than the genetic algorithm and traditional ant colony algorithm for global path planning problem.

Support Vector Machines Based Mobile Robot Path Planning in an Unknown Environment

2009 ◽  
Author(s):  
Srinivas Tennety ◽  
Saurabh Sarkar ◽  
Ernest L. Hall ◽  
Manish Kumar
Keyword(s):  
Support Vector Machines ◽  
Path Planning ◽  
Support Vector ◽  
Boundary Line ◽  
Planning Problem ◽  
Data Sets ◽  
Unknown Environment ◽  
Vector Machines ◽  
Class Boundary ◽  
Non Linear

In this paper the use of support vector machines (SVM) for path planning has been investigated through a Player/Stage simulation for various case studies. SVMs are maximum margin classifiers that obtain a non-linear class boundary between the data sets. In order to apply SVM to the path planning problem, the entire obstacle course is divided in to two classes of data sets and a separating class boundary is obtained using SVM. This non-linear class boundary line determines the heading of the robot for a collision-free path. Complex obstacles and maps have been created in the simulation environment of Player/Stage. The effectiveness of SVM for path planning on unknown tracks has been studied and the results have been presented. For the classification of newly detected data points in the unknown environment, the k-nearest neighbors algorithm has been studied and implemented.

Water wave optimization algorithm for autonomous underwater vehicle path planning problem

2021 ◽  
pp. 1-15
Author(s):  
Zheping Yan ◽  
Jinzhong Zhang ◽  
Jia Zeng ◽  
Jialing Tang
Keyword(s):  
Path Planning ◽  
Water Wave ◽  
Autonomous Underwater Vehicle ◽  
Optimal Path ◽  
Wave Theory ◽  
Underwater Vehicle ◽  
Local Optimum ◽  
Planning Problem ◽  
Vehicle Path ◽  
Path Planning Problem

In this paper, a water wave optimization (WWO) algorithm is proposed to solve the autonomous underwater vehicle (AUV) path planning problem to obtain an optimal or near-optimal path in the marine environment. Path planning is a prerequisite for the realization of submarine reconnaissance, surveillance, combat and other underwater tasks. The WWO algorithm based on shallow wave theory is a novel evolutionary algorithm that mimics wave motions containing propagation, refraction and breaking to obtain the global optimization solution. The WWO algorithm not only avoids jumps out of the local optimum and premature convergence but also has a faster convergence speed and higher calculation accuracy. To verify the effectiveness and feasibility, the WWO algorithm is applied to solve the randomly generated threat areas and generated fixed threat areas. Compared with other algorithms, the WWO algorithm can effectively balance exploration and exploitation to avoid threat areas and reach the intended target with minimum fuel costs. The experimental results demonstrate that the WWO algorithm has better optimization performance and is robust.

Robot Path Planning by Generalized Ant Colony Algorithm

2014 ◽  
Vol 494-495 ◽  
pp. 1229-1232 ◽  
Author(s):  
Dai Yuan Zhang ◽  
Peng Fu
Keyword(s):  
Path Planning ◽  
Ant Colony Algorithm ◽  
Ant Colony ◽  
Convergence Time ◽  
Planning Problem ◽  
Robot Path Planning ◽  
Definition Of ◽  
Update Strategy ◽  
Path Planning Problem ◽  
Robot Path

For the problem that the searching speed of traditional ant colony algorithm in robot path planning problem is slow, this paper will solve this problem with generalized ant colony algorithm. Generalized ant colony algorithm extends the definition of ant colony algorithm and does more general research for ant colony algorithm. Functional update strategy replaces the parametric algorithm update strategy; it accelerates the convergence speed of ant colony algorithm. Applying the generalized ant colony algorithm to robot path planning problem can improve the searching speed of robots and reduce the cost of convergence time.

A path planning method of anti-jamming ability improvement for autonomous vehicle navigating in off-road environments

2017 ◽  
Vol 44 (4) ◽  
pp. 406-415 ◽  
Author(s):  
Jiajia Chen ◽  
Wuhua Jiang ◽  
Pan Zhao ◽  
Jinfang Hu
Keyword(s):  
Path Planning ◽  
Autonomous Vehicles ◽  
Autonomous Vehicle ◽  
Safety Margin ◽  
Planning Method ◽  
General Regression Neural Network ◽  
Support Vector ◽  
Planning Problem ◽  
Fuzzy Support Vector Machine ◽  
Content Type

Purpose Navigating in off-road environments is a huge challenge for autonomous vehicles, due to the safety requirement, the effects of noises and non-holonomic constraints of vehicle. This paper aims to describe a path planning method based on fuzzy support vector machine (FSVM) and general regression neural network (GRNN) that is able to provide a solution path for the autonomous vehicle navigating in the off-road environments. Design/methodology/approach The authors decompose the path planning problem into three steps. In the first step, A* algorithm is applied to obtain the positive and negative samples. In the second step, the authors use a learning approach based on radial basis function kernel FSVM to maximize the safety margin for driving, and the fuzzy membership is designed based on GRNN which can help to resolve the problem that the traditional path planning method is easily influenced by noises or outliers. In the third step, the Bezier interpolation algorithm is used to smooth the path. The simulations are designed to verify the parameters of the path planning algorithm. Findings The method is implemented on autonomous vehicle and verified against many outdoor scenes. Road test indicates that the proposed method can produce a flexible, smooth and safe path with good anti-jamming performance. Originality/value This paper applied a new path planning method based on GRNN-FSVM for autonomous vehicle navigating in off-road environments. GRNN-FSVM can reduce the effects of outliers and maximize the safety margin for driving, the generated path is smooth and safe, while satisfying the constraint of vehicle kinematic.

scholarly journals Smooth Path Planning of Mobile Robot Based on Improved Ant Colony Algorithm

2021 ◽  
Vol 2021 ◽  
pp. 1-10
Author(s):  
Wenming Wang ◽  
Jiangdong Zhao ◽  
Zebin Li ◽  
Ji Huang
Keyword(s):  
Path Planning ◽  
Mobile Robot ◽  
Ant Colony Algorithm ◽  
Ant Colony ◽  
Local Optimum ◽  
Planning Problem ◽  
Turning Angle ◽  
Heuristic Information ◽  
Starting Point ◽  
Improved Ant Colony Algorithm

Aiming at the problems of slow convergence, easy to fall into local optimum, and poor smoothness of traditional ant colony algorithm in mobile robot path planning, an improved ant colony algorithm based on path smoothing factor was proposed. Firstly, the environment map was constructed based on the grid method, and each grid was marked to make the ant colony move from the initial grid to the target grid for path search. Then, the heuristic information is improved by referring to the direction information of the starting point and the end point and combining with the turning angle. By improving the heuristic information, the direction of the search is increased and the turning angle of the robot is reduced. Finally, the pheromone updating rules were improved, the smoothness of the two-dimensional path was considered, the turning times of the robot were reduced, and a new path evaluation function was introduced to enhance the pheromone differentiation of the effective path. At the same time, the Max-Min Ant System (MMAS) algorithm was used to limit the pheromone concentration to avoid being trapped in the local optimum path. The simulation results show that the improved ant colony algorithm can search the optimal path length and plan a smoother and safer path with fast convergence speed, which effectively solves the global path planning problem of mobile robot.

A Dynamic Local Path Planning Method for Outdoor Robot Based on Characteristics Extraction of Laser Rangefinder and Extended Support Vector Machine

2016 ◽  
Vol 30 (02) ◽  
pp. 1659004 ◽  
Author(s):  
Lingli Yu ◽  
Kaijun Zhou
Keyword(s):  
Support Vector Machine ◽  
Path Planning ◽  
Least Squares Method ◽  
Optimal Path ◽  
Support Vector ◽  
Planning Problem ◽  
Laser Rangefinder ◽  
Local Path Planning ◽  
Unstructured Environment ◽  
Local Path

For dynamic path planning problem under unstructured environment, firstly, successive edge following and least squares method (SEF-LSM) is adopted to extract environment characteristics of laser rangefinder data, and SEF-LSM with logical reasoning (SEF-LSM-LR) is proposed for dynamic obstacles characteristics detection. Furthermore, the perpendicularity (PERP) algorithm is utilized to identify dynamic vehicle, according to the perpendicularity attribute of vehicle. Secondly, all the laser rangefinder scanning points are marked as negative ([Formula: see text]) or positive ([Formula: see text]1), and the scanning points of one dynamic obstacle are marked as the same label. Thirdly, extended support vector machine (ESVM) is designed for outdoor robot local path planning under unstructured environment, which consider the practical start-goal position and heading constraints, robot kinematic constraint, and curvature constraint, moreover, the emergency obstacle is regarded as disturbances during planning processing. Finally, the optimal path is chosen by the shortest distance evaluation function. Lots of outdoor simulations show that the proposed method solve the dynamic planning problem under unstructured environment, and their effectiveness performance are verified for outdoor robot path planning.

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