scholarly journals A Novel Method for Driving Path Planning with Spark

2021 ◽  
Author(s):  
Hao Lin
Keyword(s):  
Path Planning ◽  
Heuristic Algorithms ◽  
Parallel Design ◽  
Improve Efficiency ◽  
Novel Method ◽  
Low Efficiency

Efficient and accurate driving path planning can help drivers drive. To solve the problem of low efficiency of traditional heuristic algorithms such as PSO and GA in solving driving path planning, we introduce Excellence Coefficient into heuristic algorithms and make a parallel design based on Spark, which called EC-SPPSOGA. Excellence Coefficient can increase the probability of good edges being left, simultaneously, preserves the possibility of longer side being selected. The parallel design is based on time-consuming analysis of heuristic algorithms. We validate the performance of EC-SPPSOGA based on the data in TSPLIB. It is verified that the EC-SPPSOGA can improve efficiency of driving path planning and has good scalability.

scholarly journals A Novel Method for Driving Path Planning with Spark

2021 ◽  
Author(s):  
Hao Lin
Keyword(s):  
Path Planning ◽  
Heuristic Algorithms ◽  
Parallel Design ◽  
Improve Efficiency ◽  
Novel Method ◽  
Low Efficiency

Efficient and accurate driving path planning can help drivers drive. To solve the problem of low efficiency of traditional heuristic algorithms such as PSO and GA in solving driving path planning, we introduce Excellence Coefficient into heuristic algorithms and make a parallel design based on Spark, which called EC-SPPSOGA. Excellence Coefficient can increase the probability of good edges being left, simultaneously, preserves the possibility of longer side being selected. The parallel design is based on time-consuming analysis of heuristic algorithms. We validate the performance of EC-SPPSOGA based on the data in TSPLIB. It is verified that the EC-SPPSOGA can improve efficiency of driving path planning and has good scalability.

scholarly journals Delayed improvement local search

2021 ◽  
Author(s):  
Heber F. Amaral ◽  
Sebastián Urrutia ◽  
Lars M. Hvattum
Keyword(s):  
Local Search ◽  
Heuristic Algorithms ◽  
Travelling Salesman Problem ◽  
Computation Time ◽  
The Novel ◽  
Local Optima ◽  
New Strategy ◽  
Max Cut Problem ◽  
Novel Method ◽  
Similar Solutions

AbstractLocal search is a fundamental tool in the development of heuristic algorithms. A neighborhood operator takes a current solution and returns a set of similar solutions, denoted as neighbors. In best improvement local search, the best of the neighboring solutions replaces the current solution in each iteration. On the other hand, in first improvement local search, the neighborhood is only explored until any improving solution is found, which then replaces the current solution. In this work we propose a new strategy for local search that attempts to avoid low-quality local optima by selecting in each iteration the improving neighbor that has the fewest possible attributes in common with local optima. To this end, it uses inequalities previously used as optimality cuts in the context of integer linear programming. The novel method, referred to as delayed improvement local search, is implemented and evaluated using the travelling salesman problem with the 2-opt neighborhood and the max-cut problem with the 1-flip neighborhood as test cases. Computational results show that the new strategy, while slower, obtains better local optima compared to the traditional local search strategies. The comparison is favourable to the new strategy in experiments with fixed computation time or with a fixed target.

A RESEARCH ON TECHNIQUES, MODELS AND METHODS PROPOSED FOR SHIP COLLISION AVOIDANCE PATH PLANNING PROBLEM

2021 ◽  
Vol 160 (A2) ◽  
Author(s):  
R Fışkın ◽  
H Kişi ◽  
E Nasibov
Keyword(s):  
Path Planning ◽  
Collision Avoidance ◽  
Heuristic Algorithms ◽  
Planning Problem ◽  
Related Field ◽  
Marine Industry ◽  
Soft Computing Techniques ◽  
Ship Collision ◽  
Marine Safety ◽  
Path Planning Problem

The development of soft computing techniques in recent years has encouraged researchers to study on the path planning problem in ship collision avoidance. These techniques have widely been implemented in marine industry and technology-oriented novel solutions have been introduced. Various models, methods and techniques have been proposed to solve the mentioned path planning problem with the aim of preventing reoccurrence of the problem and thus strengthening marine safety as well as providing fuel consumption efficiency. The purpose of this study is to scrutinize the models, methods and technologies proposed to settle the path planning issue in ship collision avoidance. The study also aims to provide certain bibliometric information which develops a literature map of the related field. For this purpose, a thorough literature review has been carried out. The results of the study have pointedly showed that the artificial intelligence methods, fuzzy logic and heuristic algorithms have greatly been used by the researchers who are interested in the related field.

A Research on Techniques, Models and Methods Proposed for Ship Collision Avoidance Path Planning Problem

2018 ◽  
Vol Vol 160 (A2) ◽  
Author(s):  
R Fışkın ◽  
H Kişi ◽  
E Nasibov
Keyword(s):  
Path Planning ◽  
Collision Avoidance ◽  
Heuristic Algorithms ◽  
Planning Problem ◽  
Related Field ◽  
Marine Industry ◽  
Soft Computing Techniques ◽  
Ship Collision ◽  
Marine Safety ◽  
Path Planning Problem

The development of soft computing techniques in recent years has encouraged researchers to study on the path planning problem in ship collision avoidance. These techniques have widely been implemented in marine industry and technology-oriented novel solutions have been introduced. Various models, methods and techniques have been proposed to solve the mentioned path planning problem with the aim of preventing reoccurrence of the problem and thus strengthening marine safety as well as providing fuel consumption efficiency. The purpose of this study is to scrutinize the models, methods and technologies proposed to settle the path planning issue in ship collision avoidance. The study also aims to provide certain bibliometric information which develops a literature map of the related field. For this purpose, a thorough literature review has been carried out. The results of the study have pointedly showed that the artificial intelligence methods, fuzzy logic and heuristic algorithms have greatly been used by the researchers who are interested in the related field.

An agile robot taping system – modeling, tool design, planning and execution

2016 ◽  
Vol 43 (5) ◽  
pp. 503-512 ◽  
Author(s):  
Qilong Yuan ◽  
I-Ming Chen ◽  
Teguh Santoso Lembono
Keyword(s):  
Path Planning ◽  
Robot Manipulator ◽  
System Modeling ◽  
The Novel ◽  
Rotating Platform ◽  
Content Type ◽  
Novel Method ◽  
End Effectors ◽  
The Mathematical Model ◽  
Special Process

Purpose Taping, covering objects with masking tapes, is a common process before conducting surface treatments such as plasma spraying and painting. Manual taping is tedious and takes a lot of effort of the workers. This paper aims to introduce an automatic agile robotic system and corresponding algorithm to do the surface taping. Design/methodology/approach The taping process is a special process which requires correct tape orientation and proper allocation of the masking tape for the coverage. This paper discusses on the design of the novel automatic system consisting of a robot manipulator, a rotating platform, a 3D scanner and a specially designed novel taping end-effectors. Meanwhile, the taping path planning to cover the region of interests is introduced. Findings Currently, cylindrical and freeform surfaces have been tested. With improvements on new sets of taping tools and more detailed taping method, taping of general surfaces can be conducted using such system in future. Originality/value The introduced taping path planning method is a novel method first talking about the mathematical model of the taping process. Such taping solution with the taping tool and the taping methodology can be combined as a very useful and practical taping package to replace the work of human in such tedious and time-consuming works.

A Glowworm Swarm Optimization Algorithm for Uninhabited Combat Air Vehicle Path Planning

2015 ◽  
Vol 24 (1) ◽  
pp. 69-83 ◽  
Author(s):  
Zhonghua Tang ◽  
Yongquan Zhou
Keyword(s):  
Path Planning ◽  
Heuristic Algorithms ◽  
Optimization Methods ◽  
Pso Algorithm ◽  
Population Based ◽  
Computational Accuracy ◽  
Swarm Optimization ◽  
Glowworm Swarm Optimization ◽  
Air Vehicle ◽  
The Individual

AbstractUninhabited combat air vehicle (UCAV) path planning is a complicated, high-dimension optimization problem. To solve this problem, we present in this article an improved glowworm swarm optimization (GSO) algorithm based on the particle swarm optimization (PSO) algorithm, which we call the PGSO algorithm. In PGSO, the mechanism of a glowworm individual was modified via the individual generation mechanism of PSO. Meanwhile, to improve the presented algorithm’s convergence rate and computational accuracy, we reference the idea of parallel hybrid mutation and local search near the global optimal location. To prove the performance of the proposed algorithm, PGSO was compared with 10 other population-based optimization methods. The experiment results show that the proposed approach is more effective in UCAV path planning than most of the other meta-heuristic algorithms.

scholarly journals One-Shot Multi-Path Planning Using Fully Convolutional Networks in a Comparison to Other Algorithms

2021 ◽  
Vol 14 ◽  
Author(s):  
Tomas Kulvicius ◽  
Sebastian Herzog ◽  
Timo Lüddecke ◽  
Minija Tamosiunaite ◽  
Florentin Wörgötter
Keyword(s):  
Path Planning ◽  
Arm Movement ◽  
Multi Agent Systems ◽  
Convolutional Networks ◽  
Fully Convolutional Networks ◽  
Current State ◽  
Multiple Paths ◽  
Multi Agent ◽  
Novel Method ◽  
2D And 3D

Path planning plays a crucial role in many applications in robotics for example for planning an arm movement or for navigation. Most of the existing approaches to solve this problem are iterative, where a path is generated by prediction of the next state from the current state. Moreover, in case of multi-agent systems, paths are usually planned for each agent separately (decentralized approach). In case of centralized approaches, paths are computed for each agent simultaneously by solving a complex optimization problem, which does not scale well when the number of agents increases. In contrast to this, we propose a novel method, using a homogeneous, convolutional neural network, which allows generation of complete paths, even for more than one agent, in one-shot, i.e., with a single prediction step. First we consider single path planning in 2D and 3D mazes. Here, we show that our method is able to successfully generate optimal or close to optimal (in most of the cases <10% longer) paths in more than 99.5% of the cases. Next we analyze multi-paths either from a single source to multiple end-points or vice versa. Although the model has never been trained on multiple paths, it is also able to generate optimal or near-optimal (<22% longer) paths in 96.4 and 83.9% of the cases when generating two and three paths, respectively. Performance is then also compared to several state of the art algorithms.

scholarly journals A Comparative Study on Meta-Heuristic Algorithms For Solving the RNP Problem

2019 ◽  
pp. 1639-1648
Author(s):  
Abeer Sufyan Khalil ◽  
Rawaa Dawoud Al-Dabbagh
Keyword(s):  
Heuristic Algorithms ◽  
Real Life ◽  
Research Area ◽  
Optimization Techniques ◽  
Radio Network ◽  
Life Problems ◽  
Adaptive Differential Evolution ◽  
Low Efficiency ◽  
The Many

The continuous increases in the size of current telecommunication infrastructures have led to the many challenges that existing algorithms face in underlying optimization. The unrealistic assumptions and low efficiency of the traditional algorithms make them unable to solve large real-life problems at reasonable times.The use of approximate optimization techniques, such as adaptive metaheuristic algorithms, has become more prevalent in a diverse research area. In this paper, we proposed the use of a self-adaptive differential evolution (jDE) algorithm to solve the radio network planning (RNP) problem in the context of the upcoming generation 5G. The experimental results prove the jDE with best vector mutation surpassed the other metaheuristic variants, such as DE/rand/1 and classical GA, in term of deployment cost, coverage rate and quality of service (QoS).

Sign in / Sign up

Export Citation Format

Share Document