Real-time path optimization of mobile robots based on improved genetic algorithm

2020 ◽  
pp. 095965182095220
Author(s):  
Minchuan Wang
Keyword(s):  
Genetic Algorithm ◽  
Mobile Robots ◽  
Real Time ◽  
Simulation Analysis ◽  
Fitness Function ◽  
Optimal Path ◽  
Path Optimization ◽  
Improved Genetic Algorithm ◽  
Time Path ◽  
Improved Algorithm

The emergence of intelligent mobile robots has liberated the human labor to a certain extent, especially their abilities to work in harsh environments in place of humans. For intelligent mobile robots, how to achieve fast path optimization is an important issue. In this article, the model establishment method of environmental information collected by robot sensors and the genetic algorithm for real-time optimization of running paths are briefly introduced first, the crossover, mutation probability, and fitness function are improved based on the shortcomings of the traditional genetic algorithm, and then the simulation analysis of the two algorithms is carried out using matrix laboratory (MATLAB) software. The results show that the improved algorithm obtains a smaller length of optimal path, fewer inflection points, and a smaller turning angle, which also converges faster and has a greater degree of fitness. It takes 0.053 s for the traditional algorithm to calculate the optimal path, while the improved algorithm needs 0.013 s. In summary, the improved genetic algorithm can quickly and efficiently calculate the optimal path, which is suitable for real-time path optimization of mobile robots.

Research on Improved Genetic Algorithm

2014 ◽  
Vol 635-637 ◽  
pp. 1760-1763
Author(s):  
Xiao Yu Wang ◽  
Yong Hui Yang ◽  
Shuo Li ◽  
Chuang Gao
Keyword(s):  
Genetic Algorithm ◽  
Embedded System ◽  
Real Time ◽  
Cost Reduction ◽  
Function Optimization ◽  
Improved Genetic Algorithm ◽  
The Real ◽  
Crossover Rate ◽  
Time Requirements ◽  
Improved Algorithm

An improved genetic algorithm for the function optimization of multi-core embedded system is proposed. A number of chromosomes that distribute uniformly in space are generated by the algorithm randomly. Each chromosome is randomly coded and a new one will be generated by mutual calculation. After continuous elimination and circulation, the optimized chromosomes can be selected. The improved algorithm makes the mutation offspring have the opportunity to be the next parent with the increase of mutation. It enhances the parent diversity, increases the crossover rate, activates crossover between the parents and has chance to access to the best solution. The efficiency and cost reduction performance are improved. The different tasks will be distributed in parallel to available processors so as to meet the real-time requirements.

Real-time Path Adaptation for Sweeping by Autonomous Mobile Robots

1998 ◽  
pp. 299-308
Author(s):  
Daisuke Kurabayashi ◽  
Tamio Arai ◽  
Kanji Iwase ◽  
Jun Ota ◽  
Hajime Asama ◽  
...  
Keyword(s):  
Mobile Robots ◽  
Real Time ◽  
Autonomous Mobile Robots ◽  
Time Path

scholarly journals Multiobjective Synthesis of Linear Arrays by Using an Improved Genetic Algorithm

2019 ◽  
Vol 2019 ◽  
pp. 1-13 ◽  
Author(s):  
Bo Yang
Keyword(s):  
Genetic Algorithm ◽  
Particle Swarm Optimization ◽  
Fitness Function ◽  
Particle Swarm ◽  
Weight Vector ◽  
Side Lobe Level ◽  
Improved Genetic Algorithm ◽  
Pattern Synthesis ◽  
Swarm Optimization ◽  
Dynamic Weight

In this paper, an improved genetic algorithm with dynamic weight vector (IGA-DWV) is proposed for the pattern synthesis of a linear array. To maintain the diversity of the selected solution in each generation, the objective function space is divided by the dynamic weight vector, which is uniformly distributed on the Pareto front (PF). The individuals closer to the dynamic weight vector can be chosen to the new population. Binary- and real-coded genetic algorithms (GAs) with a mapping method are implemented for different optimization problems. To reduce the computation complexity, the repeat calculation of the fitness function in each generation is replaced by a precomputed discrete cosine transform matrix. By transforming the array pattern synthesis into a multiobjective optimization problem, the conflict among the side lobe level (SLL), directivity, and nulls can be efficiently addressed. The proposed method is compared with real number particle swarm optimization (RNPSO) and quantized particle swarm optimization (QPSO) as applied in the pattern synthesis of a linear thinned array and a digital phased array. The numerical examples show that IGA-DWV can achieve a high performance with a lower SLL and more accurate nulls.

scholarly journals A Quantum Ant Colony Multi-Objective Routing Algorithm in WSN and Its Application in a Manufacturing Environment

Sensors ◽  
2019 ◽  
Vol 19 (15) ◽  
pp. 3334 ◽  
Author(s):  
Fei Li ◽  
Min Liu ◽  
Gaowei Xu
Keyword(s):  
Simulation Analysis ◽  
Fitness Function ◽  
Routing Algorithm ◽  
Optimal Path ◽  
Ant Colony ◽  
Local Optimum ◽  
Manufacturing Environment ◽  
Multi Objective ◽  
Search Path ◽  
Manufacturing Environments

In many complex manufacturing environments, the running equipment must be monitored by Wireless Sensor Networks (WSNs), which not only requires WSNs to have long service lifetimes, but also to achieve rapid and high-quality transmission of equipment monitoring data to monitoring centers. Traditional routing algorithms in WSNs, such as Basic Ant-Based Routing (BABR) only require the single shortest path, and the BABR algorithm converges slowly, easily falling into a local optimum and leading to premature stagnation of the algorithm. A new WSN routing algorithm, named the Quantum Ant Colony Multi-Objective Routing (QACMOR) can be used for monitoring in such manufacturing environments by introducing quantum computation and a multi-objective fitness function into the routing research algorithm. Concretely, quantum bits are used to represent the node pheromone, and quantum gates are rotated to update the pheromone of the search path. The factors of energy consumption, transmission delay, and network load-balancing degree of the nodes in the search path act as fitness functions to determine the optimal path. Here, a simulation analysis and actual manufacturing environment verify the QACMOR’s improvement in performance.

scholarly journals Real-Time Fault Repair Scheme Based on Improved Genetic Algorithm

IEEE Access ◽  
2019 ◽  
Vol 7 ◽  
pp. 35805-35815 ◽  
Author(s):  
Jie Wang ◽  
Junjie Kang ◽  
Gang Hou
Keyword(s):  
Genetic Algorithm ◽  
Real Time ◽  
Improved Genetic Algorithm
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