Abstract
The intelligent patrol car with environmental sensing and autonomous navigation is a special robot, which is mostly used for equipment defect detection in industrial areas such as the power distribution room or data center room. A path planning algorithm for the navigation system of intelligent patrol car is proposed to ensure efficient and secure navigation in the complex indoor environment, and its effect is verified by simulation and experiment. First, a patrol car platform integrated with several intelligent devices is built to achieve global localization, mapping and path planning. Then a new co-optimization on multi-objective Cauchy mutation cat swarm optimization (MOCMCSO) and artificial potential field method (APFM) is proposed to solve the multi-objective optimization problem on shortest global path length and minimum total turning-angle variation. The optimal path is written into the navigation module to drive the patrol car to move and navigate. The simulations are carried out to confirm that the method can achieve a balance between the shortest path and good path smoothness, which has less optimization time and lower fitness value compared with multi-objective cat swarm optimization (MOCSO) and multi-objective particle swarm optimization (MOPSO), and is more suitable for global path planning in indoor environment. Finally, the experiments have been carried out in the data center equipment room to verify the effectiveness and superiority over the path planning algorithm on MOCMCSO.
Highlights
The intelligent patrol car for industrial applications is a special highly intelligent autonomous robot, which can be used for automatic inspection for high-voltage power distribution rooms, large data center equipment rooms and manufacturing workshops, and so on [1]
This paper introduces a multi-objective Cauchy mutation cat swarm algorithm (MOCMCSO), which introduces Cauchy mutation operators to improve the performance of search patterns [34], expand the range of swarm search, and continuously improve population information during the seeking process to avoid premature convergence and localization optimal solution, fundamentally improve population diversity
The output of multiobjective Cauchy mutation cat swarm optimization (MOCMCSO) is a set of Pareto optimal solutions, any point on the Pareto curve represents a path scheme on multi-objective optimization for global path length and total turning-angle variation, but only one set of value can be input into the navigation module of intelligent patrol car
Summary
The intelligent patrol car for industrial applications is a special highly intelligent autonomous robot, which can be used for automatic inspection for high-voltage power distribution rooms, large data center equipment rooms and manufacturing workshops, and so on [1]. The high intelligent autonomous robots, such as patrol cars, service robots and rescue robots, have been widely applied to terrain detection, disaster relief and factory operations. These robots have brought huge benefits for enterprises and the industry [4]. Intelligent patrol cars need to avoid collisions with high-risk and expensive equipment in large spaces quickly in order to reach the work area. The simulation test and actual machine experiment are executed to verify the superiority of the proposed algorithm compared with several other artificial intelligence methods
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