Mobile robot path planning using multi-objective genetic algorithm in industrial automation

Mobile robot path planning problem is a significant research area in industrial automation, which is to determine an optimal path for a robot to reach the destination by avoiding obstacles. Path planning (PP) is one of the most researched topics in mobile robotics. Deriving an optimal path from a huge number of feasible paths for a given environment is called a PP problem. The existing optimization techniques are used to consider path safety, path length, and path smoothness. The conventional optimization techniques implemented for the mobile robot path planning problem incur a lot of cost due to the high complexity to solve. In order to find the optimal path for handling the mobile robot path planning problem, the mobile robot path search based on multi-objective genetic algorithm (MRPS-MOGA) is proposed. The MRPS-MOGA is designed with the novelty of genetic algorithm with multiple objective function to solve mobile robot path planning problems. Hence the proposed MRPS-MOGA handles five different objectives such as safety, distance, smoothness, traveling time, and collision-free path to obtain optimal path. The MOGA is applied to select an optimal path among multiple as well as feasible paths. The population with feasible paths is initialized with randomly generated paths. The fitness value is evaluated for the number of available candidate paths by applying objective functions for different objectives. Then the fitness criterion determines the paths which are to be passed to participate in the next generation. MRPS-MOGA is developed with the novelty of genetic algorithms such as tournament selection, ring crossover, and adaptive bit string mutation for discovering the optimal path. For the successive generations, the population is selected using the tournament. The genetic operator, crossover operator, is applied for swapping the input string to obtain offspring which is called ring crossover. Consequently, another GA operator mutation is carried out randomly on the sequence to achieve diversity in the population. Again the individual fitness criterion is verified to obtain an optimal path from the population. An experimental study of the proposed MRPS-MOGA is carried out with different cases. The result reveals that the proposed MRPS-MOGA is better in the case of optimal path selection with lower time complexity. Based on the experimental analysis, MRPS-MOGA is a more efficient mobile robot path with higher safety, reduced energy consumption, lesser traveling time than the existing methods.

Automated summary

Mobile robot path planning is a significant research area in industrial automation, aiming to determine the optimal path for a robot to reach its destination while avoiding obstacles. This paper proposes a mobile robot path search method based on multi-objective genetic algorithm, which considers safety, distance, smoothness, traveling time, and collision-free path as objectives to obtain the optimal path. Experimental results show that the proposed method has lower time complexity and achieves higher safety, reduced energy consumption, and shorter traveling time compared to existing methods.