Aiming at the problem that premature convergence and random mutation of genetic algorithm（ GA） lead to infeasible path

an improved genetic algorithm for robot path planning is proposed. The algorithm uses a new mutation operator

which adjusts the mutation nodes according to the overall fitness to ensure that the mutation produces a feasible path

so as to increase population diversity and avoid premature convergence. Firstly

the robot path planning model is established by the grid method. Next

optimize the initial population structure so that the initial population does not intersect the obstacle. Finally

the penalty function coefficient is introduced in the fitness function

and high penalty is given to the infeasible path to ensure the algorithm works on the feasible path. The simulation results show that the proposed algorithm not only outperforms the traditional genetic algorithm but also outperforms some improved genetic algorithms. Using this algorithm

global and local path planning can be implemented in static and dynamic environments

and the optimal or suboptimal paths of the robot can be obtained.