Power inspection is crucial to ensure a stable power system. Manual inspection is time-consuming and prone to errors, so intelligent methods like machine inspection greatly improve efficiency and accuracy. However, machine inspection faces challenges in path planning due to unexpected situations. To address this, a hybrid path planning algorithm that combines improved ant colony and dynamic window methods is proposed. Implemented in a power inspection robot dog, the algorithm enhances inspection efficiency. Simulation results demonstrate its advantages in both single-task and multi-task global path planning, reducing path length, turning nodes, iterations, and running time. Local path planning experiments show successful obstacle avoidance. The practical application of the robot dog confirms its ability to navigate around basic and complex obstacles. Overall, the proposed method has good applicability to power inspection robot dog’s path planning and navigation.

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