The path planning algorithm not only ensures that the mobile robot can avoid obstacles to reach the target point at a safe speed but also ensures that the mobile robot can quickly adapt to the complex changing environment. In this paper, the existing path planning algorithms of mobile robots are analyzed, and then the fusion path planning algorithm is studied. The main work is summarized as follows: A* algorithm is used to complete global path planning and path smoothing, the basic principle of dynamic window method (DWA) is studied, and the dynamic constraints of mobile robots are discussed. The shortcomings of the dynamic window method, i.e., that the dynamic window method does not have the ability to self-learn and self-adapt in the dynamic and unknown environment, are analyzed through simulation experiments. In addition, by studying the basic principle of deep reinforcement learning, the essence and characteristics of DWA algorithm and DQN algorithm are analyzed, which provides ideas for the fusion path planning algorithm based on DQN and DWA. Finally, to cope with the complex and changeable environment and improve the real-time obstacle avoidance ability of mobile robots, a fusion path planning algorithm based on DQN and DWA is proposed. First, the dynamic window method is used to limit the driving of the mobile robot directly to the velocity space. Then, a deep Q network is designed and trained to approximate the state-action value function of the mobile robot, then dynamically interact with the environment to adjust the robot’s moving trajectory in real time, and finally, find the optimal path for the robot. The simulation results show that the fusion path planning algorithm proposed in this paper ensures that the mobile robot has strong generalization ability and robustness under the complex, variable, and dynamic unknown environment. Compared with the existing DWA and DQN algorithms, the proposed fusion path planning algorithm achieves better path planning performance with less training times, shorter computation time, and faster convergence speed.

The ant colony algorithm is an approach for path planning that is used in multiple industries. This paper proposes an improved robot path planning method, referred to as Improved-ACO. First, the heuristic information calculation is optimized to increase algorithm efficiency and shorten convergence time. Secondly, an enhanced Tanh function is included into the heuristic information, allowing dynamic modifications during the search period and preventing the algorithm’s convergence to local optima. Then, a novel pheromone update strategy is employed to accelerate convergence. Next, a novel pheromone diffusion mechanism is proposed to strengthen the ants’ search capability. Additionally, a collision avoidance system and improved B-spline curves are included for path smoothing, guaranteeing that the optimized pathways conform to the robot’s kinematic limitations. Simulation results indicate that the improved ant colony algorithm decreases the average number of turns by $37.5\%$ and accelerates convergence time by $39.45\%$ relative to existing methods across diverse map dimensions. The experiments confirm that Improved-ACO achieves rapid convergence and constructs smooth curves that adhere to the robot’s kinematic constraints. Consequently, Improved-ACO is confirmed as an efficient and adaptable route planning method for robotic navigation under complicated situations.