For the problem of multitarget point inspection planning for mobile robots, this paper proposes a path planning algorithm that integrates the Improved Jump Point Search algorithm (JPS) with the Ant Colony Optimization algorithm (ACO). Firstly, an angle-guided factor is introduced into the evaluation function of the JPS algorithm to provide stronger directional guidance for the path. Then, considering the influences of path distance, smoothness, and safety on the evaluation function, a path with better comprehensive performance is obtained. Next, a bidirectional reverse jump point pruning rule is proposed to eliminate redundant nodes, further reducing path length and improving path smoothness. Finally, the path obtained from multi-objective optimization is used to replace the distance factor in the traditional Traveling Salesman Problem (TSP), and an adaptive ant colony algorithm is used to solve the multitarget point path planning problem. Simulation results show that the improved JPS algorithm has better comprehensive performance compared to the traditional JPS algorithm. When applied to multi-target point planning, it demonstrates stronger effectiveness and practicality.