Research on path optimization and efficiency improvement of intelligent robot distribution system in library circulation service

The traditional manual distribution mode faces many challenges, such as slow service response during peak hours and rising labor costs. The successful application of intelligent robot technology in logistics, medical care and other fields provides a new idea for library service optimization. This study improves the efficiency of library circulation service by constructing intelligent robot distribution system, optimizing path planning and task scheduling strategy. In the aspect of environmental modeling and demand analysis, the method of combining topological graph with grid map is studied to construct the environmental cost function and guide the robot to choose the path with the lowest cost. The task scheduling module distinguishes the urgency of tasks through priority queue design, and optimizes task completion time and energy consumption by using mixed integer programming model. In the aspect of cooperative control of multi-robots, the distributed communication framework based on ROS combines UWB and WiFi to achieve high-precision positioning, and ensures efficient cooperation between robots through space-time corridor strategy and conflict resolution mechanism. In the design of path optimization algorithm, the improved A* algorithm is adopted in global path planning, and the dynamic weight mechanism is introduced to optimize the heuristic function. The local path planning adopts dynamic window approach (DWA), and the obstacle avoidance ability and driving stability are dynamically optimized through reinforcement learning. Multi-robot collaborative path optimization introduces temporal and spatial consistency constraints, and combines time window allocation mechanism to improve the efficiency and safety of overall path execution. The results of effectiveness evaluation show that the optimized robot system has significantly improved in response time, path efficiency and energy consumption compared with the traditional manual delivery and unoptimized version. The average task response time is reduced by 60.7%, the path overlap rate is reduced by 77.5%, the energy consumption per book is reduced by 34.6%, and the task completion rate is increased to 98.3% in peak period. The field test shows that the robot system is significantly superior to the traditional manual mode in energy efficiency, and the energy consumption of single emergency delivery is reduced by 66.5%, and the energy consumption of batch transportation is reduced by 28.2%. The robustness test of the system shows that the success rate of autonomous recovery of the robot in the face of dynamic obstacles is as high as 94.5%, and the success rate of recovery in the case of network communication interruption is 82.6%. Compared with similar studies, this study has excellent performance in response time, path efficiency and system stability, with a comprehensive score of 91.7, showing significant advantages.