Robot Planning System Using Regression Models for Target Position

The planning system automatically determines target positions by having the robot perform exploration movements and acquire sensor data itself, without requiring manual teaching. The robot autonomously identifies reachable positions and selects optimal target positions based on the acquired spatial information, enabling self-service operation that improves planning efficiency while reducing time consumption.

The system performs preliminary exploration movements and data acquisition to build a spatial map before actual task execution. By预先 acquiring information about the environment and reachable positions, the robot prepares target position candidates in advance, which accelerates the subsequent target position determination process and improves overall planning efficiency.

The robot performs preliminary exploration movements to acquire spatial information and build a map of the environment before executing actual task movements. By预先 identifying the spatial relationships and positions of other objects through sensor data acquisition during exploration, the robot can plan target positions that avoid interference with other objects, thus reducing harmful effects while maintaining automation.

The system continuously acquires sensor data during exploration and uses this feedback information to update the spatial map and adjust target position selections. The feedback mechanism allows the robot to recognize obstacles and other objects in the environment, enabling it to choose target positions that avoid interference while maintaining automatic operation.

The planning system segments the target position determination process into distinct stages: exploration movement execution, sensor data acquisition, spatial map construction, reachable position identification, and target position selection. By dividing the complex process into manageable segments, the system can perform comprehensive evaluation at each stage without overwhelming computational complexity, thereby maintaining high selection accuracy while managing system complexity.

The system performs preliminary data acquisition and spatial map construction before target position selection. By预先 organizing environmental information and identifying reachable positions through simulation, the system reduces the complexity of the final target position evaluation, enabling accurate selection without excessive computational burden during the critical decision-making phase.