|
With the continuous development of science and technology, the application range of intelligent robots is becoming more and more extensive, and indoor robots have become one of the hot spots in research. How to make indoor robots accurately and effectively detect and avoid irregular objects and complete path planning tasks has become the key to research. Initially, a variety of sensors including lidar, ultrasonic sensors, and cameras, are used to obtain environmental information. Lidar is used to provide high-precision distance measurement data, ultrasonic sensors are used to detect obstacles at close range, and cameras are used to obtain visual information and identify complex environmental features. Then, the sensor fusion algorithm based on Kalman filter is used to synthesize these multi-source data, eliminate the redundancy and inconsistency between the sensor data, and form a more accurate and comprehensive environment model. In the path planning stage, the improved A* algorithm is used to preliminarily plan the global path to ensure the optimality and feasibility of the path. Then, the Dijkstra algorithm was used to refine and optimize the local path to improve the smoothness and security of the path. In order to achieve real-time obstacle avoidance, dynamic adjustment is carried out by combining the dynamic window method (DWA) and the artificial potential field method (APF). The DWA method calculates the optimal control command in real time according to the current motion state to ensure the flexibility and response speed of the robot in complex environments. The APF method further improves the robustness of obstacle avoidance by introducing a virtual potential field to guide the robot away from the obstacle and move towards the target point. From our extensive experimental results, we can observe that the proposed method significantly improves the performance of the robot in obstacle avoidance and path optimization.
|
