近年來，自動駕駛的議題受到高度的關注，一台車子要可以達到完全的自動駕駛需要包含：自身定位、導航、路徑規劃、障礙物偵測與避障。本研究重點著眼於路徑規劃與複雜的未知環境避障，並使用U-字型障礙物作為非凸地圖之範例。在模擬環境中，使用單一感測器來測障礙物並得到自身位置與靜態障礙物的相對關係，引入Danger Zone的概念，即透過自走車與障礙物的相對速度，建立碰撞曲線，在以此概念發展動態物體即時閃避功能。最後利用Simultaneous Localization And Mapping (SLAM)演算法建立地圖，進而達成兼具探索與閃避移動障礙物之功能。

Autopilot technology is not yet fully mature. An autopilot vehicle requires self-localization, navigation, path planning, obstacle detection and obstacle avoidance. Path planning and obstacle avoidance in complex unknown environments are the focus of in this research, with a use U-shaped obstacle as an example of a non-convex map. In the simulation environment, single sensor is used to detect obstacles and get the relationship between the positions of the autonomous car and obstacles. In path planning and obstacle avoidance, we introduce the concept of a danger zone. which builds a collision curve based on the relative velocity between the autonomous car and obstacles. We also develop an avoidance function for dynamic obstacles, and then use the Simultaneous Localization And Mapping(SLAM) algorithm to build maps so that the autonomous car can explore these and avoid moving objects.

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