# Multi-Task Agent Hybrid Control in Sparse Maps and Complex Environmental Conditions
## 3 Dynamic Environment Target Identification and Localization (DETIL)
### Identification of Unknown Obstacles  
Most current obstacle identification algorithms are designed based on the premise of flat ground or indoor environments, thereby eliminating the impact of ground depth changes on obstacle recognition [17], or utilize RGB-D information for obstacle detection and tracking [18]. However, the assumption of flat ground does not hold in dynamic and harsh environments, such as extraterrestrial body exploration. Additionally, since the obstacle avoidance program needs to run continuously throughout the agent's activity, using additional RGB image information for obstacle identification can impose extra computational burden on already resource-constrained intelligent agents. Therefore, there is a need to design an unknown obstacle identification algorithm that is robust in rough outdoor terrains using only depth information and does not consume excessive computational resources during operation.  
In computationally constrained environments, the task of identifying unknown obstacles should only focus on those obstacles that might impede the agent's movement in the future. Obstacles that are too far away or outside the path of travel can be disregarded, and the actual area of interest only occupies 10-25% of the depth image from the front-facing depth sensor. Therefore, in the task of unknown obstacle identification, it is first necessary to extract the Region of Interest (ROI) from the sensor's depth images. This approach can significantly reduce the number of pixels that need to be processed, thereby saving computational resources and increasing inference speed. Figure 3 presents a schematic diagram of the agent's Region of Interest (ROI) and the irrelevant areas.  
Locations in the depth image where the depth changes abruptly are mostly the edges of objects, where changes in depth in the horizontal direction reveal the vertical boundaries of objects, and changes in the vertical direction show their horizontal boundaries. The depth images obtained by depth sensors are arranged from near to far in the horizontal direction from bottom to top. If edges in both horizontal and vertical directions in the image are detected, edges in the horizontal direction are likely to be affected by the commonly occurring changes in ground depth, making them unreliable. Meanwhile, conventional obstacles will have at least one vertical boundary appearing in the depth image, so it is considered a straightforward and feasible approach to only detect abrupt changes in depth in the horizontal direction to identify the vertical boundaries of objects.  
Figure 3: The ROI area and irrelevant areas.  
After identifying the points of abrupt depth change in the horizontal direction within the ROI, it is possible to simply determine the presence of obstacles ahead by counting the number of these sudden change points. Once an obstacle is confirmed to be present ahead, further autonomous obstacle avoidance can be carried out based on the location and depth of these sudden change points in the depth image. The algorithmic flowchart for this strategy of unknown obstacle identification is shown in Figure 4.