This week I mainly worked on researching obstacle avoidance algorithms that can be integrated with the ToF sensor in our system. I reviewed several possible approaches and evaluated their feasibility based on the characteristics of ToF distance measurements. Since the ToF sensor provides accurate real-time distance data, I focused on algorithms that can effectively utilize this information for obstacle detection and avoidance. For example, I looked into methods such as simple threshold-based avoidance, potential field methods, and local path adjustment algorithms. I also compared the algorithms in terms of response speed, computational complexity, and ease of implementation on our current hardware platform. Some methods provide better navigation but require more computation, while simpler methods can respond quickly and are easier to implement with embedded systems.
For this week, the progress aligns with our current schedule. In the next step, we will need to consider how these algorithms could be adjusted to better fit with our need, as well as how to integrate the tof obstacle avoidance with our camera sensing detection to accomplish the whole work flow of detection hand gesture and then navigate to the user’s position.