This week focused on completing the vehicle mechanics and Pose estimation portion of the project. With the new mecum wheels, the vehicles are able to move in any angle direction without angular rotation around the z-axis. This made it better for tracking the position of the vehicle with wheel encoders and IMU sensors due to less overall slipping of the wheels. The code for this was translated into ROS code and integrated to publish the information.
This week I also helped to get the issues with object detection fixed. This involved moving to a Tensor RT base for the neural network inference and switching to mobile net-v1 due to difficulty getting v2 to load. This allowed us to get a much higher FPS (around 30) as well as also drastically reduced the resource usage of the inference model. Further, I added the camera mount and made it to be adjustable so that the angle of the camera could be modified if needed.
Lastly we are tidying up the last couple of path planning loose ends. Namely, the original idea may be a bit unstable due to jittering frames of the object detection algorithm. Therefore I worked on a motion planning algorithm grounded in A* to potentially use if we are unable to smoothen the result from the object detection. This planning does not require too much time (around 0.1 seconds) so the solution should be functional if needed.
This next week will be typing up any loose ends and fully integrating the systems to be fully independent .