This week we created our Project Proposal slideshow.\u00a0 Matt gave the presentation on Monday.\u00a0 We met on Thursday and discussed the general design of our motion planning accelerator and how it will interface with the simulator and with a future perception system and robotic arm.\u00a0 I reviewed the paper on RRT, found possible robotic arm options, and read about open source perception solutions.<\/p>\n
We have generally defined the interface between the accelerator and the simulator and will meet on Sunday (2\/11\/24) in order to begin implementation.\u00a0 The general idea is to partition the state space into a grid of 3D coordinates.\u00a0 Using the representation we can easily distinguish the free vertices, boundary vertices, and the vertices where object exists \/ collisions may occur.\u00a0 The RRT implementation will then take in the coordinates of the start and goal position as well as updated collision data.\u00a0 Only communicating the delta in collision data will allow for minimized data transfer.<\/p>\n
I did work on the initialization of our git repository.\u00a0 I have initialized C files for the baseline RRT implementation and Python files for the simulator.\u00a0 Using the ctypes library I have enables the C RRT implementation to be called by the python simulator.\u00a0 Doing this allows us to standardize the interfaces between the modules of our design and allows the simulator to be in Python.\u00a0 This greatly simplifies the work of visualization and testing.\u00a0 Along with this I wrote a simple shell script and started writing a README to document the repository.<\/p>\n
I am beginning to consider the actual representation of the state space.\u00a0 The matrices we will be working with we be quite large and sparse.\u00a0 How we implement this is an important design decision.\u00a0 I am considering using a Compressed Sparse Row Format but this will complicate the accelerator and software we write.\u00a0 I am also considering using a more coarsely grained partition of the state space.\u00a0 This will decrease accuracy and make the path smoothing more complicated.\u00a0 Using a coarsely grained partition of the state space will be more computationally efficient but will likely lead to less optimal paths.<\/p>\n","protected":false},"excerpt":{"rendered":"
This week we created our Project Proposal slideshow.\u00a0 Matt gave the presentation on Monday.\u00a0 We met on Thursday and discussed the general design of our motion planning accelerator and how it will interface with the simulator and with a future perception system and robotic arm.\u00a0 I reviewed the paper on RRT, found possible robotic arm … <\/p>\n