We have designed a programmable architecture to accelerate collision detection and graph search, two of the principal components of robotic motion planning. The programmability enables the architecture to be applied to a wide range of different robots and motion planning applications. We present the architecture of our accelerator and describe and evaluate its microarchitecture implementation.

We have developed a hardware accelerator for motion planning, a critical operation in robotics. In this paper, we present the microarchitecture of our accelerator and describe a prototype implementation on an FPGA. We experimentally show that the accelerator improves performance by three orders of magnitude and improves power consumption by more than one order of magnitude. These gains are achieved through careful hardware/software co-design. We modify conventional motion planning algorithms to aggressively precompute collision data, as well as implement a microarchitecture that leverages the parallelism present in the problem.