Accelerating RRT Motion Planning Using TCAM
Yuxin Yang, Shiqi Lian, Xiaoming Chen, Yinhe Han · 2020
Real-time motion planning is important for robot movement. In motion planning, path search and collision detection are two performance bottlenecks. In this paper, we adopt a range-based matching scheme with ternary content-addressable memories (TCAMs) to accelerate the processes of both nearest neighbor search and collision detection. In our approach, the nearest node search and collision detection can be both processed in a few TCAM lookup cycles. The evaluation shows that the TCAM-based accelerator is 236× faster than CPU for motion planning tasks. It is 5.4× faster and at least 8.8× more energy-efficient than a state-of-the-art dedicated ASIC-based accelerator.