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The emergence of “robotics in the wild” has triggered a wave of recent research in hardware and software to boost robots’ compute capabilities. Nevertheless, research in this area is hindered by the lack of a comprehensive benchmark suite. In this paper, we present RTRBench, a benchmark suite for robotic kernels. RTRBench includes 16 kernels, spanning the entire software pipeline of a wide swath of robots, all implemented in C++ for fast execution. Together with the suite, we conduct an evaluation of the workloads at the architecture level. We pinpoint the sources of inefficiencies in a modern robotic processor when executing the robotic kernels, along with the opportunities for improvements. The source code of the benchmark suite is available in https://cmu-roboarch.github.io/rtrbench/. 

RACOD is an algorithm/hardware co-design for mobile robot path planning. It consists of two main components: CODAcc, a hardware accelerator for collision detection; and RASExp, an algorithm extension for runahead path exploration. CODAcc uses a novel MapReduce-style hardware computational model and massively parallelizes individual collision checks. RASExp predicts future path explorations and proactively computes its collision status ahead of time, thereby overlapping multiple collision detections. By affording multiple cheap CODAcc accelerators and overlapping collision detections using RASExp, RACOD significantly accelerates planning for mobile robots operating in arbitrary environments. Evaluations of popular benchmarks show up to 41.4× (self-driving cars) and 34.3× (pilotless drones) speedup with less than 0.3% area overhead. While the performance is maximized when CODAcc and RASExp are used together, they can also be used individually. To illustrate, we evaluate CODAcc alone in the context of a stationary robotic arm and show that it improves performance by 3.4×–3.8×. Also, we evaluate RASExp alone on commodity many-core CPU and GPU platforms by implementing it purely in software and show that with 32/128 CPU/GPU threads, it accelerates the end-to-end planning time by 8.6×/2.9×.