We introduce RobotCore, an architecture to integrate hardware acceleration in the widely-used ROS 2 robotics software framework. This architecture is target-agnostic (supports edge, workstation, data center, or cloud targets) and accelerator-agnostic (supports both FPGAs and GPUs). It builds on top of the common ROS 2 build system and tools and is easily portable across different research and commercial solutions through a new firmware layer. We also leverage the Linux Tracing Toolkit next generation (LTTng) for low-overhead real-time tracing and benchmarking. To demonstrate the acceleration enabled by this architecture, we design an intra-FPGA ROS 2 node communication queue to enable faster data flows, and use it in conjunction with FPGA-accelerated nodes to achieve a 24.42% speedup over a CPU.
We introduce robomorphic computing; a methodology to transform robot morphology into a customized hardware accelerator morphology. In this work, we (i) present this design methodology; (ii) use the methodology to generate a parameterized accelerator design for the gradient of rigid body dynamics; (iii) evaluate FPGA and synthesized ASIC implementations; and (iv) describe how the design can be automatically customized for other robot models. Our FPGA accelerator achieves speedups of 8x and 86x over CPU and GPU latency, and maintains an overall speedup of 1.9x to 2.9x deployed in an end-to-end coprocessor system. ASIC synthesis indicates an additional factor of 7.2x.
Modern embedded systems are intelligent devices that involve complex hardware and software to perform a multitude of cognitive functions collaboratively. Designing such systems requires us to have deep understanding of the target application domains, as well as an appreciation for the coupling between the hardware and the software subsystems.This course is structured around building “systems” for Autonomous Machines (cars, drones, ground robots, manipulators, etc.). For example, we will discuss what are all the hardware and software components that are involved in developing the intelligence required for an autonomous car?