CppCon 2024

Motion planning is a crucial capability for robots operating in unstructured environments, enabling them to navigate and interact with their surroundings safely and efficiently. However, motion planning algorithms are computationally expensive, often requiring hundreds of thousands or millions of evaluations of subroutines, such as forward kinematics, collision checking, and nearest neighbor lookup.

This talk will present an approach to accelerating motion planning subroutines, specifically focusing on forward kinematics calculations, through compile-time optimizations. The proposed method leverages code generation techniques to directly translate Unified Robot Description Format (URDF) into optimized C++ code. Additionally, by generating hardware-specific code, such as CUDA for parallel computations, significant performance gains can be achieved. Throughout the presentation, I will compare and benchmark the compiled URDF to a naive implementation of forward kinematics and evaluate the performance impact of certain design decisions, such as memory layout and compiler flags.

The presentation will demonstrate how the aforementioned code generation process can be seamlessly integrated into the C++ build process using CMake and Jinja templates, enabling easy adoption. Attendees will gain insights into the benefits of compile-time optimizations for performance-critical applications and learn how to leverage this approach to enhance the efficiency of their own robotics or computationally-intensive systems.