Path Optimization

Rapid ABB Path Optimization

Overview

This project explores the intersection of robotics and live music.

System Architecture

Problem Statement

Traditional safety verification algorithms for robotic systems are computationally expensive, often taking seconds to minutes to verify a single trajectory. This makes them impractical for real-time applications where robots need to react to dynamic obstacles and changing environments within milliseconds.

Technical Approach

GPU Parallelization Strategy

We implemented a massively parallel collision checking algorithm that distributes workspace verification across thousands of CUDA threads. Each thread is responsible for checking a subset of the robot’s configuration space against potential obstacles.

// Simplified CUDA kernel for parallel collision checking
__global__ void verifyTrajectoryKernel(
    const Trajectory* trajectory,
    const Obstacle* obstacles,
    bool* results
) {
    int idx = blockIdx.x * blockDim.x + threadIdx.x;
    // Parallel verification logic
}

Key Innovations

  1. Spatial Hashing: Implemented GPU-friendly spatial hashing for fast obstacle queries
  2. Warp-Level Optimization: Leveraged CUDA warp intrinsics for efficient memory access
  3. Adaptive Discretization: Dynamically adjust verification granularity based on risk assessment

Left: CPU-based verification (250ms). Right: GPU-accelerated verification (8ms)

Results

Our GPU-accelerated approach achieved:

  • 30x speedup compared to optimized CPU implementations
  • Real-time verification at 120Hz for 7-DOF manipulators
  • Scalability to complex environments with 1000+ obstacles
  • Energy efficiency with 3x lower power consumption per verification

Implementation Details

The system was implemented using:

  • CUDA 11.8 for GPU acceleration
  • C++17 for the core verification algorithms
  • ROS2 for integration with robotic platforms
  • Custom memory management for zero-copy data transfers

Video Demo

Real-time safety verification in a cluttered environment

Future Work

  • Extension to multi-robot scenarios
  • Integration with learning-based motion planning
  • Hardware acceleration using custom FPGAs
  • Deployment on embedded GPU platforms (Jetson)

Publications

This work has been submitted to ICRA 2025.

Acknowledgments

This research was conducted in collaboration with [Institution Name] and supported by [Grant/Funding Source].

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