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Fast Joint Multi-Robot Trajectory Optimization by GPU Accelerated Batch Solution of Distributed Sub-Problems.

Dipanwita Guhathakurta1, Fatemeh Rastgar2, M Aditya Sharma1

  • 1International Institute of Information Technology, Hyderabad, India.

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Summary
This summary is machine-generated.

This study introduces an efficient multi-robot trajectory optimizer for aerial swarms. It significantly reduces computation time and improves trajectory quality, enabling faster and smoother flight paths for multiple robots.

Keywords:
GPU accelerated optimizerbatch optimizationcollision avoidanceconvexmulti-robot trajectory optimizationobstacle avoidance

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Area of Science:

  • Robotics
  • Artificial Intelligence
  • Control Systems

Background:

  • Coordinating multiple robots in aerial swarms presents significant computational challenges.
  • Existing trajectory optimization methods often struggle with scalability and real-time performance for large swarms.

Purpose of the Study:

  • To develop a computationally efficient joint multi-robot trajectory optimizer.
  • To improve scalability and trajectory quality for aerial robot swarms.

Main Methods:

  • A novel approach decomposes joint optimization into parallelizable sub-problems.
  • Sub-problems are reformulated as Quadratic Programming problems with shared matrices.
  • GPU acceleration is utilized for batch solution updates via matrix-vector products.

Main Results:

  • The optimizer computes trajectories for tens of robots in a fraction of a second.
  • Demonstrates significant improvements in computation time and scaling with robot number.
  • Achieves superior trajectory quality, measured by smoothness and arc-length.

Conclusions:

  • The proposed joint trajectory optimizer offers a computationally efficient and scalable solution for aerial swarms.
  • This method enhances both the speed and quality of multi-robot trajectory planning.