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Decentralized nonlinear model predictive control-based flock navigation with real-time obstacle avoidance in unknown

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Summary
This summary is machine-generated.

This study enhances robot navigation using nonlinear model predictive control (NMPC) with local obstacle avoidance. The improved NMPC enables safe navigation in complex, obstructed environments for robotic fleets.

Keywords:
distributed controlflockinghardware-in-the-looplocal obstacle avoidancenonlinear MPC

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Area of Science:

  • Robotics
  • Control Systems
  • Artificial Intelligence

Background:

  • Distributed nonlinear model predictive control (NMPC) is crucial for robot fleet navigation.
  • Existing methods require enhancement for realistic obstacle avoidance in unknown environments.

Purpose of the Study:

  • To integrate a realistic local obstacle-avoidance strategy into a distributed NMPC framework for robot fleets.
  • To develop efficient point cloud processing for real-time obstacle detection and response.

Main Methods:

  • Incorporation of local obstacle-avoidance constraints using point cloud data within the NMPC framework.
  • Development of a point cloud processing technique involving directional filtering and down-sampling for computational efficiency.
  • Validation through 3D simulations in Gazebo and hardware-in-the-loop (HIL) testing on embedded platforms.

Main Results:

  • Demonstrated safe navigation of robot agents through obstructed environments.
  • Confirmed the computational efficiency of the point cloud processing technique.
  • Validated the practical feasibility of the NMPC scheme on embedded systems via HIL simulations.

Conclusions:

  • The proposed NMPC scheme with integrated local obstacle avoidance is effective for decentralized robotic systems.
  • The method is suitable for real-world deployment in complex and dynamic environments.
  • Efficient point cloud processing is key to enabling real-time obstacle avoidance in NMPC.