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Model predictive path integral for decentralized multi-agent collision avoidance.

Stepan Dergachev1,2, Konstantin Yakovlev1,2

  • 1HSE University, Moscow, Russia.

Peerj. Computer Science
|December 16, 2024
PubMed
Summary
This summary is machine-generated.

This study introduces a generalized collision avoidance method for multi-agent systems, enhancing Model Predictive Path Integral (MPPI) control with Optimal Reciprocal Collision Avoidance (ORCA) constraints. The approach ensures safety for diverse robot kinematics.

Keywords:
Collision avoidanceDecentralized multi-agent navigationDecentralized multi-agent systemsKinematic constraintsModel predictive path integralMulti-robot systemsSampling-based optimzation

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Area of Science:

  • Robotics
  • Artificial Intelligence
  • Control Systems

Background:

  • Decentralized multi-agent navigation requires effective collision avoidance.
  • Existing methods often ignore kinematic constraints or are model-specific.

Purpose of the Study:

  • To develop a generalized decentralized collision avoidance approach for arbitrary affine kinematic motion models.
  • To enhance the Model Predictive Path Integral (MPPI) algorithm for multi-agent scenarios.

Main Methods:

  • Integrating Optimal Reciprocal Collision Avoidance (ORCA) constraints into MPPI.
  • Deriving safe distributions via convex optimization.
  • Theoretical safety guarantees and empirical evaluation.

Main Results:

  • The proposed method successfully handles arbitrary affine kinematic models.
  • Outperforms state-of-the-art methods in various simulation setups.
  • Solves problem instances intractable for existing approaches.

Conclusions:

  • The generalized approach offers robust and safe collision avoidance for diverse multi-agent systems.
  • MPPI enhanced with ORCA constraints provides a significant advancement in decentralized navigation.