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Trust-region filtered sequential convex programming for multi-UAV trajectory planning and collision avoidance.

Guangtong Xu1, Teng Long2, Zhu Wang3

  • 1Department of Precision Instrument, Tsinghua University, Beijing 100084, PR China.

ISA Transactions
|December 28, 2021
PubMed
Summary
This summary is machine-generated.

This study introduces a new method, trust-region filtered sequential convex programming (TRF-SCP), to make multi-unmanned aerial vehicle (UAV) trajectory planning more efficient by reducing computational load.

Keywords:
Adaptive trust-region updatingInactive constraintsMulti-UAV trajectory planningSequential convex programmingTrust-region based filter

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Area of Science:

  • Robotics and Control Systems
  • Artificial Intelligence
  • Optimization Algorithms

Background:

  • Multi-unmanned aerial vehicle (UAV) trajectory planning is computationally intensive.
  • Existing methods often struggle with complex environments and numerous constraints.

Purpose of the Study:

  • To develop a computationally efficient algorithm for multi-UAV trajectory planning.
  • To reduce the complexity of trajectory optimization problems by filtering unnecessary constraints.

Main Methods:

  • Introduced trust-region filtered sequential convex programming (TRF-SCP).
  • Developed a trust-region based filter to remove inactive collision-avoidance constraints.
  • Implemented an adaptive trust-region updating mechanism to handle infeasible iterations.

Main Results:

  • TRF-SCP significantly reduces computational burdens in multi-UAV trajectory planning.
  • The method effectively handles various obstacle types (cylindrical, spherical, conical, polygonal).
  • TRF-SCP outperforms standard SCP and Guaranteed Sequential Trajectory Optimization in computational efficiency.

Conclusions:

  • TRF-SCP offers a practical and efficient solution for complex multi-UAV trajectory planning.
  • The algorithm's ability to eliminate redundant constraints enhances performance.
  • Indoor flight experiments validate the practicability of the TRF-SCP approach.