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MPI CyberMotion Simulator: Implementation of a Novel Motion Simulator to Investigate Multisensory Path Integration in Three Dimensions
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Optimal Motion Planning in GPS-Denied Environments Using Nonlinear Model Predictive Horizon.

Younes Al Younes1, Martin Barczyk1

  • 1Department of Mechanical Engineering, University of Alberta, Edmonton, AB T6G 1H9, Canada.

Sensors (Basel, Switzerland)
|August 28, 2021
PubMed
Summary

This study introduces a new autonomous navigation system for drones in GPS-denied subterranean environments. It uses Nonlinear Model Predictive Horizon (NMPH) for safe, real-time path planning around obstacles.

Keywords:
drone vehicledynamic obstacle avoidancefeedback linearizationmotion plannernonlinear model predictive approachpath planning

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Area of Science:

  • Robotics
  • Autonomous Systems
  • Navigation

Background:

  • Autonomous navigation in unknown, dynamic, GPS-denied environments is challenging.
  • Real-world conditions include nonlinear dynamics, real-time computation, 3D complexity, and moving obstacles.

Purpose of the Study:

  • To develop a motion planning approach for autonomous drones in GPS-denied subterranean environments.
  • To integrate a novel local path planning method with a graph-based planner.

Main Methods:

  • Utilized Nonlinear Model Predictive Horizon (NMPH) for local path planning.
  • NMPH employs a plant dynamics model and feedback linearization for feasible, optimal, smooth, collision-free paths.
  • Augmented with efficient algorithms for global planning, obstacle mapping, and avoidance.

Main Results:

  • The proposed approach successfully generated safe trajectories respecting vehicle dynamics and dynamic obstacles.
  • Demonstrated real-time operation and collision avoidance in simulations.
  • Preliminary real flight tests in unexplored GPS-denied environments validated performance.

Conclusions:

  • The integrated motion planning approach enables autonomous drone navigation in challenging GPS-denied subterranean environments.
  • NMPH provides a robust method for real-time, dynamic path planning.
  • The system shows potential for real-world applications requiring autonomous exploration.