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Taping over varying ground profiles requires careful adaptation to achieve accurate measurements. On smooth, level ground with minimal vegetation, the tape can rest directly on the ground. Here, the taping team, typically consisting of a head and a rear tapeman, coordinates their positions with clear communication. The rear tapeman holds the tape at the starting point and guides the head tapeman toward a range pole placed beyond the endpoint, using hand or voice signals to ensure alignment.On...
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Related Experiment Video

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A Spine Robotic-Assisted Navigation System for Pedicle Screw Placement
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Online Aerial Terrain Mapping for Ground Robot Navigation.

John Peterson1, Haseeb Chaudhry2, Karim Abdelatty3

  • 1Virginia Tech Unmanned Systems Laboratory, Blacksburg, VA 24060, USA. jrpeter@vt.edu.

Sensors (Basel, Switzerland)
|February 21, 2018
PubMed
Summary
This summary is machine-generated.

This study introduces a cooperative system where an aerial drone guides a ground robot

Keywords:
air–ground cooperationmulti-robot coordinationunmanned aircraft

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Area of Science:

  • Robotics
  • Artificial Intelligence
  • Computer Vision

Background:

  • Autonomous systems require efficient path planning.
  • Real-time environmental awareness is crucial for ground vehicles.
  • Cooperative control between aerial and ground robots offers enhanced capabilities.

Purpose of the Study:

  • To develop and evaluate a collaborative unmanned aerial and ground vehicle system.
  • To leverage aerial imagery for real-time ground vehicle path planning.
  • To minimize data exchange and registration errors in a multi-vehicle system.

Main Methods:

  • An aerial vehicle acquires imagery, creating an orthomosaic map.
  • Terrain classification estimates navigation costs for the ground vehicle.
  • Real-Time Kinematic Global Positioning System (RTK GPS) registers vehicles in a common frame.
  • Onboard image processing on the aerial vehicle minimizes data transfer.

Main Results:

  • The system enables energy-efficient path generation for the ground vehicle.
  • Real-time path planning is informed by the aerial vehicle's overhead view.
  • The architecture minimizes data exchange between the aerial and ground vehicles.
  • Registration errors between the vehicles were quantified.

Conclusions:

  • The collaborative system effectively enhances ground vehicle path planning.
  • Onboard processing and RTK GPS integration ensure efficient operation.
  • This approach offers a robust solution for autonomous navigation in complex terrains.