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GPS surveying methods vary in application, accuracy, and data collection techniques, catering to diverse surveying and mapping needs. Static GPS, kinematic GPS, and real-time kinematic (RTK) surveying are widely used. Each technique offers distinct advantages.Static GPS involves placing one receiver at a known reference point and another at the target point. It collects exact positional data by observing multiple satellite ranges over an extended period, achieving centimeter-level accuracy for...
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Area of Science:

  • Robotics
  • Artificial Intelligence
  • Remote Sensing

Background:

  • Multi-agent remote sensing in GPS-denied and partially observable environments presents significant challenges.
  • Efficiently locating survivors or surveying points of interest requires robust navigation and exploration strategies.

Purpose of the Study:

  • To present a novel framework for multi-agent target-finding.
  • To address the complexities of decentralized planning and control in unknown environments.

Main Methods:

  • The framework separates planning and control problems.
  • Planning utilizes decentralized multi-agent graph search with an online Partially Observable Markov Decision Process (POMDP) solver.
  • Control employs Deep Reinforcement Learning (DRL) for local continuous-environment exploration.

Main Results:

  • The proposed framework successfully enabled multiple agents to locate targets in large, simulated environments.
  • The system demonstrated effectiveness despite unknown obstacles and environmental obstructions.
  • The approach proved robust in complex, partially observable scenarios.

Conclusions:

  • The combined POMDP planning and DRL control framework offers a viable solution for multi-agent target-finding.
  • The approach is adaptable for various time-sensitive remote-sensing applications, including disaster response and hazardous environment surveys.