Motion Control for Autonomous Heterogeneous Multiagent Area Search in Uncertain Conditions

Area of Science:

• Robotics
• Artificial Intelligence
• Search and Rescue

Background:

• Multi-robot systems offer advantages in search missions but require sophisticated motion control.
• Existing methods struggle with sensor variations, target uncertainty, and complex maneuvers for autonomous multi-agent search.

Purpose of the Study:

• To develop a robust methodology for autonomous 2-D search using multiple unmanned vehicles.
• To create a motion control algorithm that accounts for agent heterogeneity and environmental uncertainties.

Main Methods:

• A probabilistic model of target occurrence is continuously updated using agent sensor data.
• The Heat Equation-Driven Area Coverage (HEDAC) method guides agent motion via a potential field gradient for near-ergodic exploration.
• The centralized algorithm handles heterogeneous agents in motion and sensing capabilities.

Main Results:

• HEDAC demonstrated superior performance in all three simulated search missions compared to alternative methods.
• HEDAC-controlled searches achieved comparable detection rates in approximately half the time of conventional strategies.
• Scalability tests showed that increasing agent numbers with HEDAC provides a necessary speed-up, despite minor efficiency decreases.

Conclusions:

• The HEDAC method offers a flexible and competent solution for autonomous multi-agent search missions.
• The proposed methodology provides a strong foundation for real-world applications in areas like search and rescue.