A hybrid search algorithm for swarm robots searching in an unknown environment

Area of Science:

• Robotics
• Artificial Intelligence
• Swarm Intelligence

Background:

• Robot swarms require efficient search strategies for unknown environments.
• Nature-inspired foraging behaviors offer effective coordination mechanisms.
• Current methods face challenges in large-scale area coverage and resource management.

Purpose of the Study:

• To propose a novel hybrid search method for improving swarm robot efficiency.
• To integrate nature-inspired foraging behaviors with advanced search algorithms.
• To enhance target detection and area coverage in unknown environments.

Main Methods:

• A hybrid approach combining random search for global exploration and dynamic particle swarm optimization (DPSO) for local refinement.
• Synchronous updating of DPSO parameters via a local communication mechanism.
• Dividing large search areas into subregions with a target utility function for prioritized searching.

Main Results:

• The proposed method enhances search efficiency by dynamically switching between random and DPSO algorithms.
• Local communication effectively reduces communication overhead and overcomes hardware limitations.
• Subregion division and utility-based prioritization improve target residence time and overall search performance.

Conclusions:

• The hybrid random search and dynamic PSO strategy significantly improves swarm robot search efficiency.
• Nature-inspired coordination and intelligent communication are key to effective swarm robotics.
• This approach offers a robust solution for complex search and exploration tasks in unknown environments.