Optimization of power utilization in multimobile robot foraging behavior inspired by honeybees system

Area of Science:

• Robotics
• Artificial Intelligence
• Swarm Intelligence

Background:

• Mobile robots rely on finite battery power, necessitating frequent recharging and human intervention.
• Cooperative information sharing among robots can mitigate energy limitations and improve operational efficiency.

Purpose of the Study:

• To develop an integrated intelligent system for mobile robot energy management inspired by honeybee foraging behavior.
• To enhance the operational efficiency of multi-robot systems through cooperative power station identification and resource management.

Main Methods:

• Simulated a swarm of mobile robots using a honeybee foraging-inspired algorithm.
• Implemented a system for robots to share discovered power station locations.
• Evaluated energy consumption and time efficiency during the foraging and recharging process.

Main Results:

• Cooperative foraging significantly reduced energy consumption and time required for battery recharging.
• Information sharing among robots led to optimized foraging behavior.
• Increased operational uptime for mobile robots due to efficient energy management.

Conclusions:

• The proposed honeybee-inspired foraging strategy effectively enhances energy management in swarm mobile robots.
• Cooperative behavior and information sharing are crucial for overcoming energy limitations in autonomous multi-robot systems.
• Optimized foraging allows robots to dedicate more time to their primary tasks, increasing overall productivity.