An improved artemisinin algorithm for task allocation in heterogeneous robot systems for chemical inspection.

Area of Science:

• Robotics
• Artificial Intelligence
• Operations Research

Background:

• Efficient task allocation for multiple robots is critical in chemical inspections for timely hazard detection and risk mitigation.
• The multiple heterogeneous robot task assignment (MHRTA) problem is complex due to sensor applicability constraints and its NP-hard nature.

Purpose of the Study:

• To develop an effective integer programming model for the MHRTA problem.
• To propose a novel, enhanced algorithm to solve the MHRTA model efficiently.

Main Methods:

• An integer programming model for MHRTA was established, considering sensor constraints.
• A multiple strategy enhanced artemisinin algorithm was developed, incorporating slime mold algorithm concepts, nonlinear probability factors, and self-adaptive t-distribution mutation.
• A two-layer encoding scheme and variable neighborhood search were integrated to address discrete optimization and improve computational efficiency.

Main Results:

• The proposed algorithm demonstrated strong optimization ability and stability in solving MHRTA problems.
• Experimental results on eight test cases validated the method's performance against other algorithms and CPLEX solvers.

Conclusions:

• The enhanced artemisinin algorithm provides a robust and efficient solution for the complex MHRTA problem in chemical inspections.
• The developed approach contributes to improving safety and efficiency in industrial chemical environments through advanced robotic task allocation.