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

The task allocation of multiple robot chemical inspection plays a vital role in enhancing the inspection efficiency of robots, and it is crucial for the timely detection of hazardous factors in chemical enterprises and the elimination of potential production risks. In this paper, an integer programming model of multiple heterogeneous inspection robot task assignment (MHRTA) problem is established based on the consideration of the constraints on the applicability of sensors carried by robots to inspection tasks. Given the NP-hard nature of the MHRTA problem, this study proposes a multiple strategy enhanced artemisinin algorithm to solve the proposed MHRTA model. Improving the position update strategy of artemisinin molecules in the comprehensive elimination phase of the artemisinin algorithm by incorporating concepts from the slime mold algorithm, and incorporates a nonlinear curve as the probability factor during the later consolidation phase, while introducing self-adaptive t distribution mutation to enhance the quality of solutions. Furthermore, Considering the discrete combinatorial optimization characteristics of the MHRTA problem, a two-layer encoding scheme is adopted to create a connection between the encoding space and the solution space, specifically for addressing multiple robot task allocation and inspection Hamiltonian routing, a variable neighborhood search strategy is embedded in the artemisinin optimizer to improve its computational efficiency. In eight test cases, the proposed method was evaluated against other algorithms and CPLEX solvers. The experimental results verified that the proposed method has strong optimization ability and stability in solving MHRTA problems.