A Knee-Guided Evolutionary Algorithm Based Navigation Approach for Mobile Robots in Intelligent Manufacturing Scenarios

This paper proposes a novel navigation and optimization approach for mobile robots operating in intelligent manufacturing (IM) scenarios with unknown obstacles. First, we introduce a set of evaluation functions that simultaneously consider multiple metrics, including speed, security, sampling step, and obstacle avoidance ability, for scenarios with unknown dynamic obstacles. We then adopt a knee-guided multi-objective evolutionary algorithm capable of balancing parameter size and performance to optimize these conflicting objectives. Finally, we present a local navigation framework that integrates navigation and multi-objective optimization to enable obstacle avoidance in unstructured environments. The proposed algorithm is validated through simulations and practical scenarios, demonstrating its effectiveness in both static and dynamic scenarios. Note to Practitioners —The Human-cyber-physical System (HCPS) is the basic principle of the new generation of intelligent manufacturing (IM). It presents new requirements for the production method, and robot operate completely autonomously is expected to be the future solution for some complex tasks. However, traditional navigation methods often fail in these scenarios due to environmental changes and restricted workspaces. To address this challenge, this paper proposes a multi-objective optimization-based local navigation approach (MONA) that transforms the local navigation problem into a multi-objective optimization problem. The approach uses the knee-guided multi-objective evolutionary algorithm to optimize the control input for the mobile robot, ensuring safe and fast navigation in complex environments. The proposed approach has been validated through simulation and real scenario experiments, demonstrating its effectiveness.