Workspace modeling and feasibility analysis of robotic manipulators based on multi-strategy optimized SCSO

Abstract

As a core component in modern manufacturing and automation, the workspace of a robotic manipulator directly determines the effectiveness of its task range and path planning. This study proposes a Multi-Strategy Workspace Modeling of Manipulator based on Sand Cat Swarm Optimization (MSWM-SCSO) algorithm to achieve more efficient and accurate workspace modeling. The proposed model enhances the original SCSO framework by incorporating multiple strategies: adaptive parameter adjustment balances exploration and exploitation; a hybrid mutation mechanism helps escape local optima; elite opposition-based learning expands boundary search; and a distributed ring neighborhood maintains population diversity. These strategies jointly promote both global search and local optimization. Performance validation is conducted on 23 standard benchmark functions, with further evaluation under complex and dynamic scenarios using the CEC2017 and CEC2022 benchmarks. Results show that MSWM-SCSO outperforms other state-of-the-art models across multiple metrics. For unimodal functions, convergence is achieved in as few as 48 generations. In the CEC2017 benchmark, the number of function evaluations is reduced by ∼24.3% compared to traditional SCSO, indicating improved computational efficiency. In the CEC2022 benchmark, the environmental adaptability score improves to 0.992 over the second-best model, demonstrating superior performance in dynamic environments. Application validation on four commonly used robotic manipulators further confirms the model’s advantages in volume error rate, boundary fitting accuracy, and coverage of singular regions in high-degree-of-freedom manipulators. Considering both algorithmic performance and practical applicability, MSWM-SCSO meets the modeling requirements of typical industrial robotic workspaces and demonstrates strong feasibility. This study provides a reference method for robotic manufacturers, automation system integrators, and engineering practitioners, offering a reliable geometric foundation for subsequent path planning, structural design, and task deployment.