Optimising human-robot collaborative teleoperation using adaptive fuzzy logic control and real-time motion intention estimation

Human-robot collaborative (HRC) teleoperation requires seamless integration of intention understanding and adaptive control to achieve natural, efficient, and reliable remote manipulation. Existing tele-operation models (TOMs) suffer from limited intention-prediction capabilities, static control parameters, and inadequate adaptation to dynamic operational conditions, resulting in reduced task performance and increased cognitive burden for the operator. The proposed TOM that combines real-time motion intention estimation using long short-term memory (LSTM) + adaptive fuzzy logic control to enhance human-robot collaboration. The proposed TOM leverages multimodal bio-signals, including electromyography, inertial measurement units, and joint kinematics, to decode operator intentions via temporal feature extraction and sequential classification. The LSTM-based classifier processes normalised feature vectors to predict discrete motion intentions with 91.4% accuracy across varying task complexities. Experimental validation using a 6-degree-of-freedom collaborative manipulator and 12 human participants demonstrates significant performance improvements over traditional TOM. The integrated system achieved a 93.5% task-completion success rate, 89% faster execution times, a 60% improvement in placement accuracy, and a 47% reduction in operator mental workload across low, moderate, and high-complexity manipulation tasks. Statistical analysis confirms highly significant improvements (P < 0.001) with large effect sizes across all performance metrics. The proposed model addresses fundamental limitations in HRC teleoperation by providing temporally-aware intention recognition and context-sensitive adaptive control, enabling more natural and efficient collaborative manipulation in remote and hazardous environments.