Bio-Intuitive Navigation for Agricultural Robotics: A Case Study into sEMG-Based Control of the CAMeL Platform
Year: 2026
Type: conference-paper
Abstract: The rapid modernization of precision agriculture has introduced sophisticated and autonomous platforms. However, the human-machine interface (HMI) for these systems often remains a bottleneck. Complex Radio Frequency (RF) controllers, characterized by multi-axis joysticks and non-intuitive switchgear, impose a steep cognitive load on operators, particularly in the demographics typical of the farming sector. This study proposes a bio-intuitive control framework for the CAMeL agricultural robot, a quad-motored platform developed at Obuda University, and describes a methodology to decode neuromuscular intent using Surface Electromyography (sEMG) to translate natural hand gestures into steering commands. The system employs a signal conditioning pipeline that addresses specific artifacts i.e. DC offset and baseline wander, followed by a hybrid Deep Learning architecture. By cascading 2D Convolutional Neural Networks (CNN) for spatial feature extraction from 8 sEMG channels, with Long Short-Term Memory (LSTM) networks for temporal sequence modeling, the presented model achieves robust classification of dynamic gestures achieving accuracies of 80.5% and 84.8% for horizontal and vertical wrist rotation, respectively. While the fist activity is estimated with 99.59% of accuracy. Preliminary results suggest that the proposed approach offers a fluid and natural alternative to complex joystick maneuverability.
Institution: Obuda University
Language: English
Subfield: Biomedical Engineering
Field: Engineering
Domain: Physical Sciences
SDG: Zero hunger
Open Access status: hybrid
OpenAlex ID: W7204209731