Simulation, Design, and Experimental Implementation of Dynamic Visual Servoing Syntheses for Field Applications

Abstract

The labor shortage in agriculture, exemplified by a 30% decline in the availability of seasonal workers in Europe over the past decade, coupled with a 20% increase in production costs due to inflation and resource scarcity, presents significant challenges to modern food production systems. Traditional manual operations for tasks such as sensor deployment, crop monitoring, and harvesting are labor-intensive, time-consuming, and often inefficient. These inefficiencies arise because traditional methods rely heavily on human labor, which is prone to fatigue and error, and struggle to adapt to the unpredictable and unstructured nature of field environments, such as irregular terrains and varying weather conditions. This PhD project hypothesizes that the integration of advanced robotics, AI-driven visual servoing, and robust simulation frameworks can address these challenges by enabling precise and autonomous manipulation tasks in agriculture. To achieve this, the research will focus on customizing an industrial robotic manipulator capable of handling complex agricultural tasks under variable conditions. By combining AI-based image processing and object detection for real-time identification and localization, 3D structure reconstruction for precise spatial understanding, and synchronized robotic control for adaptive task execution, the project directly addresses challenges such as occlusion by enhancing visibility through multi-angle imaging, variable lighting by leveraging robust illumination-invariant algorithms, and obstacle avoidance by integrating advanced path-planning strategies with environmental sensing. Simulation environments will be further employed to optimize collision-free path planning and motion strategies. To ensure practical applicability, these simulations will incorporate data from real-world scenarios, such as field conditions and object interactions, to enhance their accuracy and relevance. Extensive programming using MATLAB and ROS will enable seamless coordination between visual feedback and robotic movements, bridging the gap between simulated and real-world operations. This interdisciplinary approach, validated through experimental testing in (simulated) field settings, promises to enhance efficiency, sustainability, and productivity in agriculture, contributing to healthier ecosystems and communities.