Optimizing Grasping Precision for Industrial Pick-and-Place Tasks Through a Novel Visual Servoing Approach

The integration of robotic arm manipulators into industrial manufacturing lines has become common, thanks to their efficiency and effectiveness in executing specific tasks. With advancements in camera technology, visual sensors and perception systems have been incorporated to address more complex operations. This study introduces a novel visual serving control system designed for robotic operations in challenging environments, where accurate object pose estimation is hindered by factors such as vibrations, tool path deviations, and machining marks. To overcome these obstacles, our solution focuses on enhancing the accuracy of picking and placing tasks, ensuring reliable performance across various scenarios. This is accomplished by a novel visual servoing method based on the integration of two complementary methodologies: a technique for object localization and a separate approach for precise control through visual feedback, leveraging their strengths to address the challenges posed by the industrial context and thereby improving overall grasping accuracy. Our method employ feedback from perception sensors to adjust the control loop efficiently, enabling the robotic system to adeptly pick and place objects. We have introduced a controller capable of seamlessly managing the detection and manipulation of various shapes and types of objects within an industrial context, addressing numerous challenges that arise in such environments.

The Dilemma of Decision-Making in the Real World: When Robots Struggle to Make Choices Due to Situational Constraints

In order to demonstrate the limitations of assistive robotic capabilities in noisy real-world environments, we propose a Decision-Making Scenario analysis approach that examines the challenges due to user and environmental uncertainty, and incorporates these into user studies. The scenarios highlight how personalization can be achieved through more human-robot collaboration, particularly in relation to individuals with visual, physical, cognitive, auditory impairments, clinical needs, environmental factors (noise, light levels, clutter), and daily living activities. Our goal is for this contribution to prompt reflection and aid in the design of improved robots (embodiment, sensors, actuation, cognition) and their behavior, and we aim to introduces a groundbreaking strategy to enhance human-robot collaboration, addressing the complexities of decision-making under uncertainty through a Scenario analysis approach. By emphasizing user-centered design principles and offering actionable solutions to real-world challenges, this work aims to identify key decision-making challenges and propose potential solutions.