Research

Introduction

The primary objective of creating robots is to efficiently perform beneficial tasks that contribute to human activities and technological progress. These tasks include search and rescue operations, spatial exploration, merchandise shipment, garbage collection, handling toxic waste, and serving as service robots and personal assistants, among others. The Robotics Lab is dedicated to educating both graduate and undergraduate students specializing in robotics engineering through the execution of research projects. The Lab adopts a theoretical and practical training approach, emphasizing the fundamentals in systematic project development. Various types of engineering robotic projects have been successfully implemented in our laboratory, covering distributed robotic architectures, real-time operating system (RTOS) embedded systems, mechanical locomotion devices, sensor fusion architectures, and robot dynamics and control. This Lab, in particular, experiences a high demand from students enrolled in Mechatronics Engineering programs, reflecting its reputation for offering valuable and hands-on experience in the field of robotics.

Locomotion models play a critical role in defining how a robotic platform interacts with its environment. They not only influence the efficiency of movement in terms of stability and controllability but also shape the way the robotic platform moves. On the other hand, kinematic models take into account the constraints of locomotion, establishing the geometry of the robot's movement regardless of the underlying causes. However, dynamic models or mobility equations are employed to develop intelligent control software. These models go beyond kinematics, considering the forces and torques involved, and are instrumental in crafting sophisticated control strategies for the robot.

Another aspect of critical importance is the computational organization of the robots' intelligence, which is manifested in both the hardware and the operating system. Furthermore, the capacity for robot perception involves utilizing sensory information to carry out cognitive processes.

For a mobile robot to achieve autonomous task performance, the localization process is crucial in addressing questions like: Where does it come from? Where is it currently? And where is it headed? Consequently, the ability of the robot to determine its location becomes the cornerstone of autonomy, providing intelligent robots with an understanding of their mobility. Sensory information is obtained through algorithms and pattern recognition, extracting data from native sensors. Sensing models play a valuable role in multi-sensory fusion schemes. Robotics sensors are classified into proprioceptive (odometers, inclinometers, gyroscopes, accelerometers, etc.), exteroceptive (LADAR, sound, touch, vision, proximity, etc.), and proprio-exteroceptive (GPS). These sensors serve as the means for robots to perceive and measure the world in which they operate. Creating mobile robots for various control modalities (ground, aerial, or aquatic) requires a synergistic fusion of diverse areas of engineering and science. The fundamental purpose of creating robots is to enable them to perform useful and efficient tasks that benefit human activities and progress. These tasks include search and rescue, spatial exploration, merchandise shipment, garbage collection, handling toxic waste, and serving as service robots or personal assistants. To achieve autonomy in task execution, the robot's location process becomes vital for understanding its origin, current position, and destination. Location, therefore, forms the core of autonomy, focusing on creating intelligent robots with a sense of mobility. Locomotion models are critical for establishing the interaction of the robotic platform with the environment, determining movement efficiency in terms of stability and controllability. Kinematic models consider the restrictions of locomotion to determine the geometry of the robot's movement, irrespective of the underlying causes. Dynamic models or mobility equations are instrumental in developing intelligent control software. Additionally, the computational organization of the robot's intelligence, manifested in both hardware and the operating system, is of paramount importance for implementation. Finally, the robot's perception capacity involves using sensory information to perform cognitive processes. Sensory information is acquired through extraction algorithms and pattern recognition from native sensors, with sensing models playing a crucial role in multi-sensory fusion schemes. The sensors used in robotics, categorized as proprioceptive, exteroceptive, and proprio-exteroceptive, serve as the means for the robot to observe and interact with the world.