Ricardo Huaman

Researcher

Cardiff University

Cardiff, UK

I graduated with a Bachelor's degree in Electronic Engineering from the Universidad Privada Antenor Orrego (UPAO) in Peru in 2022.

During my time at UPAO, I contributed to projects within the Multidisciplinary Research Laboratory, focusing on autonomous navigation for mobile robots in complex environments and the development of computer vision systems to address challenges in the agroindustry.

I am driven by the endless possibilities technology offers. I invite you to explore my website to learn more about my work or to get in touch regarding collaboration opportunities.

Key Areas of Interest: Robotics, Computer Vision, Deep Reinforcement Learning, Control, and Optimization.

news

Jan 19, 2024	I participated as a volunteer member of the staff at Science Clubs Peru!
Dec 3, 2023	I was selected for the REPUcs 2024 program!
Sep 13, 2023	Two of our research posters have been accepted for presentation at IROS 2023!
Aug 19, 2023	I won 2nd and 3rd place in research competitions at CONEIMERA 2023!
Jul 3, 2023	Our robotics work was spotlighted at Innovation Week 2023!

latest posts

Jun 08, 2023	Setting Up and Running microROS on Raspberry Pi Pico

selected publications

IEEE Xplore

Autonomous Navigation of a Four-Wheeled Robot in a Simulated Blueberry Farm Environment

RJH Kemper, C Gonzalez, and SRP Gardini.

IEEE, Dec 2022

Abs HTML PDF

Fruit sampling for blueberry harvesting projections are labor-intensive and if not properly done cause delay and economic loss throughout the entire supply chain. The use of robots could be a promising tool to address these issues. However, field tests for robots in agricultural environments are not always readily available. This paper presents a simulation for a ROS based four-wheeled agricultural robot which navigates autonomously through blueberry bushes rows. The autonomous navigation system comprises a Simultaneous Localization and Mapping algorithm (LeGO-LOAM), a path planning algorithm (A*) and a path tracking algorithm (an ad hoc algorithm). Its performance is assessed using the ATE metric in a simulated blueberry farm environment in Gazebo simulator. The proposed navigation system allowed the robot to navigate successfully in this environment with the highest error reaching 500 mm and 250 mm for the x and y Euclidean components respectively. The presented simulation methodology will expedite development of agricultural robots and ultimately aid in overcoming the aforementioned issues.