College - Author 1
College of Engineering
Department - Author 1
Electrical Engineering Department
Degree Name - Author 1
BS in Electrical Engineering
College - Author 2
College of Engineering
Department - Author 2
Electrical Engineering Department
Degree - Author 2
BS in Electrical Engineering
College - Author 3
College of Engineering
Department - Author 3
Electrical Engineering Department
Degree - Author 3
BS in Electrical Engineering
Date
6-2026
Primary Advisor
Siavash Farzan, College of Engineering, Electrical Engineering Department
Abstract/Summary
This report is dual-purpose. The first component presents the design of a fully equipped hardware testing suite for 2D nonholonomic mobile robotics: the Multi-Agent Universal Robotic Test Bench (MAURTB). This open-source architecture is intended to empower robotics researchers to collect and analyze data in physical experiments. As the field of robotics matures, it is increasingly important that findings verified in simulation are extended by demonstrating robustness in real-world scenarios. By adopting this predeveloped system, researchers can focus on algorithmic development while gaining a standardized way to compare performance against other systems. The second component is the hardware testing and verification of a novel formation control algorithm [1] and its extended obstacle-avoidance controller [2] using this testing suite. Similar controllers exist that feature safety guarantees and formation control; however, this controller provides the same capabilities in communication-limited environments. It combines a robust state estimator, which accurately estimates other agents’ velocities and orientations, with a decentralized formation controller that utilizes Control Barrier Functions to enforce interagent safety and obstacle avoidance. Operation in communication-limited environments enables applications such as search and rescue, agricultural crop monitoring, and remote mapping and surveying. The system uses multiple JetBots: open-source mobile robots built on the NVIDIA Jetson Nano single-board computer. Each agent and obstacle carries an AprilTag so that an overhead camera can measure precise poses. A centralized computer connected to the camera performs all control calculations, iterating a state estimator and computing angular velocity and linear acceleration commands for each agent; these commands are transmitted via UDP over the local campus network. Across the five tests presented, the average maximum position estimation error was 0.097 m, the average maximum velocity estimation error was 0.422 m/s, and the closest approach to an obstacle was 0.18 m and 0.24 m for the static and dynamic obstacle tests, respectively. Across all tests, the testbed executed the algorithms with a mean linear acceleration error of -0.0759 m/s2 and a mean angular velocity error of 0.0752 rad/s, demonstrating successful performance of the testbed under use.
URL: https://digitalcommons.calpoly.edu/eesp/733