# Clemson University - Autonomous Systems {% hint style="danger" %} Welcome! This page is no longer active, and will not be updated. \[RIP 2017-2021] cheers! -Alex {% endhint %} ![](https://2257299444-files.gitbook.io/~/files/v0/b/gitbook-legacy-files/o/assets%2F-MDFWl6FJNNzxuDA_xAi%2F-MDGGhqdkQ45qwviXbol%2F-MDGxosDZELfrLFoghPc%2FClemson%20Autonomous%20Systems%20Logo.png?alt=media\&token=b999b2f6-6916-4ac5-bbaa-a1956b840bca) The Autonomous Systems research team is directed by Dr. Yiqiang Han, a research assistant professor in the department of mechanical engineering at Clemson University. The research has been supported by the creative inquiry and undergraduate research offices since its founding in 2018. Since then, the project has grown and more students from other disciplines have started participating, advancing our capabilities as a team. Students have gained hands on experience experimenting with physical prototypes and have been able to gain practical skills through our research. Members of our team have gone on to continue their work as undergraduate students into graduate school and have been able to perform physical testing of their algorithms, fully validating their works. We have experience working with autonomous drones and ground vehicles that use ackermann and skid steer geometries. We have demonstrated how machine learning can be used to control the navigation of these vehicles using deep neural networks, and have written our own variations of existing path planning algorithms that produce more computational and energy efficient solutions compared to existing works. Undergraduate students join our group with little to no background experience in robotics, and we are able to teach them the tools and principles necessary to develop their own autonomous robots. ## Development Approach Our team is primarily made up of undergraduate students from various disciplines, which presents different learning curve challenges when trying to begin development on our prototype vehicles. In order to reduce the time spent troubleshooting the same issues that other members have solved in setting up their packages and build environments we utilize docker containers. This way we can quickly deploy existing build environments and directly integrate new packages into those containers. ![An example of multiple AI Machines and their underlying software layers](https://2257299444-files.gitbook.io/~/files/v0/b/gitbook-legacy-files/o/assets%2F-MDFWl6FJNNzxuDA_xAi%2F-MEDtGSrMPWKEMpw500h%2F-MEDxEBn8F3rB7g0VfGj%2FArchitecture%20Overview.png?alt=media\&token=29142103-584d-4d68-bec0-cf61578780fe) ![Generic AI machine software and hardware component overview](https://2257299444-files.gitbook.io/~/files/v0/b/gitbook-legacy-files/o/assets%2F-MDFWl6FJNNzxuDA_xAi%2F-MEDtGSrMPWKEMpw500h%2F-MEDylA7HYUrk4WfExPm%2FMachine%20Overview.png?alt=media\&token=ecb5cbbe-df90-4278-a382-4b8919612cc4) --- # Agent Instructions: Querying This Documentation If you need additional information that is not directly available in this page, you can query the documentation dynamically by asking a question. Perform an HTTP GET request on the current page URL with the `ask` query parameter: ``` GET https://clemson-autonomous-systems.gitbook.io/clemson-university-autonomous-systems/master.md?ask= ``` The question should be specific, self-contained, and written in natural language. The response will contain a direct answer to the question and relevant excerpts and sources from the documentation. Use this mechanism when the answer is not explicitly present in the current page, you need clarification or additional context, or you want to retrieve related documentation sections.