Research Goals and Objectives

The Autonomy Research Institute (ARI) at Texas A&M University-Corpus Christi (TAMU-CC) will develop advanced autonomous systems capable of operating effectively in air, land, sea, and space environments. ARI’s initial focus is ensuring safe and sustainable integration into the National Airspace System (NAS).

Core Autonomous Systems Applied Research and Development

  • Develop robust control algorithms for autonomous vehicles to navigate complex and dynamic terrains.
  • Design intelligent systems capable of real-time decision-making and adaptation to unforeseen circumstances.
  • Create advanced sensor fusion techniques for accurate environmental perception and object recognition.
  • Explore energy-efficient solutions for extended autonomous operation.
  • Develop secure communication protocols for reliable data transmission and control.
  • Investigate human-autonomous system interaction to ensure safe and effective collaboration.
  • Conduct comprehensive testing and validation of autonomous systems in diverse operational environments.
  • Develop and validate deconfliction techniques to ensure safe vehicle operations.

Advanced Air Mobility (AAM)

  • UAS Traffic Management (UTM): UTM is a "traffic management" ecosystem for uncontrolled operations that is separate from, but complementary to, the FAA's Air Traffic Management (ATM) system. UTM is how airspace will be managed to enable multiple drone operations conducted beyond visual line-of-sight (BVLOS), where air traffic services are not provided.
  • Regional Air Mobility (RAM): RAM will increase the safety, accessibility, and affordability of regional travel while building on the extensive and underutilized federal, state, and local investment in our nation’s local airports..
  • Airworthiness: Implement and develop airworthiness processes and standards to ensure safe UAS operations, resulting in statements of airworthiness for all test-site UAS.
  • Command and Control: Develop reliable command and control link solutions to operate UAS systems safely and securely.
  • Human Factors: Optimize UAS control station layout and certification based on human factors principles.
  • Detect and Avoid (DAA): Develop effective ground and airborne detect-and-avoid technologies for UAS.
  • Environmental Impact: Assess and mitigate environmental impacts of UAS operations at launch and recovery sites.
  • Integration: Develop strategies for seamless integration of UAS into the NAS.
  • Data Management: Establish robust data management and analysis capabilities for UAS operations.

By addressing these objectives, we aim to advance the state-of-the-art in autonomous systems and contribute to the safe and efficient integration of UAS into the national airspace system.

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