CASER

The Coordinated Autonomous Systems for Exploration and Reconnaissance (CASER) project aims to develop fundamental research that can enhance the capabilities of the Unmanned Autonomous Systems (UAS) for exploration and reconnaissance across complex environments (ground and air). Robust multi-robot teams need to implement models of coordinated maneuvers and techniques that support the understanding of the surroundings and their characteristics, as well as to implement self-organizing behaviors and control strategies that generate paths/routes that can maximize cover, while maximizing team’s efficiency. Strategies will enable robot teams to create formations, identify terrain characteristics, obstacles, and environmental conditions, and perform structural assessments without compromising the mission by maximizing tactical maneuvers, minimizing exposure, and reacting successfully to contact. 

Project will address three main research areas: 

1. Distributed Control of Multi-Robot Systems and Multisensory Synthesis.

2. Models and Metrics for Robust Systems.

3. Information Processing and Fusion.

Institution: Texas A&M University-Corpus Christi, USA

Period: 2023 - 2027

Funding Agency: Department of Defense HBCU/MI - Army Research Office (DoD / ARO)

TEAM:

• Dr. Jose Baca (PI), Assistant Professor, Department of Engineering, Texas A&M University-Corpus Christi

• Dr. Pablo Rangel (Co-PI), Assistant Professor, Department of Engineering, Texas A&M University-Corpus Christi

• Dr. Miguel Cid Montoya (Co-PI), Assistant Professor, Department of Engineering, Texas A&M University-Corpus Christi

• Dr. Thang Nguyen (Co-PI), Assistant Professor, Department of Engineering, Texas A&M University-Corpus Christi

RESEARCH AREAS:

1. Distributed Control of Multi-Robot Systems and Multisensory Synthesis.

   1. Distributed non-linear systems for autonomous area coverage and multi-robot task allocation, as well as techniques for computing constraints and derivation of optimal solutions under uncertainty and local information.

   2. Seamless interaction between human and UAS teams by developing strategies to intuitively operate multiple UAS by a single operator via multisensory synthesis.

   3. Collision-free coordination control of a multi-robot system with obstacles of different sizes and formations in a parallel fashion.

   4. Robust control strategies to increase the adaptability of a multi-robot system during communication constraints.
      
      

2. Models and Metrics for Robust Systems.

   1. Testing and Evaluation (T&E) techniques for UAS that can better assist users in the selection and acquisition of such technologies, as well as the implementation of qualitative methodologies to define UAS Safe Autonomy Integrity Levels.

   2. Risk assessment-based bubble theory that can assist in measuring exposure of the UAS team and increase autonomy. 

3. Information Processing and Fusion.

   1. Structural health inspection techniques using multimodal UAS-acquired data.

   2. Digital twins of damaged structures for risk-assessment usage decision making, and for live monitoring of damaged structures during emergencies.

   3. Environmental and weather emulation and analysis techniques harnessing multimodal UAS-acquired data.