CPS: Synergy: Distributed Sensing, Learning and Control in Dynamic Environments

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The objective of this project is to improve the performance of autonomous systems in dynamic environments, such as disaster recovery, by integrating perception, planningparadigms, learning, and databases. For the next generation of autonomous systems to betruly effective in terms of tangible performance improvements (e.g., long-term operations,complex and rapidly changing environments), a new level of intelligence must be attained.This project improves the state of robotic systems by enhancing their ability to coordinateactivities (such as searching a disaster zone), recognize objects or people, account foruncertainty, and "most important" learn, so the system's performance is continuously improving. To do this, the project takes an interdisciplinary approach to developing techniques in core areas and at the interface of perception, planning, learning, and databases to achieve robustness. This project seeks to significantly improve the performance of cyber-physical systems for time-critical applications such as disaster monitoring, search and rescue, autonomous navigation, and security and surveillance. It enables the development of techniques and tools to augment all decision making processes and applications which are characterizedby continuously changing operating conditions, missions and environments. The projectcontributes to education and a diverse engineering workforce by training students at theUniversity of California, Riverside, one of the most diverse research institutions in US and an accredited Hispanic Serving Institution. Instruction and research opportunitiescross traditional disciplinary boundaries, and the project serves as the basis for undergraduate capstone design projects and a new graduate course. The software and testbeds from this project will be shared with the cyber-physical system research community,industry, and end users. The project plans to present focused workshops/tutorials at major IEEE and ACM conferences. The results will be broadly disseminated through the project website.For further information see the project website at: http://vislab.ucr.edu/RESEARCH/DSLC/DSLC.php