The broader impact/commercial potential of this project is to commercialize previous research in tensegrity robots for new markets in disaster response. Future deaths and injuries of both victims and first responders during disaster rescues in unchartered, risky environments could be prevented by rapidly deploying sensor robots that use semi-autonomous technology to explore the regions of disasters, provide surveillance to inform first responders, and assist in the rescue of victims until human first responders can arrive. Current emergency response procedures are time-consuming, requiring first responders to don protective suits and hand place sensors to obtain air quality readings. Current disaster robots cannot be rapidly deployed and can be ineffective in navigating surface obstacles and climbing steep slopes to reach areas of interest. Instead, tensegrity sensor robots dropped from aerial vehicles, such as drones or helicopters, can land in dangerous, often difficult-to-reach areas and immediately transmit surveillance and environmental data. With this information, first responder teams can better understand the situational hazards and plan how best to ameliorate the emergency saving lives, while reducing costs and property damage. This proposed technology will also have broader impact in use for scientific and commercial monitoring and surveillance as well.
This Small Business Innovation Research (SBIR) Phase II project will advance the development of tensegrity robots, their durability, and their propulsion, de-risking the technology for the commercial market. This work will focus on expanding the features and structures of two robotic platforms: (1) stationary robots that provide persistent monitoring in one location and (2) mobile robots that are capable of ground travel over rough terrain (rubble, rocks, slopes). Finite element analysis/computational fluid dynamics simulations will be used to reduce the robots' weight and improve impact-resilience and portability. Software improvements will focus on enhancing speed capabilities and energy efficiency for rough terrain locomotion. Control engineers will integrate robust Model Predictive Control to improve path planning and enhance the robots' locomotion in a wide range of topologies and environments. Improved software algorithms and user interfaces will provide first responders with summarized data analytics for real-time assessments in the field as well as deliver cloud-server support. Integrated playback functionality and data-driven learning will improve post-situation evaluations. With technologies that provide live 360-degree video feeds and greater incident intelligence, this project will improve rescue outcomes in future disaster recovery operations.
This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.
