Mobile autonomous robotic insects such as dragon flies and virtual bugs that chiefly rely on energy scavenging frequently languish due to the excessive power dissipation by digital processors while executing the underlying software algorithms used in navigation through dynamic environments. This NSF research will study the underlying adaptive control-theoretic software algorithms to replace them by energy-efficient hardware capable of brain-like reinforcement learning. The proposed biology-inspired hardware mounted on robotic insects will be beneficial to the defense related applications, first responders to disaster areas for remote monitoring, and mapping of hostile and hazardous environments. Such algorithmic hardware will also benefit applications in flight control, biochemical processes, power generators, and telecommunications. Educational improvement and research integration is a major goal of this integrative and cross-disciplinary research, thereby providing opportunity to upgrade the current Computer Science and Engineering curriculum and train engineering workforce of the future who will apply knowledge from multiple disciplines to design ultra-low-power autonomous nanosystems.
Adaptive dynamic programming (ADP) algorithms are used in many large-scale engineering applications involving adaptive optimal control systems and signal processing. However, the success of ADP on the microprocessor has limited its scope in mobile computing and autonomous robotic insects where the battery energy preservation is paramount. Reasonable ADP algorithms cannot run on portable low power machines because ADP needs a large memory bank and requires parallel processing for a reasonable runtime. This research aims to develop methods for porting higher-level algorithms from software implementation on the microprocessor to a mixed signal CMOS chip design at first and then an ultra-low-energy chip design by combining CMOS and memristor technologies.
