Learning and Adaptation for Long-Term Autonomous Robotics Applications

点击翻译按钮获取中文摘要

The majority of today’s robots are unable to reliably operate in unknown, changing and generally uncontrolled environments. Their systems rely on knowing in advance the specifics of every possible situation they might encounter. They are not able to adapt to new situations. This is one of the main reasons why, to date, autonomous (mobile) robots have been primarily deployed in highly controlled environments (e.g. manufacturing plants or warehouses). To overcome this major limitation, the proposed research program will develop novel robotics algorithms and systems that extend robot operations to uncontrolled environments by enabling robots to learn and adapt. Such capabilities will lead to a major paradigm shift in robotics towards the long-term deployment of autonomous robots in uncontrolled real-world application scenarios such as inspection, remote sensing, resources monitoring, and space exploration. Assuming that useful a priori information about a robotic system and its environments is available, we focus on hybrid model-and-data-driven approaches, which aim to combine the best of two worlds: a priori model information assures safe operation initially (e.g. based on a robust model-based controller design), while based on the data collected during operation the model can be refined and the robot’s performance can be gradually improved. This approach blends the worlds of machine learning and data mining with classical control and estimation theory, and has proven to be successful in isolated scenarios. Recent work by the applicant has shown that difficult-to-model aerodynamic effects can be learned effectively and efficiently resulting in precise high-speed flight maneuvers. She has also shown that a ground robot can learn to compensate for unknown rough terrain. These results show that hybrid model-and-data-driven control is a promising new direction for robotics that has the potential to help bridge the gap from simple lab experiments to serious long-term service applications. We seek to enable reliable long-term single- and multi-robot applications in uncontrolled environments. To do so, we focus on two long-term objectives: (I) To develop learning algorithms and software that enable mobile aerial and ground robots to operate over the long term in challenging, uncontrolled real-world environments; and (II) To enable technology transfer of these techniques to real-world applications by focusing on the critical technology gaps and rigorous field testing needed to make this leap. In order to address these objectives, it will be necessary to carry out both extensive theoretical analyses and a significant experiment campaign using ground and aerial robots. The two objectives above can be further divided into several shorter-term goals to be addressed in the next five years: • designing task-specific learning strategies for operations in uncontrolled environments; • devising methods that enable task-independent learning; • developing collaborative learning schemes for heterogeneous and homogeneous teams of robots; and • demonstrating successful learning on different robot platforms over extended periods of time. The applicant will collaborate with Canadian corporations and organizations such as MDA, CSA, Clearpath and PrecisionHawk. These collaborations facilitate extensive testing of the algorithms on commercial aerial and ground robot platforms and provide the applicant with feedback from end users. Ultimately, based on the applicant’s findings there will be tremendous opportunities for Canadian industries to develop personal, enterprise and space robotic solutions, and diversify their current portfolio into multi-billion application areas such as remote sensing, natural resources monitoring, and surveillance.