Current Projects

The project addresses fundamental research to develop novel autonomous and adaptive monitoring systems for natural resources across large spatio-temporal scales using networks of aerial robots equipped with visual sensors. The robots will be able to autonomously adapt to the observed phenomena and to multiple, often conflicting, time-varying constraints and mission specifications, greatly improving the precision of the collected data, and allowing spatio-temporal scalability. This is achieved by considering tradeoffs between visual sensing, real-time trajectory planning, decision making, and system optimization.

Key research topics addressed by ARCS:

• Multi-robot information gathering

• Multi-agent decision making

• Field testing and evaluation

This project investigates the mechanisms that uncertainties in robot-environment interactions affect (small) robot behavior. Small robot motion is more stochastic since errors at the actuators and uncertain interactions with the environment amplify errors in pose. The goal is to introduce a platform-agnostic, data-driven modeling framework to quantify uncertainty and subsequently exploit it via control for reliable robot navigation under uncertainty. The specific aims are to: 1) extract dynamics using limited data for modeling uncertain systems; 2) synthesize uncertainty-aware model-based controllers based on derived reduced-order models; and 3) test and validate theoretical analysis and derived models and control algorithms with aerial, ground, and marine robots. Spectral methods are used to extract spatio-temporal dynamics and to quantify uncertainty. A model-reference adaptive control scheme utilizes extracted dynamics and uncertainty for reliable robot navigation. While the basic principles developed in this research are grounded on small robots, this project's findings may generalize to larger robots with limited sensing and noisy actuation.

The goal of this project is to develop and deploy heterogeneous teams of autonomous robots (specifically, aerial and ground robots) to enable frequent and dense sampling in the field. The motivating hypothesis is that an increase in sampling density and frequency can indicate noticeable spatiotemporal variability in water potential that would remain otherwise undetected because of insufficient sampling resolution. To this end, the project incorporates 1) development of robotized pressure chamber, 2) visual sensing for accurate determination of leaf water potential, 3) multi-robot coordination and planning, and 4) extensive field testing and evaluation across multiple crop species.

Key research topics addressed by ARCS:

• Mechanism design for robotic in-situ leaf sampling and analysis

• Multi-robot task allocation and field exploration/mapping

• In-field testing and evaluation

The project investigates how compliance embedded into a legged robot can be harnessed to facilitate control and computation, with an eye to enabling efficient and resilient navigation in real agricultural fields. Research activities innovate along three key foundational robotics research directions. 1) Hardware design and dynamic modeling: The project offers fundamental insights and develops models regarding the effect of various forms of compliance on center of mass motion and gait stabilization for certain classes of legged robots and introduces new hardware designs that can harness compliance and enable principles of morphological computation. 2) Locomotion control: The project establishes compliance-aware legged locomotion controllers according to principles of whole-body and central pattern generator-based control to enable efficient closed-loop legged locomotion over a range of engineered and natural unstructured terrains. 3) Non-holonomic motion planning and autonomous navigation: The project develops non-holonomic motion planners that rely upon and utilize distinctive features of robot body morphology and embedded compliance for efficiency and resilience during autonomous legged locomotion over real agricultural fields. This research can transform the science and technology of autonomous legged robots by making them more efficient and resilient in their operation, and thus unlock legged robots' full potential in precision agriculture.