Current projects

All Access Surface Mobility on Small Bodies

The last Planetary Science Decadal Survey has prioritized missions to small bodies (e.g., asteroids, comets, Phobos, Deimos, and other Kuiper belt objects). Their in-situ exploration, in particular, would be pivotal to shed light on the origin of the Solar System, and would be instrumental in developing technologies for the future manned exploration of Mars. As a consequence, there is currently a strong interest in developing robotic platforms capable of fast and precise mobility on the surface of small bodies. The aim of this line of research is to develop a unique spacecraft/rover hybrid capable of precise all surface mobility on small bodies, and to study mission scenarios enabled by this concept. This project is in collaboration with J. Castillo (JPL), I. Nesnas (JPL), J. Hoffman (MIT), and R. Binzel (MIT).

Spacecraft Motion Planning

We devise real-time, efficient and dependable algorithms for spacecraft autonomous maneuvering, with a focus on dynamic and cluttered environments (which may arise, e.g., due to debris or outgassing activity).  Specifically, this project is aimed at devising a technology for the online planning of trajectories in proximity operations, which, together with reliable environmental sensing and autonomous high-level decision-making, is a key enabler for autonomous spacecraft navigation. As a radical departure from traditional methods, we are leveraging recent algorithmic advances in the field of robotic motion planning to spacecraft control.


Distributed Control of Spacecraft Formations

Significant interest in formation flying started to develop in the late 1990s, and today formation flying is a critical technology for many planned and future missions of NASA, the DoD, and ESA.
We have developed a class of Cyclic algorithms for formation flying, for which (i) a rigorous stability analysis is possible, and (ii) the information requirements are minimal. In particular, we studied control policies that only rely on relative measurements, since in deep-space missions global measurements may not be available. Our control algorithms have been tested on the International Space Station in 2008/2009. This is joint work with E. Frazzoli (MIT), J. Ramirez (MIT), and D. Miller (MIT)


Autonomous Mobility on Demand

This project aims to advance scientific knowledge on the modeling, analysis, and control of robotic networks consisting of unmanned vehicles autonomously operating in a coordinated fashion to fulfill service requests such as the transportation of people or goods. To work efficiently, such systems must overcome allocation and scheduling challenges that, in practice, can create backups, unacceptable wait times, and detrimental cascade effects. We cast the problem within the framework of spatial queuing theory, and investigate theoretical models and real-time control methods to optimally allocate vehicles to service requests. Theory and control algorithms will be applied for the design, system-wide control, and economic assessment of autonomous mobility-on-demand systems. Such systems represent a transformative, rapidly developing mode of transportation where electric, self-driving shuttles transport urban passengers and provide a mobility option to people unable or unwilling to drive.


Real-Time Kinodynamic Planning

We have developed an algorithmic framework that allows for real-time motion planning/obstacle avoidance for high-speed robotic systems, such as quadrotors. This framework, which addresses a critical need in many cutting edge robotic technologies, is enabled by a novel application of machine learning algorithms to estimate cost-limited reachable sets for dynamical systems. As a validating test, we've implemented the framework on an autonomous quadrotor and tasked it with avoiding high speed obstacles (such as a fencing blade) while navigating to objectives in an indoor environment. This stands  as, arguably, the first demonstration of truly online motion planning for a quadrotor system.


Dynamic Vehicle Routing for Robotic Networks

The last decade has seen an increasing number of application domains where networks of uninhabited vehicles (UVs) are required to fulfill tasks that arise dynamically in time, are spatially distributed over an environment, and possibly require some type of additional on-site service. Examples include UAV systems, robotic environmental monitoring, and sensor networks.

In collaboration with E. Frazzoli (MIT), F. Bullo (UCSB), and S. Smith (U. Waterloo), Prof. Pavone developed a novel approach for the solution to this problem, called algorithmic queueing theory, which relies upon methods from queueing theory, combinatorial optimization, and stochastic geometry.