Gaston A. Fiore

Indoor Multi-Vehicle Flight Testbed for Fault Detection, Isolation, and Recovery

This paper presents flight tests of a unique indoor, multi-vehicle testbed that was developed to study long duration UAV missions in a controlled environment. This testbed uses real hardware to examine research questions related to single and multi-vehicle health management, such as vehicle failures, refueling, and maintenance. The primary goal of the project is to embed health management into the full UAV planning system, thereby leading to improved overall mission performance, even when using simple aircraft that are prone to failures. The testbed has both aerial and ground vehicles that operate autonomously in a large test region and can be used to execute many different mission scenarios. The success of this testbed is largely related to our choice of vehicles, sensors, and the system’s command and control architecture, which has resulted in a testbed that is very simple to operate. This paper discusses this testbed infrastructure and presents flight test results from some of our most recent singleand multi-vehicle experiments.

A review of urban computing for mobile phone traces: current methods, challenges and opportunities

In this work, we present three classes of methods to extract information from triangulated mobile phone signals, and describe applications with different goals in spatiotemporal analysis and urban modeling. Our first challenge is to relate extracted information from phone records (i.e., a set of time-stamped coordinates estimated from signal strengths) with destinations by each of the million anonymous users. By demonstrating a method that converts phone signals into small grid cell destinations, we present a framework that bridges triangulated mobile phone data with previously established findings obtained from data at more coarse-grained resolutions (such as at the cell tower or census tract levels). In particular, this method allows us to relate daily mobility networks, called motifs here, with trip chains extracted from travel diary surveys. Compared with existing travel demand models mainly relying on expensive and less-frequent travel survey data, this method represents an advantage for applying ubiquitous mobile phone data to urban and transportation modeling applications. Second, we present a method that takes advantage of the high spatial resolution of the triangulated phone data to infer trip purposes by examining semantic-enriched land uses surrounding destinations in individuals motifs. In the final section, we discuss a portable computational architecture that allows us to manage and analyze mobile phone data in geospatial databases, and to map mobile phone trips onto spatial networks such that further analysis about flows and network performances can be done. The combination of these three methods demonstrate the state-of-the-art algorithms that can be adapted to triangulated mobile phone data for the context of urban computing and modeling applications.

Motion planning for urban driving using RRT

This paper provides a detailed analysis of the motion planning subsystem for the MIT DARPA Urban Challenge vehicle. The approach is based on the Rapidly-exploring Random Trees (RRT) algorithm. The purpose of this paper is to present the numerous extensions made to the standard RRT algorithm that enable the on-line use of RRT on robotic vehicles with complex, unstable dynamics and significant drift, while preserving safety in the face of uncertainty and limited sensing. The paper includes numerous simulation and race results that clearly demonstrate the effectiveness of the planning system.

Motion Planning in Complex Environments using Closed-loop Prediction

This paper describes the motion planning and control subsystems of Team MIT’s entry in the 2007 DARPA Grand Challenge. The novelty is in the use of closed-loop prediction in the framework of Rapidly-exploring Random Tree (RRT). Unlike the standard RRT, an input to the controller is sampled, followed by the forward simulation using the vehicle model and the controller to compute the predicted trajectory. This enables the planner to generate smooth trajectories much more efficiently, while the randomization allows the planner to explore cluttered environment. The controller consists of a Proportional-Integral speed controller and a nonlinear pure-pursuit steering controller, which are used both in execution and in the simulation-based prediction. The main advantages of the forward simulation are that it can easily incorporate any nonlinear control law and nonlinear vehicle dynamics, and the resulting trajectory is dynamically feasible. By using a stabilizing controller, it can handle vehicles with unstable dynamics. Several results obtained using MIT’s race vehicle demonstrate these features of the approach.