Mario Valenti

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.

Hover, Transition, and Level Flight Control Design for a Single-Propeller Indoor Airplane

This paper presents vehicle models and test flight results for an autonomous fixed-wing aircraft with the capability to take off, hover, transition to and from level-flight, and perch on a vertical landing platform. These maneuvers are all demonstrated in the highly space constrained environment of the Real-time indoor Autonomous Vehicle test ENvironment (RAVEN) at MIT. RAVEN promotes the rapid prototyping of UAV planning and control technologies by allowing the use of unmodified commercially available model aircraft for autonomous flight. Experimental results of several hover tests, transition maneuvers, and perch landings are presented. By enabling a fixed-wing UAV to achieve these feats, we demonstrate that the desirable speed and range performance of an autonomous fixed-wing aircraft in level flight can be complimented by hover capabilities that are typically limited to rotary-wing vehicles. This combination has the potential to significantly ease support and maintenance of operational autonomous vehicles.

Estimation and Control of a Quadrotor Vehicle Using Monocular Vision and Moire Patterns

We present the vision-based estimation and control of a quadrotor vehicle using a single camera relative to a novel target that incorporates the use of moire patterns. The objective is to acquire the six degree of freedom estimation that is essential for the operation of vehicles in close proximity to other craft and landing platforms. A target contains markers to determine its relative orientation and locate two sets of orthogonal moire patterns at two difierent frequencies. A camera is mounted on the vehicle with the target in the fleld of view. An algorithm processes the images, extracting the attitude and position information of the camera relative to the target utilizing geometry and four single-point discrete Fourier transforms on the moire patterns. The position and yaw estimations with accompanying control techniques have been implemented on a remote-controlled quadrotor. The ∞ight tests conducted prove the systems feasibility as an option for precise relative navigation for indoor and outdoor operations.

UAV Task Assignment

Unmanned aerial vehicles (UAVs) are becoming vital warfare and homeland security platforms because they have the potential to significantly reduce cost and risk to human life while amplifying warfighter and first-responder capabilities. This article builds on the very active area of planning and control for autonomous multiagent systems. This work represents a step toward enabling robust decision making for distributed autonomous UAVs by improving the teams operational reliability and capabilities through better system self-awareness and adaptive mission planning. The health-aware task assignment algorithm developed in this article was demonstrated to be effective both in simulation and flight experiments.

Cooperative Vision Based Estimation and Tracking Using Multiple UAVs

Unmanned aerial vehicles (UAVs) are excellent platforms for detecting and tracking objects of interest on or near the ground due to their vantage point and freedom of movement. This paper presents a cooperative vision-based estimation and tracking system that can be used in such situations. The method is shown to give better results than could be achieved with a single UAV, while being robust to failures. In addition, this method can be used to detect, estimate and track the location and velocity of objects in three dimensions. This real-time, vision-based estimation and tracking algorithm is computationally efficient and can be naturally distributed among multiple UAVs. This chapter includes the derivation of this algorithm and presents flight results from several real-time estimation and tracking experiments conducted on MIT’s Real-time indoor Autonomous Vehicle test ENvironment (RAVEN).

Embedding Health Management into Mission Tasking for UAV Teams

Coordinated multi-vehicle autonomous systems can provide incredible functionality, but off-nominal conditions and degraded system components can render this capability ineffective. This paper presents techniques to improve mission-level functional reliability through better system self-awareness and adaptive mission planning. In particular, we extend the traditional definition of health management, which has historically referred to the process of actively monitoring and managing vehicle sub-systems (e.g., avionics) in the event of component failures, to the context of multiple vehicle operations and autonomous multi-agent teams. In this case, health management information about each mission system component is used to improve the mission systems self-awareness and adapt vehicle, guidance, task and mission plans. This paper presents the theoretical foundations of our approach and recent experimental results on a new UAV testbed.

Mission Health Management for 24/7 Persistent Surveillance Operations

This paper presents the development and implementation of techniques used to manage autonomous unmanned aerial vehicles (UAVs) performing 24/7 persistent surveillance operations. Using an indoor flight testbed, flight test results are provided to demonstrate the complex issues encountered by operators and mission managers when executing an extended persistent surveillance operation in realtime. This paper presents mission health monitors aimed at identifying and improving mission system performance to avoid down time, increase mission system eciency and reduce operator loading. This paper discusses the infrastructure needed to execute an autonomous persistent surveillance operation and presents flight test results from one of our recent automated UAV recharging experiments. Using the RAVEN at MIT, we present flight test results from a 24 hr, fully-autonomous air vehicle flight-recharge test and an autonomous, multi-vehicle extended mission test using small, electric-powered air vehicles.

The MIT Indoor Multi-Vehicle Flight Testbed

This paper and video present the components and flight tests of an indoor, multi-vehicle testbed that was developed to study long duration UAV missions in a controlled environment. This testbed is designed to use real hardware to examine research questions related to single- and multi-vehicle health management, such as vehicle failures, refueling, and maintenance. The testbed has both aerial and ground vehicles that operate autonomously in a large, indoor flight test area 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 systems command and control architecture. The video presents flight test results from single- and multi-vehicle experiments over the past year.

Implementation and Flight Test Results of MILP-based UAV Guidance

This paper discusses the implementation of a guidance system based on mixed integer linear programming (MILP) on a modified, autonomous T-33 aircraft equipped with Boeings UCAV avionics package. A receding horizon MILP formulation is presented for safe, real-time trajectory generation in a partially-known, cluttered environment. Safety at all times is guaranteed by constraining the intermediate trajectories to terminate in a loiter pattern that does not intersect with any no-fly zones and can always be used as a safe backup plan. Details about the real-time software implementation using CPLEX and Boeings OCP platform are given. A test scenario developed for the DARPA-sponsored software enabled control capstone demonstration is outlined, and simulation and flight test results are presented

Implementation of a Manned Vehicle-UAV Mission System

We discuss the development, integration, simulation, and flight test of a manned vehicle UAV mission system in a partially-known environment. The full control system allows a manned aircraft to issue mission level commands to an autonomous aircraft in realtime. This system includes a Natural Language (NL) Interface to allow the manned and unmanned vehicle to communicate in languages understood by both agents. The unmanned vehicle implements a dynamic mission plan determined by a weapons systems officer (WSO) on the manned vehicle. The unmanned vehicle uses a Mixed-Integer Linear Programming (MILP) based trajectory planner to direct the vehicle according to the mission plan. We provide simulation and test results for this system using an integrated computer-based simulation and the vehicle hardware testing in late June 2004. The activities described in this paper are part of the Capstone demonstration of the DARPA-sponsored Software Enabled Control effort.