To investigate and develop unmanned vehicle systems technologies for autonomous multiagent mission platforms, we are using an indoor multivehicle testbed called real-time indoor autonomous vehicle test environment (RAVEN) to study long-duration multivehicle missions in a controlled environment. Normally, demonstrations of multivehicle coordination and control technologies require that multiple human operators simultaneously manage flight hardware, navigation, control, and vehicle tasking. However, RAVEN simplifies all of these issues to allow researchers to focus, if desired, on the algorithms associated with high-level tasks. Alternatively, RAVEN provides a facility for testing low-level control algorithms on both fixed- and rotary-wing aerial platforms. RAVEN is also being used to analyze and implement techniques for embedding the fleet and vehicle health state (for instance, vehicle failures, refueling, and maintenance) into UAV mission planning. These characteristics facilitate the rapid prototyping of new vehicle configurations and algorithms without requiring a redesign of the vehicle hardware. This article describes the main components and architecture of RAVEN and presents recent flight test results illustrating the applications discussed above.

Real-time indoor autonomous vehicle test environment

The sea robot-assisted inspection (Sea-RAI) marsupial robot team is the first known manportable unmanned surface vehicle (USV) that hosts an unmanned aerial vehicle (UAV). The Sea-RAI is designed for inspecting littoral environments for military, environmental, and disaster-response applications. The project also provides a platform for exploring the four roles in a marsupial team: courier, messenger, manager, and coach. The cooperation between the vehicles extends their capabilities beyond the capabilities of a single vehicle. This article describes the robot team, details the design and construction of low-cost USVs, and describes the demonstration of the integrated system and the four key capabilities, such as seaworthiness, data display, marsupialism, and mission logging.

Sea Robot-Assisted Inspection

This thesis presents the development and implementation of approximate dynamic programming methods used to manage multi-agent systems. The purpose of this thesis is to develop an architectural framework and theoretical methods that enable an autonomous mission system to manage real-time multi-agent operations. To meet this goal, we begin by discussing aspects of the real-time multi-agent mission problem. Next, we formulate this problem as a Markov Decision Process (MDP) and present a system architecture designed to improve mission-level functional reliability through system self-awareness and adaptive mission planning. Since most multi-agent mission problems are computationally difficult to solve in real-time, approximation techniques are needed to find policies for these large-scale problems. Thus, we have developed theoretical methods used to find feasible solutions to large-scale optimization problems. More specifically, we investigate methods designed to automatically generate an approximation to the cost-to-go function using basis functions for a given MDP. Next, these techniques are used by an autonomous mission system to manage multi-agent mission scenarios. Simulation results using these methods are provided for a large-scale mission problem. In addition, this thesis presents the implementation of techniques used to manage autonomous unmanned aerial vehicles (UAVs) performing persistent surveillance operations. We present an indoor multi-vehicle testbed called RAVEN (Real-time indoor Autonomous Vehicle test ENvironment) that was developed to study long-duration missions in a controlled environment. The RAVEN's design allows researchers to focus on high-level tasks by autonomously managing the platform's realistic air and ground vehicles during multi-vehicle operations, thus promoting the rapid prototyping of UAV technologies by flight testing new vehicle configurations and algorithms without redesigning vehicle hardware. Finally, using the RAVEN, we present flight test results from autonomous, extended mission tests using the technologies developed in this thesis. Flight 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 are provided. (Copies available exclusively from MIT Libraries, Rm. 14-0551, Cambridge, MA 02139-4307. Ph. 617-253-5668; Fax 617-253-1690.)

Approximate dynamic programming with applications in multi-agent systems

The problem of autonomously exploring an environment with a team of robots received considerable attention in the past. However, there are relatively few approaches to coordinate teams of robots that are able to deploy and retrieve other robots. Efficiently coordinating the exploration with such marsupial robots requires advanced planning mechanisms that are able to consider symbolic deployment and retrieval actions. In this paper, we propose a novel approach for coordinating the exploration with marsupial robot teams. Our method integrates a temporal symbolic planner that explicitly considers deployment and retrieval actions with a traditional cost-based assignment procedure. Our approach has been implemented and evaluated in several simulated environments and with varying team sizes. The results demonstrate that our proposed method is able to coordinate marsupial teams of robots to efficiently explore unknown environments.

/pdf/coordinated-exploration-with-marsupial-teams-of-robots-using-27lbm7qwv8.pdf

Coordinated exploration with marsupial teams of robots using temporal symbolic planning

In this paper, a tetrahedral mobile robot with central axis for transformation to the flat vehicle is presented. The throwable robot with the function of going into narrow spaces when its in the flat-vehicle mode is explained in detail. A prototype has been developed to illustrate the concept. Motion experiments confirm the novel properties of this mechanism: mode changing function and omnidirectional motion. Basic Motion experiments, with a test vehicle are also presented.

/pdf/throwable-tetrahedral-robot-with-transformation-capability-1w148jbeg1.pdf

Throwable tetrahedral robot with transformation capability

The Scouts, developed at the University of Minnesota, are miniature robots designed mainly for reconnaissance and surveillance tasks. The Scout's small size and multiple mobility and sensing modes allow it to efficiently navigate and carry out certain missions in indoor environments and on relatively smooth surfaces. However, like many other small-sized robots, its miniature size and limited battery life become a bottleneck when it comes to cover long distances, particularly on rough, outdoor surfaces. In order to address this issue, we present a method to carry the Scouts to and from their main mission locations by adding a larger robot, a Pioneer 2-AT, to the team. The Pioneer carries a team of Scouts in a color marked box that it holds with its grippers. The Scouts jump out of the box, carry out their mission, and autonomously find the box and jump back in.

A method for transporting a team of miniature robots

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A method for transporting a team of miniature robots