Yoichiro Endo

Adaptive Teams of Autonomous Aerial and Ground Robots for Situational Awareness

This is a preprint of an article accepted for publication in the Journal of Field Robotics, copyright 2007. Journal of Field Robotics 24(11), 991–1014 (2007)

Tactical Mobile Robot Mission Specification and Execution

Abstract Georgia Tech, as part of DARPAs Tactical Mobile Robotics (TMR) Program, is developing a widerange of mission specification capabilities for the urban warfighter. These include the development of arange of easily configurable mission-specific robot behaviors suitable for various battlefield and special forces scenarios; communications planning and configuration capabilities for small teams of robotsacting in a coordinated manner; interactive graphical visual programming environments for missionspecification; and real-time analysis tools and methods for mission execution verification. This paperprovides an overview of the approach being taken by the Georgia Tech/Honeywell team and presentsa range of preliminary results for a variety of missions in both simulation and on actual robots. 1. Introduction and Overview As part of DARPAs Tactical Mobile Robotics Program, Georgia Tech is providing certain basic capabilities suitable for robotic missions in urban settings: flexible reactive behaviors suitable for specific

Integrated Mission Specification and Task Allocation for Robot Teams - Design and Implementation

As the capabilities, range of missions, and the size of robot teams increase, the ability for a human operator to account for all the factors in these complex scenarios can become exceedingly difficult. Our previous research has studied the use of case-based reasoning (CBR) tools to assist a user in the generation of multi-robot missions. These tools, however, typically assume that the robots available for the mission are of the same type (i.e., homogeneous). We loosen this assumption through the integration of contract-net protocol (CNP) based task allocation coupled with a CBR-based mission specification wizard. Two alternative designs are explored for combining case-based mission specification and CNP-based team allocation as well as the tradeoffs that result from the selection of one of these approaches over the other.

Anticipatory robot control for a partially observable environment using episodic memories

This paper explains an episodic-memory-based approach for computing anticipatory robot behavior in a partially observable environment. Inspired by biological findings on the mammalian hippocampus, here, episodic memories retain a sequence of experienced observation, behavior, and reward. Incorporating multiple machine learning methods, this approach attempts to help reducing the computational burden of a partially observable Markov decision process (POMDP) problem. In particular, proposed computational reduction techniques include: (1) abstraction of the state space via temporal difference learning; (2) abstraction of the action space by utilizing motor schemata; (3) narrowing down the state space in terms of goals through instance-based learning; (4) elimination of the value-iteration by assuming a unidirectional-linear-chaining formation of the state space; (5) reduction of the state-estimate computation by exploiting the property of the Poisson distribution; and (6) trimming the history length by imposing a cap on the number of episodes that are computed. Claims (5) and (6) were empirically verified, and it was confirmed that the state estimation can be in fact computed in an 0(n) time (where n is the number of the states), more efficient than a conventional Kalman-filter based approach of 0(n2).

Implementing Tolman's schematic sowbug: behavior-based robotics in the 1930's

This paper re-introduces and evaluates the schematic sowbug proposed by Tolman (1939). The schematic sowbug is based on Tolmans purposive behaviorism, and it is believed to be the first prototype in history that actually implemented a behavior-based architecture suitable for robotics. The schematic sowbug navigates the environment based on two types of vectors, orientation and progression, that are computed from the values of sensors perceiving stimuli. Our experiments on both simulation and real robot proved the legitimacy of Tolmans assumptions, and the potential of applying the schematic sowbug model and principles within modern robotics is recognized.

Multi-robot User Interface Modeling

This paper investigates the problem of user interface design and evaluation for autonomous teams of heterogeneous mobile robots. We explore an operator modeling approach to multi-robot user interface evaluation. Specifically the authors generated GOMS models, a type of user model, to investigate potential interface problems and to guide the interface development process. Results indicate that our interface design changes improve the usability of multi-robot mission generation substantially. We conclude that modeling techniques such as GOMS can play an important role in robotic interface development. Moreover, this research indicates that these techniques can be performed in an inexpensive and timely manner, potentially reducing the need for costly and demanding usability studies.

Usability evaluation of an automated mission repair mechanism for mobile robot mission specification

This paper describes a usability study designed to assess ease of use, user satisfaction, and performance of a mobile robot mission specification system. The software under consideration, MissionLab, allows users to specify a robot mission as well as compile it, execute it, and control the robot in real-time. In this work, a new automated mission repair mechanism that aids users in correcting faulty missions was added to the system. This mechanism was compared to an older version in order to better inform the development process, and set a direction for future improvements in usability.

Landmark-based robust navigation for tactical UGV control in GPS-denied communication-degraded environments

Control of current tactical unmanned ground vehicles (UGVs) is typically accomplished through two alternative modes of operation, namely, low-level manual control using joysticks and high-level planning-based autonomous control. Each mode has its own merits as well as inherent mission-critical disadvantages. Low-level joystick control is vulnerable to communication delay and degradation, and high-level navigation often depends on uninterrupted GPS signals and/or energy-emissive (non-stealth) range sensors such as LIDAR for localization and mapping. To address these problems, we have developed a mid-level control technique where the operator semi-autonomously drives the robot relative to visible landmarks that are commonly recognizable by both humans and machines such as closed contours and structured lines. Our novel solution relies solely on optical and non-optical passive sensors and can be operated under GPS-denied, communication-degraded environments. To control the robot using these landmarks, we developed an interactive graphical user interface (GUI) that allows the operator to select landmarks in the robot’s view and direct the robot relative to one or more of the landmarks. The integrated UGV control system was evaluated based on its ability to robustly navigate through indoor environments. The system was successfully field tested with QinetiQ North America’s TALON UGV and Tactical Robot Controller (TRC), a ruggedized operator control unit (OCU). We found that the proposed system is indeed robust against communication delay and degradation, and provides the operator with steady and reliable control of the UGV in realistic tactical scenarios.