(Georgia Institute of Technology, 2007)
Arkin, Ronald C.; Endo, Yoichiro; Chaimowicz, Luiz; Cowley, Anthony; Grocholsky, Ben; Hsieh, Mong-ying A.; Jung, Boyoon; Keller, James F.; Kumar, Vijay; MacKenzie, Douglas Christopher; Sukhatme, Gaurav S.; Taylor, Camillo J.; Wolf, Denis F.
In this paper, we report on the integration challenges of the various component
technologies developed towards the establishment of a framework for deploying
an adaptive system of heterogeneous robots for urban surveillance. In our
integrated experiment and demonstration, aerial robots generate maps that are
used to design navigation controllers and plan missions for the team. A team of
ground robots constructs a radio signal strength map that is used as an aid for
planning missions. Multiple robots establish a mobile, ad-hoc communication
network that is aware of the radio signal strength between nodes and can adapt
to changing conditions to maintain connectivity. Finally, the team of aerial
and ground robots is able to monitor a small village, and search for and localize
human targets by the color of the uniform, while ensuring that the information from the team is available to a remotely located human operator. The key
component technologies and contributions include (a) mission speci cation
and planning software; (b) exploration and mapping of radio signal strengths
in an urban environment; (c) programming abstractions and composition of
controllers for multi-robot deployment; (d) cooperative control strategies for
search, identi cation, and localization of targets; and (e) three-dimensional
mapping in an urban setting.
(Georgia Institute of Technology, 2003)
Arkin, Ronald C.; Endo, Yoichiro; Lee, Brian; MacKenzie, Douglas Christopher; Martinson, Eric
This article describes three different methods for introducing machine learning into a hybrid deliberative/reactive architecture for multirobot systems: learning momentum, Q-learning, and CBR wizards. A range of simulation experiments and results are reported using the Georgia Tech MissionLab mission specification system.
(Georgia Institute of Technology, 2002)
Arkin, Ronald C.; Endo, Yoichiro; MacKenzie, Douglas Christopher
MissionLab is a mission specification system that
implements a hybrid deliberative and reactive control
architecture for autonomous mobile robots. The user creates
and executes the robot mission plans through its graphical
user interface. As robot deployments become more common in
highly stressful situations, such as in dealing with explosives or
biohazards, the usability of their mission specification system
becomes critical. To address this need, a mission-planning
“wizard” has been recently integrated into MissionLab. By
retrieving and adapting past successful mission plans stored in
its database, this new feature is designed to simplify the user’s
planning process. The latest formal usability experiments,
reported in this paper, testing for usability improvements in
terms of speed of the mission planning process, accuracy of the
produced mission plans, and ease of use is conducted. This
paper introduces the mission-planning wizard, describes the
usability experiments (including design), and discusses the
results in detail.