Yoichiro Endo

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Georgia Tech, as part of DARPA's Tactical Mobile Robotics (TMR) Program, is developing a wide range of mission speci cation capabilities for the urban war ghter. These include the development of a range of easily con gurable mission-speci c robot behaviors suitable for various battle eld and special forces scenarios; communications planning and con guration(More)
This paper presents a method for a mobile robot to construct and localize relative to a “cognitive map”, where the cognitive map is assumed to be a representational structure that encodes both spatial and behavioral information. The localization is performed by applying a generic Bayes filter. The cognitive map was implemented within a behavior-based(More)
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, <i>MissionLab</i>, 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(More)
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(More)
This paper reintroduces and evaluates the schematic sowbug proposed by Edward C. Tolman, psychologist, in 1939. The schematic sowbug is based on Tolman's purposive behaviorism, and it is believed to be the rst prototype in history that actually implemented a behavior-based architecture suitable for robotics. The schematic sowbug navigates the environment(More)
In 1999, Georgia Tech conducted two field experiments to determine the performance of its mission specification system. The experiments were developed for the DARPA Tactical Mobile Robotics (TMR) Program and were conducted at Fort Sam Houston, Texas. The goal of the TMR Program is to develop robotic tools that can perform useful tasks on future military(More)
  • Yoichiro Endo
  • 2008 IEEE International Conference on Robotics…
  • 2008
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(More)
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,(More)