SLAM-Based Spatial Memory for Behavior-Based Robots

  title={SLAM-Based Spatial Memory for Behavior-Based Robots},
  author={Shu Jiang and Ronald C. Arkin},
Formal Performance Guarantees for Behavior-Based Localization Missions
Two approaches to modeling probabilistic localization for verification are presented: a high-level approach, and a sample-based approach which allows run-time code to be embedded in verification.
The complete integration of MissionLab and CARMEN
The proposed platform solves the proposed goal, that is, it simplifies the programmer’s job when developing control software for robot teams, and it further facilitates multi-robot deployment task in mission-critical situations.
Establishing A-Priori Performance Guarantees for Robot Missions that Include Localization Software
The authors have applied this second approach to automatically derive performance guarantees for behavior-based, multi-robot critical mission software using an innovative approach to formal verification for robotic software.
Performance verification for robot missions in uncertain environments
Establishing A-Priori Performance Guarantees for Robot Missions that Include Localization Software
Several approaches to incorporating pre-existing software into the authors’ probabilistic verification framework are presented, and one used to include Monte-Carlo based localization software is presented.
Sequential Localizing and Mapping: A Navigation Strategy via Enhanced Subsumption Architecture
A navigation strategy exclusively designed for social robots with limited sensors for applications in homes that integrates a reactive design based on subsumption architecture and a knowledge system with learning capabilities.
Toward Initiative Decision-Making for Distributed Human-Robot Teams
The brief history of human-robot teams can be traced through the changing perspective of a robot's role within the team, which has evolved from being treated as a tool to a recent shift toward the
Real-Time Simultaneous Localization and Mapping for Low-Power Wide-Area Communication
The experimental results show that the proposed methods can be used to optimize SLAM in real-time implementation, especially for remote monitoring through wireless communication.
Multiarquitectura distribuida para el desarrollo de misiones multi-robot
La Robotica Autonoma es un campo en rapido crecimiento relacionado con un extenso grupo de lineas de investigacion, como localizacion, tracking, mapping, planificacion de caminos, algoritmos IA,


Hybrid robot control and SLAM for persistent navigation and mapping
Integrating behavioral, perceptual, and world knowledge in reactive navigation
  • R. Arkin
  • Psychology
    Robotics Auton. Syst.
  • 1990
Exploration with active loop-closing for FastSLAM
  • C. Stachniss, D. Hähnel, W. Burgard
  • Computer Science
    2004 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS) (IEEE Cat. No.04CH37566)
  • 2004
A novel and integrated approach that combines autonomous exploration with simultaneous localization and mapping that uses a grid-based version of the FastSLAM algorithm and at each point in time considers actions to actively close loops during exploration.
Active SLAM using Model Predictive Control and Attractor based Exploration
A novel technique is introduced that utilises an attractor combined with local planning strategies such as model predictive control (a.k.a. receding horizon) to solve the active SLAM problem as an optimal trajectory planning problem.
Integration of representation into goal-driven behavior-based robots
  • M. Matarić
  • Computer Science
    IEEE Trans. Robotics Autom.
  • 1992
An architecture that integrates a map representation into a reactive, subsumption-based mobile robot is described, which removes the distinction between the control program and the map.
Perception-driven navigation: Active visual SLAM for robotic area coverage
  • Ayoung Kim, R. Eustice
  • Computer Science
    2013 IEEE International Conference on Robotics and Automation
  • 2013
This paper reports on an integrated navigation algorithm for the visual simultaneous localization and mapping (SLAM) robotic area coverage problem. In the robotic area coverage problem, the goal is
Information based adaptive robotic exploration
This paper addresses the problem of maximizing the accuracy of the map building process during exploration by adaptively selecting control actions that maximize localisation accuracy by adaptingive sensing.
On the comparison of uncertainty criteria for active SLAM
It is shown that contrary to what has been previously reported, the D-optimality criterion is indeed capable of giving fruitful information as a metric for the uncertainty of a robot performing SLAM.
Avoiding the past: a simple but effective strategy for reactive navigation
  • T. Balch, R. Arkin
  • Computer Science
    [1993] Proceedings IEEE International Conference on Robotics and Automation
  • 1993
It is shown that the addition of a local spatial memory that allows a robot to avoid areas that have already been visited offers a solution to the box canyon and other navigational problems.
Global A-Optimal Robot Exploration in SLAM
  • Robert Sim, N. Roy
  • Computer Science
    Proceedings of the 2005 IEEE International Conference on Robotics and Automation
  • 2005
It is shown that optimizing the a-optimal information measure results in a more accurate map than existing approaches, using a greedy, closed-loop strategy, and that by restricting the planning to an appropriate policy class, one can tractably find non-greedy, global planning trajectories that produce more accurate maps.