Indoor navigation with uncertainty using sensor-based motions

Khatib, Maher; Bouilly, B.; Siméon, Thierry; Chatila, Raja

doi:10.1109/robot.1997.606804

Cited by 22 publications

(9 citation statements)

References 12 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Some approaches make use of randomized planning techniques, e.g. [1,2], other approaches use reactive schemes, for instance [3][4][5], and/or make use of a navigation function to follow a path like [6] or plan directly in the velocity space like [7][8][9][10]. Yet other approaches employ a search, some in physical space, some in configuration space.…”

Section: Related Workmentioning

confidence: 99%

Robust Collision Avoidance in Unknown Domestic Environments

Jacobs

Ferrein

Schiffer

et al. 2010

RoboCup 2009: Robot Soccer World Cup XIII

View full text Add to dashboard Cite

Abstract. Service robots operating in domestic indoor environments must be endowed with a safe collision avoidance and navigation method that is reactive enough to avoid contacts with the furniture of the apartment and humans that suddenly appear in front of the robot. Moreover, the method should be local, i.e. should not need a predefined map of the environment. In this paper we describe a navigation and collision avoidance method which is all of that: safe, fast, and local. Based on a geometric grid representation which is derived from the laser range finder of our domestic robot, a path to the next target point is found by employing A * . The obstacles which are used in the local map of the robot are extended depending on the speed the robot travels at. We compute a triangular area in front of the robot which is guaranteed to be free of obstacles. This triangle serves as the space of feasible solutions when searching for the next drive commands. With this triangle, we are able to decouple the path search from the search for drive commands, which tremendously decreases the complexity. We used the proposed method for several years in RoboCup@Home where it was a key factor to our success in the competitions.

show abstract

Section: Related Workmentioning

confidence: 99%

Robust Collision Avoidance in Unknown Domestic Environments

Jacobs

Ferrein

Schiffer

et al. 2010

RoboCup 2009: Robot Soccer World Cup XIII

View full text Add to dashboard Cite

show abstract

“…In chronological order, the main family of approaches in the literature have been pre-image back-chaining [17], [13], [18]; sensory uncertainty fields [8], [19], [20], [21]; sensorbased planning [22], [4], [23]; the Information Space approach [10]; the set-membership approach [24], [5]; stochastic dynamic programming [15], [25]; and classical search strategies in an extended space [12], [1], [9], [26], [27]. The latter is the family to which this paper belongs.…”

Section: Minimizing the Expectation Of A Functional Of Q(t)mentioning

confidence: 99%

A Bayesian framework for optimal motion planning with uncertainty

Censi

Calisi

Luca

et al. 2008

2008 IEEE International Conference on Robotics and Automation

View full text Add to dashboard Cite

Abstract-Modeling robot motion planning with uncertainty in a Bayesian framework leads to a computationally intractable stochastic control problem. We seek hypotheses that can justify a separate implementation of control, localization and planning. In the end, we reduce the stochastic control problem to pathplanning in the extended space of poses × covariances; the transitions between states are modeled through the use of the Fisher information matrix. In this framework, we consider two problems: minimizing the execution time, and minimizing the final covariance, with an upper bound on the execution time. Two correct and complete algorithms are presented. The first is the direct extension of classical graph-search algorithms in the extended space. The second one is a back-projection algorithm: uncertainty constraints are propagated backward from the goal towards the start state.

show abstract

“…Thus the moment-by-moment computer control of arm trajectories is the appropriate paradigm for macroscale robots, but not for nanoscale robots. For nanoscale robots, the appropriate manipulator control paradigm is often trajectory trial and error, also known as sensor-based motion control [41].…”

Section: Proposed Approachmentioning

confidence: 99%

Nanorobotics Control Design: A Practical Approach Tutorial

Cavalcanti

Freitas

Kretly

2004

Volume 2: 28th Biennial Mechanisms and Robotics Conference, Parts a and B

View full text Add to dashboard Cite

The authors present a new approach using genetic algorithms, neural networks and nanorobotics concepts applied to the problem of control design for nanoassembly automation and its application in medicine. As a practical approach to validate the proposed design, we have elaborated and simulated a virtual environment focused on control automation for nanorobotics teams that exhibit collective behavior. This collective behavior is a suitable way to perform a large range of tasks and positional assembly manipulation in a complex 3D workspace. We emphasize the application of such techniques as a feasible approach for the investigation of nanorobotics system design in nanomedicine. Theoretical and practical analyses of control modelling is one important aspect that will enable rapid development in the emerging field of nanotechnology.

show abstract

Indoor navigation with uncertainty using sensor-based motions

Cited by 22 publications

References 12 publications

Robust Collision Avoidance in Unknown Domestic Environments

Robust Collision Avoidance in Unknown Domestic Environments

A Bayesian framework for optimal motion planning with uncertainty

Nanorobotics Control Design: A Practical Approach Tutorial

Contact Info

Product

Resources

About