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Open AccessProceedings ArticleDOI

Poisson-driven dirt maps for efficient robot cleaning

TLDR
A novel approach that uses cell-wise Poisson processes on a regular grid to estimate the distribution of dirt in the environment allows for an effective estimation of the dynamics of the generation of dirt and for making predictions about the absolute dirt values.
Abstract
Being able to estimate the dirt distribution in an environment makes it possible to compute efficient paths for robotic cleaners. In this paper, we present a novel approach for modeling and estimating the dynamics of the generation of dirt in an environment. Our model uses cell-wise Poisson processes on a regular grid to estimate the distribution of dirt in the environment. It allows for an effective estimation of the dynamics of the generation of dirt and for making predictions about the absolute dirt values. We propose two efficient cleaning policies that are based on the estimated dirt distributions and can easily be adapted to different needs of potential users. Through extensive experiments carried out with a modified iRobot Roomba vacuum cleaning robot and in simulation we demonstrate the effectiveness of our approach.

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Citations
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Journal ArticleDOI

Planning robot manipulation to clean planar surfaces

TL;DR: This paper proposes to define the problem using explicitly uncertain actions, and then plan the most effective sequence of actions in terms of time, and demonstrates that planning is more advantageous than simple reactive strategies for accomplishing complex tasks, while still providing a similar performance for easy tasks.
Journal ArticleDOI

Table Cleaning Task by Human Support Robot Using Deep Learning Technique.

TL;DR: This work presents a table cleaning and inspection method using a Human Support Robot (HSR) which can operate in a typical food court setting by implementing a deep learning technique and planner framework and shows that the food litter detection module achieves an average of 96% detection accuracy.
Proceedings ArticleDOI

A probabilistic approach to high-confidence cleaning guarantees for low-cost cleaning robots

TL;DR: This paper introduces a novel probabilistic model for jointly estimating the trajectory of the robot and the current dirt distribution in the environment and adapts the future cleaning path during operation such that the robot re-visits areas in which high dirt levels are still likely.
Proceedings ArticleDOI

A Floor Cleaning Robot for Domestic Environments

TL;DR: A mobile robot with sweeping, vacuum suction and wiping capacity for effective cleaning of a domestic floor and can avoid collisions with dynamic obstacles through the fusion of information from two sensors: a sharp infra red sensor and an ultrasonic sonar sensor.
Proceedings ArticleDOI

Workflow Variability for Autonomic IoT Systems

TL;DR: The semantics of the DX-MAN service model is extended so as to combine variability with behaviour, allowing the design of composite services that define an infinite number of workflow variants which can be chosen at runtime without any reconfiguration mechanism.
References
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Book

Probabilistic Robotics

TL;DR: This research presents a novel approach to planning and navigation algorithms that exploit statistics gleaned from uncertain, imperfect real-world environments to guide robots toward their goals and around obstacles.
BookDOI

Springer Handbook of Robotics

TL;DR: The contents have been restructured to achieve four main objectives: the enlargement of foundational topics for robotics, the enlightenment of design of various types of robotic systems, the extension of the treatment on robots moving in the environment, and the enrichment of advanced robotics applications.
Proceedings ArticleDOI

High resolution maps from wide angle sonar

TL;DR: The use of multiple wide-angle sonar range measurements to map the surroundings of an autonomous mobile robot deals effectively with clutter, and can be used for motion planning and for extended landmark recognition.
Journal ArticleDOI

An effective implementation of the Lin–Kernighan traveling salesman heuristic

TL;DR: An implementation of the Lin–Kernighan heuristic, one of the most successful methods for generating optimal or near-optimal solutions for the symmetric traveling salesman problem (TSP), is described.
Journal ArticleDOI

Coverage for robotics – A survey of recent results

TL;DR: This paper surveys recent results in coverage path planning, a new path planning approach that determines a path for a robot to pass over all points in its free space, and organizes the coverage algorithms into heuristic, approximate, partial-approximate and exact cellular decompositions.
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How do I map my MI robot vacuum?

Through extensive experiments carried out with a modified iRobot Roomba vacuum cleaning robot and in simulation we demonstrate the effectiveness of our approach.