Fuzzy maps: A new tool for mobile robot perception and planning

Oriolo, Giuseppe; Ulivi, Giovanni; Vendittelli, Marilena

doi:10.1002/(sici)1097-4563(199703)14:3<179::aid-rob3>3.0.co;2-o

Cited by 109 publications

(50 citation statements)

References 20 publications

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“…The basic exploration process is wall-following combined with obstacle avoidance. Oriolo et al (25) developed a grid-based environment mapping process that uses fuzzy logic to update the grid cells. The mapping process runs on-line (24), and the local maps are built from the data obtained by the sensors and integrated into the environment map as the robot travels along the path defined by the A * algorithm to the goal.…”

Section: Approaches To Explorationmentioning

confidence: 99%

SLAM and Exploration using Differential Evolution and Fast Marching

Garrido¹,

Moreno²,

Blanco³

2011

Advances in Robot Navigation

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Section: Approaches To Explorationmentioning

confidence: 99%

SLAM and Exploration using Differential Evolution and Fast Marching

Garrido¹,

Moreno²,

Blanco³

2011

Advances in Robot Navigation

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“…Mapping with label uncertainty in mobile robotics has been addressed by several approaches from Bayesian techniques (Elfes, 1991) to fuzzy logic (Oriolo et al, 1997). Trivial approaches such as using latest labels only or running averages are simple and fast but lack confidence and accuracy and would cause an early fusion of the multi-class network outputs.…”

Section: Label Uncertainty: Cost From Histogrammentioning

confidence: 99%

A multirange architecture for collision‐free off‐road robot navigation

Sermanet

Hadsell

Scoffier

et al. 2008

Journal of Field Robotics

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We present a multi-layered mapping, planning, and command execution system developed and tested on the LAGR mobile robot. Key to robust performance under uncertainty is the combination of a short-range perception system operating at high frame rate and low resolution and a long-range, adaptive vision system operating at lower frame rate and higher resolution. The short-range module performs local planning and obstacle avoidance with fast reaction times, while the long-range module performs strategic visual planning. Probabilistic traversability labels provided by the perception modules are combined and accumulated into a robot-centered hyperbolic-polar map with a 200 meter effective range. Instead of using a dynamical model of the robot for short-range planning, the system uses a large lookup table of physically-possible trajectory segments recorded on the robot in a wide variety of driving conditions. Localization is performed using a combination of GPS, wheel odometry, IMU, and a high-speed, low-complexity rotational visual odometry module. The end to end system was developed and tested on the LAGR mobile robot, and was verified in independent government tests.

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“…Methods based on Bayesian inference [26,27,85], Dempster-Shafer evidence theory [100], Fuzzy set theory [98,99], and various ad hoc rules, particularly in the early literature, exist to update estimates of the state of each cell in the grid as new measurements arrive.' The key concession common to all these methods is the inability to represent dependencies between the states of multiple cells.…”

Section: Background: Occupancy Gridsmentioning

confidence: 99%

Stochastic mapping for chemical plume source localization with application to autonomous hydrothermal vent discovery

Jakuba¹

2007

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This thesis presents a stochastic mapping framework for autonomous robotic chemical plume source localization in environments with multiple sources. Potential applications for robotic chemical plume source localization include pollution and environmental monitoring, chemical plant safety, search and rescue, anti-terrorism, narcotics control, explosive ordinance removal, and hydrothermal vent prospecting. Turbulent flows make the spatial relationship between the detectable manifestation of a chemical plume source, the plume itself, and the location of its source inherently uncertain. Search domains with multiple sources compound this uncertainty because the number of sources as well as their locations is unknown a priori.Our framework for stochastic mapping is an adaptation of occupancy grid mapping where the binary state of map nodes is redefined to denote either the presence (occupancy) or absence of an active plume source. A key characteristic of the chemical plume source localization problem is that only a few sources are expected in the search domain. The occupancy grid framework allows for both plume detections and non-detections to inform the estimated state of grid nodes in the map, thereby explicitly representing explored but empty portions of the domain as well as probable source locations. However, sparsity in the expected number of occupied grid nodes strongly violates a critical conditional independence assumption required by the standard Bayesian recursive map update rule. While that assumption makes for a computationally attractive algorithm, in our application it results in occupancy grid maps that are grossly inconsistent with the assumption of a small number of occupied cells. To overcome this limitation, several alternative occupancy grid update algorithms are presented, including an exact solution that is computationally tractable for small numbers of detections and an approximate recursive algorithm with improved performance relative to the standard algorithm but equivalent computational cost.Application to hydrothermal plume data collected by the autonomous underwater vehicle ABE during vent prospecting operations in both the Pacific and Atlantic oceans verifies the utility of the approach. The resulting maps enable nested surveys for homing-in on seafloor vent sites to be carried out autonomously. This eliminates inter-dive processing, recharging of batteries, and time spent deploying and recovering the vehicle that would otherwise be necessary with survey design directed by human operators.

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Fuzzy maps: A new tool for mobile robot perception and planning

Cited by 109 publications

References 20 publications

SLAM and Exploration using Differential Evolution and Fast Marching

SLAM and Exploration using Differential Evolution and Fast Marching

A multirange architecture for collision‐free off‐road robot navigation

Stochastic mapping for chemical plume source localization with application to autonomous hydrothermal vent discovery

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