Safe Navigation for Indoor Mobile Robots. Part II: Exploration,                Self-Localization and Map Building

This article presented a cooperative mapping technique using a novel edge gradient algorithm for multiple mobile robots. The proposed edge gradient algorithm can be divided into four behaviors such as adjusting the movement direction, evaluating the safety of motion behavior, following behavior, and obstacle information exchange, which can effectively prevent multiple mobile robots falling into concave obstacle areas. Meanwhile, a visual field factor is constructed based on biological principles so that the mobile robots can have a larger field of view when moving away from obstacles. Also, the visual field factor will be narrowed due to the obstruction of the obstacle when approaching an obstacle and the obtained map-building data are more accurate. Finally, three sets of simulation and experimental results demonstrate the performance superiority of the presented algorithm.

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Probabilistic and Count Methods in Map Building for Autonomous Mobile Robots

Advances in Robot Learning - Lecture Notes in Computer Science ◽

10.1007/3-540-40044-3_8 ◽

2000 ◽

pp. 120-137 ◽

Cited By ~ 2

Author(s):

Miguel Rodríguez ◽

José Correa ◽

Roberto Iglesias ◽

Carlos V. Regueiro ◽

Senén Barro

Keyword(s):

Mobile Robots ◽

Autonomous Mobile Robots ◽

Map Building

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POSSIBILISTIC SONAR DATA MODELING FOR MOBILE ROBOTS

International Journal of Uncertainty Fuzziness and Knowledge-Based Systems ◽

10.1142/s0218488599000118 ◽

1999 ◽

Vol 07 (02) ◽

pp. 173-198 ◽

Cited By ~ 2

Author(s):

K. DEMIRLI ◽

M. MOLHIM ◽

A. BULGAK

Keyword(s):

Probability Theory ◽

Mobile Robots ◽

Data Modeling ◽

Possibility Theory ◽

Sensor Design ◽

Map Building ◽

Sonar Sensors ◽

New Models ◽

Possibility Distributions ◽

Target Characteristics

Sonar sensors are widely used in mobile robots applications such as navigation, map building, and localization. The performance of these sensors is affected by the environmental phenomena, sensor design, and target characteristics. Therefore, the readings obtained from these sensors are uncertain. This uncertainity is often modeled by using Probability Theory. However, the probablistic approach is valid when the available knowledge is precise which is not the case in sonar readings. In this paper, the behavior of sonar readings reflected from walls and corners are studied, then new models of angular uncertainty and radial imprecision for sonar readings obtained from corners and walls are proposed. These models are represented by using Possibility Theory, mainly possibility distributions.

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Autonomous and Safe Navigation of Mobile Robots in Vineyard with Smooth Collision Avoidance

Agriculture ◽

10.3390/agriculture11100954 ◽

2021 ◽

Vol 11 (10) ◽

pp. 954

Author(s):

Abhijeet Ravankar ◽

Ankit A. Ravankar ◽

Arpit Rawankar ◽

Yohei Hoshino

Keyword(s):

Mobile Robots ◽

Real Time ◽

Collision Avoidance ◽

Autonomous Navigation ◽

Autonomous Robots ◽

Robot Navigation ◽

Indoor Environments ◽

Safe Distance ◽

Field Robots ◽

Safe Navigation

In recent years, autonomous robots have extensively been used to automate several vineyard tasks. Autonomous navigation is an indispensable component of such field robots. Autonomous and safe navigation has been well studied in indoor environments and many algorithms have been proposed. However, unlike structured indoor environments, vineyards pose special challenges for robot navigation. Particularly, safe robot navigation is crucial to avoid damaging the grapes. In this regard, we propose an algorithm that enables autonomous and safe robot navigation in vineyards. The proposed algorithm relies on data from a Lidar sensor and does not require a GPS. In addition, the proposed algorithm can avoid dynamic obstacles in the vineyard while smoothing the robot’s trajectories. The curvature of the trajectories can be controlled, keeping a safe distance from both the crop and the dynamic obstacles. We have tested the algorithm in both a simulation and with robots in an actual vineyard. The results show that the robot can safely navigate the lanes of the vineyard and smoothly avoid dynamic obstacles such as moving people without abruptly stopping or executing sharp turns. The algorithm performs in real-time and can easily be integrated into robots deployed in vineyards.

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