Graph SLAM based mapping for AGV localization in large-scale warehouses

Beinschob, Patric; Reinke, Christoph

doi:10.1109/iccp.2015.7312637

Cited by 34 publications

(20 citation statements)

References 13 publications

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“…Another central issue in AGV control is the motion planning; several researchers worldwide address this issue [ 4 , 5 ]. Several research groups look into navigation tasks and self-orientation, such as simultaneous localization and mapping (SLAM) [ 6 , 7 , 8 , 9 ]. Yet another important topic is the coordination of multi-AGV systems; in this field, the so-called integrated Markov neural reliability computation method [ 10 ], cloud solutions [ 11 ], colored Petri nets (CPNs) [ 12 ], conflict avoidance strategies [ 13 ], re-scheduling algorithms [ 14 ], dynamic fuzzy cognitive maps [ 15 ], as well as fuzzy logic and genetic algorithms [ 16 ], are applied.…”

Section: State Of the Artmentioning

confidence: 99%

A Fuzzy Virtual Actuator for Automated Guided Vehicles

Stetter

2020

Sensors

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In the last decades, virtual sensors have found increasing attention in the research community. Virtual sensors employ mathematical models and different sources of information such as actuator states or sensors, which are already existing in a system, in order to generate virtual measurements. Additionally, in recent years, the concept of virtual actuators has been proposed by leading researchers. Virtual actuators are parts of a fault-tolerant control strategy and aim to accommodate faults and to achieve a safe operation of a faulty plant. This paper describes a novel concept for a fuzzy virtual actuator applied to an automated guided vehicle (AGV). The application of fuzzy logic rules allows integrating expert knowledge or experimental data into the decision making of the virtual actuator. The AGV under consideration disposes of an innovative steering concept, which leads to considerable advantages in terms of maneuverability, but requires an elaborate control system. The application of the virtual actuator allows the accommodation of several possible faults, such as a slippery surface under one of the drive modules of the AGV.

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Section: State Of the Artmentioning

confidence: 99%

A Fuzzy Virtual Actuator for Automated Guided Vehicles

Stetter

2020

Sensors

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“…Currently LIDAR-based systems have found wide acceptance in industry. Cheap reflectors spread across the factory provide landmarks from which the LIDAR's position is determined [22]. This facilitates positioning, but requires an expensive sensor for each object of interest.…”

Section: Localisationmentioning

confidence: 99%

Distributed Coordination, Transportation & Localisation in Industry 4.0

Parys

Verbandt

Kotzé³

et al. 2018

2018 International Conference on Indoor Positioning and Indoor Navigation (IPIN)

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Factories of the future must be agile to adapt to rapidly changing customer needs, market volatility and shortened product life cycles. This requires flexibility in hardware and software at distinct levels of the factory and manufacturing process: multipurpose machines with fast change-overs, easy to use reconfigurable software and distributed decision-making are key. This paper leverages the new Industry 4.0 design principles to cope with these new manufacturing requirements: (i) distributed auction-based coordination allows local decision-making and task allocation, (ii) distributed model predictive controlbased transportation enables free space collision avoidance of automated guided vehicles (AGVs), and (iii) distributed visionbased localisation provides scalable and dynamic position information of key resources on the factory floor. Furthermore, these contributions are brought together in a lab-scale reconfigurable manufacturing system to showcase modularity and distributed decision-making at several levels of a manufacturing process' logistics.

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“…However, the ability of mapping is still limited to the low-speed applications in a small and “feature-rich” environment. For unmanned vehicles or robots, the warehouse environment lacks the so-called “features”, and most scenes are quite similar from robot’s perception, which is one of the main obstacles causing navigation failure [ 11 , 12 ]. Many studies have been conducted about the sensor fusion technique, and Visual-SLAM is an alternative approach for 3D SLAM with low-cost camera [ 13 ], but it is gated by the computing capability.…”

Section: Introductionmentioning

confidence: 99%

A Lightweight Localization Strategy for LiDAR-Guided Autonomous Robots with Artificial Landmarks

Wang

Chen

Ding³

et al. 2021

Sensors

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This paper proposes and implements a lightweight, “real-time” localization system (SORLA) with artificial landmarks (reflectors), which only uses LiDAR data for the laser odometer compensation in the case of high-speed or sharp-turning. Theoretically, due to the feature-matching mechanism of the LiDAR, locations of multiple reflectors and the reflector layout are not limited by geometrical relation. A series of algorithms is implemented to find and track the features of the environment, such as the reflector localization method, the motion compensation technique, and the reflector matching optimization algorithm. The reflector extraction algorithm is used to identify the reflector candidates and estimates the precise center locations of the reflectors from 2D LiDAR data. The motion compensation algorithm predicts the potential velocity, location, and angle of the robot without odometer errors. Finally, the matching optimization algorithm searches the reflector combinations for the best matching score, which ensures that the correct reflector combination could be found during the high-speed movement and fast turning. All those mechanisms guarantee the algorithm’s precision and robustness in the high speed and noisy background. Our experimental results show that the SORLA algorithm has an average localization error of 6.45 mm at a speed of 0.4 m/s, and 9.87 mm at 4.2 m/s, and still works well with the angular velocity of 1.4 rad/s at a sharp turn. The recovery mechanism in the algorithm could handle the failure cases of reflector occlusion, and the long-term stability test of 72 h firmly proves the algorithm’s robustness. This work shows that the strategy used in the SORLA algorithm is feasible for industry-level navigation with high precision and a promising alternative solution for SLAM.

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Graph SLAM based mapping for AGV localization in large-scale warehouses

Cited by 34 publications

References 13 publications

A Fuzzy Virtual Actuator for Automated Guided Vehicles

A Fuzzy Virtual Actuator for Automated Guided Vehicles

Distributed Coordination, Transportation & Localisation in Industry 4.0

A Lightweight Localization Strategy for LiDAR-Guided Autonomous Robots with Artificial Landmarks

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