Research on a Panoramic Mobile Robot for Autonomous Navigation

Gao, Chong; Li, Xinlu; Ge, Xinyu; Bai, Yanxin; Liu, Chen; Zhang, Jing; Wu, Guodong; Shao, Weiqiang

doi:10.2991/icmeit-19.2019.35

Cited by 5 publications

(2 citation statements)

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“…Simultaneous Localization and Mapping (SLAM) is a fundamental technology in intelligent robot systems that enables the robot to autonomously navigate and map its environment [7]. SLAM allows for the positioning of robots in the map while creating the map [8],which is necessary For our autonomous robots.…”

Section: Slam Algorithmmentioning

confidence: 99%

A multifunctional ship sorting and clearance robot implementation model

2024

AJETS

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In the process of transporting commodity by sea, the ship clearance of general cargo ship has always been a hassle for the crew. Under a tight schedule and heavy task chart, it is frequently essential to work day and night upside down for crew to accomplish the cleaning of the cabin and unloading work. Sometimes it may not even be possible to complete unloading and clearance in time, resulting in massive economic losses. In this paper, a multi-functional ship clearance and sorting robot model is proposed based on the actual environment of the cargo ship's hold, which can not only realize the sorting of goods, but also assist in the cleaning of the ship hold after unloading. We have combined a robotic arm, a relevant target recognition algorithm and an Automatic Guided Vehicle(AGV) chassis which equipped with a Simultaneous Localization and Mapping (SLAM) algorithm to realize this robot, Collaborate with each other through the Robot Operating System(ROS) system. Experimental trials show that our robot is more accurate in positioning in the cabin environment compared to other algorithms, and has the preliminary function of cleaning the cabins of general cargo ships as a potential intelligent future trend, it may be useful for ship clearance functions. This project can be applied to the above scenarios or other similar areas. This article also compared two classic SLAM algorithms: hector slam and gmapping, and ultimately concluded that hector slam is more suitable for the above environment.

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Section: Slam Algorithmmentioning

confidence: 99%

A multifunctional ship sorting and clearance robot implementation model

2024

AJETS

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“…Lasers can accurately obtain the depth information of the surrounding environment and form a high-precision point cloud. Thus, these devices have been widely applied to various tasks, such as location [1], navigation [2], three-dimension reconstruction [3], etc. Among them, 3D laser scanners (LSs) and 2D laser rangefinders (LRFs) are the most widely used devices.…”

Section: Introductionmentioning

confidence: 99%

Extrinsic Calibration of Multiple Two-Dimensional Laser Rangefinders Based on a Trihedron

Zhu

Huang

Tian

et al. 2020

Sensors

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Multiple two-dimensional laser rangefinders (LRFs) are applied in many applications like mobile robotics, autonomous vehicles, and three-dimensional reconstruction. The extrinsic calibration between LRFs is the first step to perform data fusion and practical application. In this paper, we proposed a simple method to calibrate LRFs based on a corner composed of three mutually perpendicular planes. In contrast to other methods that require a special pattern or assistance from other sensors, the trihedron corner needed in this method is common in daily environments. In practice, we can adjust the position of the LRFs to observe the corner until the laser scanning plane intersects with three planes of the corner. Then, we formed a Perspective-Three-Point problem to solve the position and orientation of each LRF at the common corner coordinate system. The method was validated with synthetic and real experiments, showing better performance than existing methods.

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