Navigation of Mobile Robot Through Mapping Using Orbbec Astra Camera and ROS in an Indoor Environment

Basavanna, M.; Shivakumar, M.; Prakash, Krishna

doi:10.1007/978-981-16-4222-7_53

Cited by 6 publications

(2 citation statements)

References 9 publications

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“…In the realm of localization and mapping, mobile robots are tasked with estimating their position within a given environment-a process aptly termed localization [7,8]. Simultaneously, mapping involves the construction of a model of the robot's surroundings, leveraging data harvested from its sensors [9]. The ingenious technique of Simultaneous Localization and Mapping (SLAM) deftly addresses both these tasks in tandem, allowing the robot to dynamically build a map while continually ascertaining its real-time position [10][11][12].…”

Section: Introductionmentioning

confidence: 99%

Elevating Mobile Robotics: Pioneering Applications of Artificial Intelligence and Machine Learning

Nasrallah,

Stepanyan,

Nassrullah

et al. 2024

RIA

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The present study delves into the utilization of subsumption architecture for the modeling of mobile robot behaviors, particularly those that respond adaptively to environmental dynamics and inaccuracies in sensor measurements. Central to this investigation is the deployment of reactive controller networks, wherein each node-representing a distinct state-is governed by sensor-triggered conditions that dictate state transitions. The methodology adopted comprises a thorough literature review, encompassing sources from IEEE Xplore, ScienceDirect, and the ACM Digital Library, which discuss the integration of subsumption architecture in the realm of mobile robot control. Through this review, the effectiveness of subsumption architecture in crafting reactive robotic behaviors is underscored. It has been established that augmented finite state machines (AFSMs), which are integral to the subsumption architecture and possess internal timing mechanisms, are pivotal in managing the temporal aspects of state transitions. Additionally, the technique of layering-merging multiple simple networks to form intricate behavior patterns-emerges as a significant finding, accentuating the architecture's capability to facilitate complex behavioral constructs. The prime contribution of this body of work lies in identifying and elucidating the strategic role of subsumption architecture in enhancing the adaptability and robustness of mobile robots. The insights gleaned from this study not only advance our understanding of robotic control systems but also hold implications for the amplification of industrial efficiency and effectiveness through the application of sophisticated AI and machine learning techniques in mobile robotics.

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Section: Introductionmentioning

confidence: 99%

Elevating Mobile Robotics: Pioneering Applications of Artificial Intelligence and Machine Learning

Nasrallah,

Stepanyan,

Nassrullah

et al. 2024

RIA

View full text Add to dashboard Cite

show abstract

“…The single-line LiDAR sensor can scan and identify obstacles in the plane while performing realtime updates, which has the advantages of high accuracy and fast map building [3] , but the single-line LiDAR can only scan on a certain horizontal plane and cannot scan and identify obstacles above or below the horizontal plane; the depth camera sensor collects pixel depth information directly, which is less computationally intensive, and collects location information faster and more abundant [4] , has a wider vertical view, and can detect obstacles that cannot be recognized by LiDAR, but because of the view angle, it takes more time to build the map, and the accuracy of the constructed map is not enough to be used directly for navigation, and the result of building the map is more easily affected by the lighting factor [5] .…”

Section: Introductionmentioning

confidence: 99%

A Mobile Robot Mapping Method Integrating Lidar and Depth Camera

Jia

Zhao

2022

J. Phys.: Conf. Ser.

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For mobile robots in uncertain and complex environments, a single sensor obtains a single set of environmental information, creates maps with low accuracy, is easily disturbed by external factors, and easily collides with obstacles in the navigation process. In this paper, we propose a depth camera and LiDAR fusion map building method, which firstly collects data using the LiDAR and depth camera on board the mobile robot, and then preprocesses the collected radar data and depth data and builds a 2D local environment map using RTAB-MAP algorithm. Finally, the local maps are fused using a voxel filter to stitch together a complete global map to complete the map construction. Experimental validation on a ROS-based mobile robot platform shows that the map built using multi-sensor fusion is more complete, presenting more comprehensive environmental information and laying the foundation for subsequent navigation.

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Vehicle Localization and Navigation

Tyagi

Sreenath

2022

Disruptive Technologies and Digital Transformations for Society 5.0

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Navigation of Mobile Robot Through Mapping Using Orbbec Astra Camera and ROS in an Indoor Environment

Cited by 6 publications

References 9 publications

Elevating Mobile Robotics: Pioneering Applications of Artificial Intelligence and Machine Learning

Elevating Mobile Robotics: Pioneering Applications of Artificial Intelligence and Machine Learning

A Mobile Robot Mapping Method Integrating Lidar and Depth Camera

Vehicle Localization and Navigation

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