Blind-Spot Monitoring System using Lidar

Pawar, Kuldeep S.; Teli, S. N.; Shetye, Prasad; Shetty, Saukshit; Satam, Vedant; Sahani, Atul

doi:10.1007/s40032-022-00856-2

Cited by 6 publications

(2 citation statements)

References 15 publications

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“…A 2D LIDAR is considered, which is set to scan at the midship with a maximum detecting range of 1341 m. The area that exceeds this range constitutes the sensor's blind-spot zone, where no information can be derived from [117]. It is worth noting that the selected maximum detecting range is not arbitrary but it is comparative to the manoeuvrability of the OS.…”

Section: Case Studymentioning

confidence: 99%

Robust Decision-Making for the Reactive Collision Avoidance of Autonomous Ships against Various Perception Sensor Noise Levels

Lee,

Theotokatos,

Boulougouris

2024

JMSE

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Autonomous ships are expected to extensively rely on perception sensors for situation awareness and safety during challenging operations, such as reactive collision avoidance. However, sensor noise is inevitable and its impact on end-to-end decision-making has not been addressed yet. This study aims to develop a methodology to enhance the robustness of decision-making for the reactive collision avoidance of autonomous ships against various perception sensor noise levels. A Gaussian-based noisy perception sensor is employed, where its noisy measurements and noise variance are incorporated into the decision-making as observations. A deep reinforcement learning agent is employed, which is trained in different noise variances. Robustness metrics that quantify the robustness of the agent’s decision-making are defined. A case study of a container ship using a LIDAR in a single static obstacle environment is investigated. Simulation results indicate sophisticated decision-making of the trained agent prioritising safety over efficiency when the noise variance is higher by conducting larger evasive manoeuvres. Sensitivity analysis indicates the criticality of the noise variance observation on the agent’s decision-making. Robustness is verified against noise variance up to 132% from its maximum trained value. Robustness is verified only up to 76% when the agent is trained without the noise variance observation with lack of its prior sophisticated decision-making. This study contributes towards the development of autonomous systems that can make safe and robust decisions under uncertainty.

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Section: Case Studymentioning

confidence: 99%

Robust Decision-Making for the Reactive Collision Avoidance of Autonomous Ships against Various Perception Sensor Noise Levels

Lee,

Theotokatos,

Boulougouris

2024

JMSE

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“…18 LiDAR technologies use near infrared light to illuminate target surface, and then distance and reflectivity measure of the surface can be estimated from the reflected light. 19 The key benefit of this technology is both elevation and intensity image can be obtained with high accuracy. 20 All reflected points are gathered to generate a 3D model of the illuminated object/surface.…”

Section: Selection Of Componentsmentioning

confidence: 99%

Development of LiDAR operated inventory control and assistance robot

Sharma¹,

Patange

Padalghare³

et al. 2023

Proceedings of the Institution of Mechanical Engineers, Part E:

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The cordial existence of robots and humans in the same workstation is an important aspect of the deployment of autonomous systems. Usually, robots are integrated with auxiliary sensors for monitoring the surroundings and tackling unexpected actions. The incorporation of LiDAR (light detection and ranging) and SLAM (simultaneous localization and mapping) has gained the researcher’s attention these days. The current study aims at developing a collision-free motion for an autonomous robot to ensure the least possible travel period at internodes. First, a SLAM-based algorithm for a static environment is proposed. A low-cost microcontroller Arduino UNO is used here along with LiDAR VL53L0X and is integrated with MATLAB to store and display the acquired data. The A* algorithm is used to navigate between the shortest paths on the obtained map. The robot is also integrated with radio frequency identification (RFID) sensors to automate the process. The proposed system would be useful for material handling, assistance, and cleaning purpose in large warehouses or households.

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