Collision avoidance approaches for autonomous mobile robots to tackle the problem of pedestrians roaming on campus road

Manikandan, N.S.; Kaliyaperumal, Ganesan

doi:10.1016/j.patrec.2022.06.005

Cited by 8 publications

(14 citation statements)

References 18 publications

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“…In terms of steering type, purpose, curvature detection method, small obstacles on its path, and slip angle error rate, The proposed algorithm is compared with existing methods in Table 11. According to Ali & Mailah [20], and Manikandan & Ganesan [21], a camera mounted on the mobile robot with DC motor-driven steering was utilized to extract road features [21], and the mobile robot successfully travelled along the on-road location (described in Section E.1), as illustrated in Figures 28 (a shown in Figures 28 (c) and (d), because the region is larger and the road borders are hard to locate. If the mobile robot was larger, this problem might be avoided.…”

Section: Discussionmentioning

confidence: 99%

“…The proposed algorithm eliminates road-wide intersection issues, as the curve-aware Pure Pursuit algorithm was navigating the mobile robot and it was also aware of the curvature speed limit. The proposed Linear actuator-controlled steering system with a curve-aware Pure Pursuit algorithm was compared with the existing Ali & Mailah [20] and Manikandan & Ganesan [21] algorithms in terms of performance metrics and RMSE metrics, as shown in Tables 9 and 11, respectively. In table 9, the existing algorithm navigated the mobile robot at maximum speed (10 km/h) in the curvature path, moved off the road, and fell in a dig before reaching the target.…”

Section: Discussionmentioning

confidence: 99%

“…LS and sensor fusion data from a laser range finder, camera, and odometry are used to calculate path planning and roundabout (curvature) detection. Using the DC motor-controlled steering system, Manikandan and Ganesan [21] have proposed a novel navigation cum decision-making algorithm to tackle the uncertain direction of wandering pedestrians on Indian roads. Their algorithm can detect an unmarked lane on the road, detect obstacles, and track the direction of movement of the obstacles and provide stability to the mobile robot.…”

Section: Introductionmentioning

confidence: 99%

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Hardware and Software Design for Mobile Robot’s Navigation Over On-Road and Off-Road Curvature Paths

Manikandan

Kaliyaperumal

2023

IEEE Access

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Section: Discussionmentioning

confidence: 99%

Section: Discussionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Hardware and Software Design for Mobile Robot’s Navigation Over On-Road and Off-Road Curvature Paths

Manikandan

Kaliyaperumal

2023

IEEE Access

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“…Based on ABI Research, an expected shipment volume of outdoor mobile robots is 350,000 units by 2030, compared to 40,000 units in 2021 [1]. Much research has been published on autonomous performance improvement to fit the outdoor environment; for example, environmental perception [2], visual navigation [3], path planning [4], localisation [5], motion control [6], and pedestrian safety [7]. Though the system degradation in an outdoor robot is faster due to its exposure to extreme terrain and weather changes, focus on Condition Monitoring (CM) studies are still not commonly used to assess health states and terrain-related flaws, causing accelerated deterioration and potential hazards.…”

Section: Introductionmentioning

confidence: 99%

AI-Enabled Vibrotactile Feedback-Based Condition Monitoring Framework for Outdoor Mobile Robots

Pookkuttath,

Abdulkader,

Elara

et al. 2023

Mathematics

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An automated Condition Monitoring (CM) and real-time controlling framework is essential for outdoor mobile robots to ensure the robot’s health and operational safety. This work presents a novel Artificial Intelligence (AI)-enabled CM and vibrotactile haptic-feedback-based real-time control framework suitable for deploying mobile robots in dynamic outdoor environments. It encompasses two sections: developing a 1D Convolutional Neural Network (1D CNN) model for predicting system degradation and terrain flaws threshold classes and a vibrotactile haptic feedback system design enabling a remote operator to control the robot as per predicted class feedback in real-time. As vibration is an indicator of failure, we identified and separated system- and terrain-induced vibration threshold levels suitable for CM of outdoor robots into nine classes, namely Safe, moderately safe system-generated, and moderately safe terrain-induced affected by left, right, and both wheels, as well as severe classes such as unsafe system-generated and unsafe terrain-induced affected by left, right, and both wheels. The vibration-indicated data for each class are modelled based on two sensor data: an Inertial Measurement Unit (IMU) sensor for the change in linear and angular motion and a current sensor for the change in current consumption at each wheel motor. A wearable novel vibrotactile haptic feedback device architecture is presented with left and right vibration modules configured with unique haptic feedback patterns corresponding to each abnormal vibration threshold class. The proposed haptic-feedback-based CM framework and real-time remote controlling are validated with three field case studies using an in-house-developed outdoor robot, resulting in a threshold class prediction accuracy of 91.1% and an effectiveness that, by minimising the traversal through undesired terrain features, is four times better than the usual practice.

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“…As per ABI research, the expected shipment of outdoor mobile robots by 2030 is 350,000 units, compared with 40,000 units in 2021, a compound annual growth rate (CAGR) of 27% [1]. The state-of-theart technology for outdoor mobile robots is more focused on operational performance, for instance, understanding the outdoor scene and environmental perception [2], pose estimation and localization [3], visual navigation [4], motion control design [5], path planning [6], and pedestrian safety [7]. Since outdoor mobile robots are exposed to uneven terrain and weather extremities, the system deterioration rate in outdoor mobile robots is higher compared with those for indoor applications.…”

Section: Introductionmentioning

confidence: 99%

AI-Enabled Condition Monitoring Framework for Outdoor Mobile Robots Using 3D LiDAR Sensor

Pookkuttath,

Palanisamy,

Elara

2023

Mathematics

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An automated condition monitoring (CM) framework is essential for outdoor mobile robots to trigger prompt maintenance and corrective actions based on the level of system deterioration and outdoor uneven terrain feature states. Vibration indicates system failures and terrain abnormalities in mobile robots; hence, five vibration threshold classes for CM in outdoor mobile robots were identified, considering both vibration source system deterioration and uneven terrain. This study proposes a novel CM approach for outdoor mobile robots using a 3D LiDAR, employed here instead of its usual use as a navigation sensor, by developing an algorithm to extract the vibration-indicated data based on the point cloud, assuring low computational costs without losing vibration characteristics. The algorithm computes cuboids for two prominent clusters in every point cloud frame and sets motion points at the corners and centroid of the cuboid. The three-dimensional vector displacement of these points over consecutive point cloud frames, which corresponds to the vibration-affected clusters, are compiled as vibration indication data for each threshold class. A simply structured 1D Convolutional Neural Network (1D CNN)-based vibration threshold prediction model is proposed for fast, accurate, and real-time application. Finally, a threshold class mapping framework is developed which fuses the predicted threshold classes on the 3D occupancy map of the workspace, generating a 3D CbM map in real time, fostering a Condition-based Maintenance (CbM) strategy. The offline evaluation test results show an average accuracy of vibration threshold classes of 89.6% and consistent accuracy during real-time field case studies of 89%. The test outcomes validate that the proposed 3D-LiDAR-based CM framework is suitable for outdoor mobile robots, assuring the robot’s health and operational safety.

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Collision avoidance approaches for autonomous mobile robots to tackle the problem of pedestrians roaming on campus road

Cited by 8 publications

References 18 publications

Hardware and Software Design for Mobile Robot’s Navigation Over On-Road and Off-Road Curvature Paths

Hardware and Software Design for Mobile Robot’s Navigation Over On-Road and Off-Road Curvature Paths

AI-Enabled Vibrotactile Feedback-Based Condition Monitoring Framework for Outdoor Mobile Robots

AI-Enabled Condition Monitoring Framework for Outdoor Mobile Robots Using 3D LiDAR Sensor

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