Inertial sensor-based outdoor terrain classification for wheeled mobile robots

Csík, Dominik; Odry, Ákos; Sárosi, József; Šarčević, Peter

doi:10.1109/sisy52375.2021.9582504

Cited by 8 publications

(11 citation statements)

References 18 publications

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“…However, the incorporated sensors are imperfect, namely, the inherent noise (e.g., random measurement noise and temperature dependent bias) yields to imprecise motion-model based pose results. On one hand, drift is generated during data processing, on the other hand, the uncertain parameters of the physical system, moreover, the uncertainties induced by the environment of the robot (e.g., uneven, and slippery terrain [3,7]) significantly reduce the estimation performance and thus the reliability of the so-called a priori estimate.…”

Section: Methodsmentioning

confidence: 99%

Sensor fusion-based localization methods for mobile robots

Odry

Csík

Stefanoni³

et al. 2022

Anal. Tech. Szeged.

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Localization aims to provide the best estimate of the robot pose. It is a crucial algorithm in every robotics application, since its output directly determines the inputs of the robot to be controlled in its configuration space. In real world of engineering, the robot dynamics related measurements are subject to both uncertainties and disturbances. These error sources yield unreliable inferences of the robot state, which inherently result in wrong consensus about the appropriate control strategy to be applied. This outcome may drive the system out of stability and damage both the physical system and its environment. The localization algorithm captures the uncertainties with probabilistic approaches. Namely, the measurement processes are modelled along with their unreliability, moreover, the synergy of multiple information sources is formulated with the aim to calculate the most probable estimate of the robot pose. In essence, this algorithm is composed of two main parts, i.e., first the dynamics of the system is derived, and the corresponding uncertainties are initially predicted, next the additional sensor information is incorporated in the algorithm to refine the posterior estimate. This approach provides the state-of-the-art solution for the derivation of mobile robot poses in real applications.

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Section: Methodsmentioning

confidence: 99%

Sensor fusion-based localization methods for mobile robots

Odry

Csík

Stefanoni³

et al. 2022

Anal. Tech. Szeged.

Self Cite

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“…It is crucial to monitor and ensure the health conditions of an outdoor mobile robot and that its workspace terrain features are safe or at acceptable levels during its entire course of operation, as here the robot is exposed to undetected and uneven terrain features and adverse weather conditions, which causes an accelerated mode of system degradation resulting unexpected downtime, catastrophic failure, high maintenance cost, operational hazards, and customer dissatisfaction. The terrainability studies mentioned in [9][10][11][12][13][14] will help to plan paths, especially for last mail delivery or security robots, where the end destination or point-to-point travel can be executed with a suitable path planner, minimizing the traverse through unstructured terrain. However, such plans may need to be revised often as the terrain features may keep changing in an outdoor environment.…”

Section: Problem Statementmentioning

confidence: 99%

“…It is generally conducted by two approaches [8]: a wheel-terrain interactive vibration proprioceptive traversability analysis (retrospective techniques) and geometry or appearance-based exteroceptive analysis (prospective methods) using onboard sensors and adopting suitable Artificial Intelligence (AI) techniques for accurate classification. Some examples include terrain classification studies using an Inertial Measurement Unit (IMU) in [9,10], camerabased classification in [11], and a combination of camera and IMU-based classification in [12]. Similarly, a traversability analysis using 3D LiDAR is conducted in [13], and a comparative study showed better terrain classification under different lighting conditions using a 3D LiDAR sensor than a camera in [14].…”

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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“…An environmentindependent sensor selection for data acquisition and a fast and accurate classifier model is crucial for real-time CM frameworks for the outdoor environment. There are numerous AI-enabled and vibration-based terrainability studies for outdoor robots using different sensors to classify tile, stone, sand, asphalt, grass, and gravel [15][16][17][18][19], which will help to decide the traversability of the robot, but there is not a proper CM approach for assessing the health condition of the robot and level of terrain-induced concerns that accelerate system degradation. Hence, an automated CM and feasible remote controlling framework involving a shared autonomy and haptic feedback system is imperative for outdoor robots assisting in either stopping or minimising the exposure to extreme terrain conditions based on the internal health and external terrain states of the robot.…”

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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Inertial sensor-based outdoor terrain classification for wheeled mobile robots

Cited by 8 publications

References 18 publications

Sensor fusion-based localization methods for mobile robots

Sensor fusion-based localization methods for mobile robots

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

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

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