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  • 11th Asia-Pacific Regional Conference of the ISTVS
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  • Papers of the 11th Asia-Pacific Regional Conference of the ISTVS
    • 0303 / Composite Beam Tests with Closed Cell Polyurethane and Aluminum Foam
    • 0356 / Design and Simulation Analysis of Intelligent Suspension for Manned Lunar Rover
    • 0861 / Review of the Reconfigurable Wheel-Tracked System
    • 0963 / A Wheel and Vehicle Mobility Index Based on Traction and Velocity...
    • 1128 / Semi-Active Reinforcement Learning Suspension Control for the Off-Road Vehicles
    • 1491 / Design and Verification of a Creeping Mars Rover
    • 1534 / Foothold Selection Considering Constraint and Slippage Evaluation for Legged Robots
    • 1561 / Prominent Problems and Thoughts of “Paddy Soil-Terrain Machine System”...
    • 1655 / Modeling of Lunar Rover Vehicle Wheel-Soil Interaction Using Fem-Dem Method
    • 2034 / A Comprehensive Lumped Parameter Approach for the Dynamic Simulation...
    • 2149 / Investigation of the Shear Stress Dynamics on Silty Loam Soil and Measurement...
    • 2190 / Tyre Parameterization Tests: Dynamic vs. Static
    • 2539 / Model Predictive Control of a Robot Driven Vehicle for Testing of Advanced Driver...
    • 2632 / Energy Consumption Analysis of Door Opening with a Mobile Manipulator...
    • 2643 / An Improved Simultaneous Localization and Mapping Method Base on LeGO-LOAM and Motion Compens
    • 3351 / Benchmarking of Compression Testing Devices in Snow
    • 4054 / Field Validation of Egress Process for Planetary Rover
    • 4243 / Soil Compaction Monitoring Technique Using Deep Learning
    • 4260 / The Running Gear Construction Impact on Obstacles Overcoming by Light High-Mobility UGV
    • 4409 / Design of Self-Driving Bulldozer System
    • 4744 / Terrain Classification Using Mars Raw Images Based on Deep Learning Algorithms...
    • 4774 / Steadily Learn to Drive with Virtual Memory
    • 4782 / Experimental Study of Track-Soil Interactions of the Steering Performance of Tracked...
    • 4812 / Multi-Fidelity Machine Learning Modeling for Wheeled Locomotion on Soft Soil
    • 4827 / Introducing Polibot: A High Mobility Tracked Robot with Innovative Passive Suspensions
    • 5060 / Bionic Quadruped Robot for Mars Surface Exploration
    • 5408 / Ride Comfort Comparison Between Suspension Modes: Input Towards Designing Difference...
    • 5800 / Interaction Modeling and Dynamic Control Strategy for C-Shaped Leg with Sandy Terrain...
    • 5979 / Research on Drag Reduction Performance of Sliding Plate of Rice Direct Seeding Machine...
    • 6174 / Factors Affecting Bevameter Soil Characterization
    • 6316 / Perceptive Locomotion of Legged Robot Coupling Model Predictive Control and Terrain Mapping
    • 6718 / Research on Vehicle Running Performance on Paved Roads Covered with Falling Volcanic Ash
    • 6796 / Nonparametric Terrain Estimation Based on the Interaction Simulation Between Planetary...
    • 7018 / A Review of Modeling and Validation Techniques for Tire-Deformable Soil Interactions
    • 7092 / A Time Domain Passivity Controller for Teleoperation of Four Wheeled Differential...
    • 7199 / Vehicle Dynamic Factor Characterized by Actual Velocity and Combined Influence...
    • 7233 / Study of Passive Steering Mechanism for Mars Surface Exploration Rovers
    • 7399 / Tire-Soil Tangential Force Reinforcement Learning Modeling
    • 7878 / A Method for Fast Obtaining of Soil Shear Strength Index Based on Dem Free-Fall Cone...
    • 8131 / Parameters Calibration of Red Clay Soil in Hilly Area of Southwest China for Discrete...
    • 8349 / The Effect of Integrating a Bio-Inspired Convex Structure with a Low-Surface Energy...
    • 8654 / Construction of a Soil Clods Recognition Bench-Scale Experiment for Discrete Element...
    • 8658 / Investigation of the Relationship between the Cone Index and the Physical and...
    • 9352 / 3D-DEM Simulation and Post-Process Method of Wheel-Terrain Interaction for Planetary Rovers
    • 9768 / Design and Traction Performance Test of Bionic Paddy Wheel Based on Cattle Hoof
    • 9913 / Acquisition of Flipper Motion in Step-Climbing of Tracked Robot Using Reinforcement Learning
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  1. Papers of the 11th Asia-Pacific Regional Conference of the ISTVS

6718 / Research on Vehicle Running Performance on Paved Roads Covered with Falling Volcanic Ash

Paper presented at the 11th Asia-Pacific Regional Conference of the ISTVS

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Last updated 2 years ago

Title: Research on Vehicle Running Performance on Paved Roads Covered with Falling Volcanic Ash

Authors: Junya Yamakawa, Ryosuke Eto, Yasuhiro Ichikado, Mitsuhiro Yoshimoto, Tatsuji Nishizawa, Tomohiro Kubo, and Hiroyuki Yamada

Abstract: Japan has many active volcanoes, and a large eruption can cause ash fall over a wide area. The accumulation of volcanic ash on paved roads affects the driving of vehicles. Therefore, we collected data by driving over volcanic ash spread on paved roads with a vehicle equipped with instruments that can measure the force applied to the running tires. Three types of volcanic ash with different grain sizes and thicknesses of 1 cm, 5 cm, and 10 cm were used. Experiments were conducted at constant speeds of 10 to 40 km/h and at rapid acceleration and deceleration. Data were collected not only on flat straight roads but also on curved roads with a radius of 30 m. In addition to the tire's longitudinal and side forces and the normal load, the camber angle, rotational speed, and ground speed were measured. The slip ratio can be obtained from the difference between the tire rotation speed and the ground speed. Further, since the ground speed of the tire is measured in two directions, the longitudinal and lateral directions of the tire, the sideslip angle of the tire can be calculated. From these data, the characteristics of rolling resistance, driving force, and side force on an ash-fall road surface can be obtained. This paper describes the driving roads on which the experiments were conducted and the effects of volcanic ash on driving by processing the tire data obtained. As the thickness of the volcanic ash increases, the rolling resistance increases, but as the driving speed increases, the rolling resistance tends to decrease. The relationship between the sideslip angle and lateral force can be obtained by processing the data for turn, and these effects are investigated using Gaussian process regression, which takes into account several variables such as sideslip angle, camber angle and normal load.

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https://doi.org/10.56884/YTYF8036
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