The Experts below are selected from a list of 327 Experts worldwide ranked by ideXlab platform
Kaspar Althoefer - One of the best experts on this subject based on the ideXlab platform.
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Hybrid Soil Parameter Measurement and Estimation Scheme for Excavation Automation
IEEE Transactions on Instrumentation and Measurement, 2009Co-Authors: Kaspar Althoefer, Yahya Zweiri, Choo Par Tan, Lakmal SeneviratneAbstract:Real-time measurement and estimation of Soil-tool interaction dynamics are key tools for the development of automated excavation systems. Automating a complex operation such as excavation is a challenging task that strongly hinges on a good estimate of Soil properties as well as the interaction forces between the excavator tool and the environment. This paper presents a method for identifying all the unknown Soil Parameters (density, tool-Soil friction angle, Soil-Soil friction angle, and cohesion) required for predicting the interaction forces in real time using a novel hybrid Soil model. The hybrid model consists of the Mohr-Coulomb (M-C) Soil model and the Chen and Liu upper bound (CLUB) Soil model. A switching mode is utilized to select the most appropriate Soil model to compute the failure forces that depend on the position of the excavator bucket. The Newton-Raphson method (NRM) is adapted and is used to identify the Soil Parameters by minimizing the error between the measured forces and the forces computed by the hybrid Soil model. The experimental results presented demonstrate that the proposed estimation scheme is accurate when compared to measured Soil Parameters. The Newton-Raphson estimation method is applied iteratively to efficiently and robustly identify the Parameters. The conducted comparative study shows that the proposed method is a promising way for the on-line Soil-tool interaction force identification of an automated excavator in dynamic and potentially hazardous environments, outperforming other estimation methods including the least squares method (LSM).
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Soil Parameter Identification and Driving Force Prediction for Wheel-Terrain Interaction
International Journal of Advanced Robotic Systems, 2008Co-Authors: Suksun Hutangkabodee, Yahya Zweiri, Lakmal Seneviratne, Kaspar AlthoeferAbstract:This paper considers wheeled vehicles traversing unknown terrain, and proposes an approach for identifying the unknown Soil Parameters required for vehicle driving force prediction (drawbar pull prediction). The predicted drawbar pull can potentially be employed for traversability prediction, traction control, and trajectory following which, in turn, improve overall performance of off-road wheeled vehicles. The proposed algorithm uses an approximated form of the wheel-terrain interaction model and the Generalized Newton Raphson method to identify terrain Parameters in real-time. With few measurements of wheel slip, i, vehicle sinkage, z, and drawbar pull, DP, samples, the algorithm is capable of identifying all the Soil Parameters required to predict vehicle driving forces over an entire range of wheel slip. The algorithm is validated with experimental data from a wheel-terrain interaction test rig. The identified Soil Parameters are used to predict the drawbar pull with good accuracy. The technique presented in this paper can be applied to any vehicle with rigid wheels or deformable wheels with relatively high inflation pressure, to predict driving forces in unknown environments.
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MODEL-BASED Soil Parameter IDENTIFICATION FOR WHEEL-TERRAIN INTERACTION DYNAMICS
IFAC Proceedings Volumes, 2007Co-Authors: Suksun Hutangkabodee, Yahya Zweiri, Lakmal Seneviratne, Kaspar AlthoeferAbstract:Abstract This paper presents an algorithm for identifying Soil Parameters for wheel-terrain interaction dynamics. The Soil Parameters are useful for traversability prediction, traction control, and performance optimization of a wheeled vehicle traveling on unknown terrain. The Composite Simpson's Rule (CSR) is employed to approximate integrals of the full wheel-terrain interaction dynamic model. This is to facilitate the implementation of Soil Parameter identification on this model and allow fast identification speed. The 2-stage iterative Newton Raphson (NR) method is used for Soil Parameter identification. Simulation results show successful identification of a complete set of Soil Parameters with relatively fast speed. The approach in this paper has great potential to be applied for real off-road wheeled vehicle.
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IROS - Validation of Soil Parameter identification for track-terrain interaction Dynamics
2007 IEEE RSJ International Conference on Intelligent Robots and Systems, 2007Co-Authors: Suksun Hutangkabodee, Yahya Zweiri, Lakmal Seneviratne, Kaspar AlthoeferAbstract:This paper considers a tracked vehicle traversing unknown terrain, and proposes an approach based on the Generalized Newton Raphson (GNR) method for identifying all the unknown Soil Parameters required for tractive force prediction. For the first time, the methodology, based on measurements of track slip, i, and tractive force, F, to find unknown Soil Parameters is developed. The tractive force is the force generated by a tracked vehicle to drive itself forwards. This tractive force depends to a large extent on certain Soil Parameters, namely Soil cohesion (c), Soil internal friction angle (phi), and Soil shear deformation modulus (K). Accurately identifying Parameters of the Soil on which a tracked vehicle is moving will potentially lead to accurate traversability prediction, effective traction control, and precise trajectory tracking. The Soil Parameter identification algorithm is validated with the experimental data from Wong [3] and from in-house track- terrain interaction test rig showing good identification accuracy and fast execution speed. It is also shown to be relatively robust to initial condition. The identified Soil Parameters are, in turn, used to predict the tractive forces showing good agreement with all the experimental data. The technique presented in this paper is general and can be applied to any tracked vehicle.
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Soil Parameter identification for wheel-terrain interaction dynamics and traversability prediction
International Journal of Automation and Computing, 2006Co-Authors: Suksun Hutangkabodee, Yahya Zweiri, Lakmal Seneviratne, Kaspar AlthoeferAbstract:This paper presents a novel technique for identifying Soil Parameters for a wheeled vehicle traversing unknown terrain. The identified Soil Parameters are required for predicting vehicle drawbar pull and wheel drive torque, which in turn can be used for traversability prediction, traction control, and performance optimization of a wheeled vehicle on unknown terrain. The proposed technique is based on the Newton Raphson method. An approximated form of a wheel-Soil interaction model based on Composite Simpson’s Rule is employed for this purpose. The key Soil Parameters to be identified are internal friction angle, shear deformation modulus, and lumped pressure-sinkage coefficient. The fourth Parameter, cohesion, is not too relevant to vehicle drawbar pull, and is assigned an average value during the identification process. Identified Parameters are compared with known values, and shown to be in agreement. The identification method is relatively fast and robust. The identified Soil Parameters can effectively be used to predict drawbar pull and wheel drive torque with good accuracy. The use of identified Soil Parameters to design a traversability criterion for wheeled vehicles traversing unknown terrain is presented.
Lakmal Seneviratne - One of the best experts on this subject based on the ideXlab platform.
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Hybrid Soil Parameter Measurement and Estimation Scheme for Excavation Automation
IEEE Transactions on Instrumentation and Measurement, 2009Co-Authors: Kaspar Althoefer, Yahya Zweiri, Choo Par Tan, Lakmal SeneviratneAbstract:Real-time measurement and estimation of Soil-tool interaction dynamics are key tools for the development of automated excavation systems. Automating a complex operation such as excavation is a challenging task that strongly hinges on a good estimate of Soil properties as well as the interaction forces between the excavator tool and the environment. This paper presents a method for identifying all the unknown Soil Parameters (density, tool-Soil friction angle, Soil-Soil friction angle, and cohesion) required for predicting the interaction forces in real time using a novel hybrid Soil model. The hybrid model consists of the Mohr-Coulomb (M-C) Soil model and the Chen and Liu upper bound (CLUB) Soil model. A switching mode is utilized to select the most appropriate Soil model to compute the failure forces that depend on the position of the excavator bucket. The Newton-Raphson method (NRM) is adapted and is used to identify the Soil Parameters by minimizing the error between the measured forces and the forces computed by the hybrid Soil model. The experimental results presented demonstrate that the proposed estimation scheme is accurate when compared to measured Soil Parameters. The Newton-Raphson estimation method is applied iteratively to efficiently and robustly identify the Parameters. The conducted comparative study shows that the proposed method is a promising way for the on-line Soil-tool interaction force identification of an automated excavator in dynamic and potentially hazardous environments, outperforming other estimation methods including the least squares method (LSM).
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Soil Parameter Identification and Driving Force Prediction for Wheel-Terrain Interaction
International Journal of Advanced Robotic Systems, 2008Co-Authors: Suksun Hutangkabodee, Yahya Zweiri, Lakmal Seneviratne, Kaspar AlthoeferAbstract:This paper considers wheeled vehicles traversing unknown terrain, and proposes an approach for identifying the unknown Soil Parameters required for vehicle driving force prediction (drawbar pull prediction). The predicted drawbar pull can potentially be employed for traversability prediction, traction control, and trajectory following which, in turn, improve overall performance of off-road wheeled vehicles. The proposed algorithm uses an approximated form of the wheel-terrain interaction model and the Generalized Newton Raphson method to identify terrain Parameters in real-time. With few measurements of wheel slip, i, vehicle sinkage, z, and drawbar pull, DP, samples, the algorithm is capable of identifying all the Soil Parameters required to predict vehicle driving forces over an entire range of wheel slip. The algorithm is validated with experimental data from a wheel-terrain interaction test rig. The identified Soil Parameters are used to predict the drawbar pull with good accuracy. The technique presented in this paper can be applied to any vehicle with rigid wheels or deformable wheels with relatively high inflation pressure, to predict driving forces in unknown environments.
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MODEL-BASED Soil Parameter IDENTIFICATION FOR WHEEL-TERRAIN INTERACTION DYNAMICS
IFAC Proceedings Volumes, 2007Co-Authors: Suksun Hutangkabodee, Yahya Zweiri, Lakmal Seneviratne, Kaspar AlthoeferAbstract:Abstract This paper presents an algorithm for identifying Soil Parameters for wheel-terrain interaction dynamics. The Soil Parameters are useful for traversability prediction, traction control, and performance optimization of a wheeled vehicle traveling on unknown terrain. The Composite Simpson's Rule (CSR) is employed to approximate integrals of the full wheel-terrain interaction dynamic model. This is to facilitate the implementation of Soil Parameter identification on this model and allow fast identification speed. The 2-stage iterative Newton Raphson (NR) method is used for Soil Parameter identification. Simulation results show successful identification of a complete set of Soil Parameters with relatively fast speed. The approach in this paper has great potential to be applied for real off-road wheeled vehicle.
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IROS - Validation of Soil Parameter identification for track-terrain interaction Dynamics
2007 IEEE RSJ International Conference on Intelligent Robots and Systems, 2007Co-Authors: Suksun Hutangkabodee, Yahya Zweiri, Lakmal Seneviratne, Kaspar AlthoeferAbstract:This paper considers a tracked vehicle traversing unknown terrain, and proposes an approach based on the Generalized Newton Raphson (GNR) method for identifying all the unknown Soil Parameters required for tractive force prediction. For the first time, the methodology, based on measurements of track slip, i, and tractive force, F, to find unknown Soil Parameters is developed. The tractive force is the force generated by a tracked vehicle to drive itself forwards. This tractive force depends to a large extent on certain Soil Parameters, namely Soil cohesion (c), Soil internal friction angle (phi), and Soil shear deformation modulus (K). Accurately identifying Parameters of the Soil on which a tracked vehicle is moving will potentially lead to accurate traversability prediction, effective traction control, and precise trajectory tracking. The Soil Parameter identification algorithm is validated with the experimental data from Wong [3] and from in-house track- terrain interaction test rig showing good identification accuracy and fast execution speed. It is also shown to be relatively robust to initial condition. The identified Soil Parameters are, in turn, used to predict the tractive forces showing good agreement with all the experimental data. The technique presented in this paper is general and can be applied to any tracked vehicle.
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Soil Parameter identification for wheel-terrain interaction dynamics and traversability prediction
International Journal of Automation and Computing, 2006Co-Authors: Suksun Hutangkabodee, Yahya Zweiri, Lakmal Seneviratne, Kaspar AlthoeferAbstract:This paper presents a novel technique for identifying Soil Parameters for a wheeled vehicle traversing unknown terrain. The identified Soil Parameters are required for predicting vehicle drawbar pull and wheel drive torque, which in turn can be used for traversability prediction, traction control, and performance optimization of a wheeled vehicle on unknown terrain. The proposed technique is based on the Newton Raphson method. An approximated form of a wheel-Soil interaction model based on Composite Simpson’s Rule is employed for this purpose. The key Soil Parameters to be identified are internal friction angle, shear deformation modulus, and lumped pressure-sinkage coefficient. The fourth Parameter, cohesion, is not too relevant to vehicle drawbar pull, and is assigned an average value during the identification process. Identified Parameters are compared with known values, and shown to be in agreement. The identification method is relatively fast and robust. The identified Soil Parameters can effectively be used to predict drawbar pull and wheel drive torque with good accuracy. The use of identified Soil Parameters to design a traversability criterion for wheeled vehicles traversing unknown terrain is presented.
Yahya Zweiri - One of the best experts on this subject based on the ideXlab platform.
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Hybrid Soil Parameter Measurement and Estimation Scheme for Excavation Automation
IEEE Transactions on Instrumentation and Measurement, 2009Co-Authors: Kaspar Althoefer, Yahya Zweiri, Choo Par Tan, Lakmal SeneviratneAbstract:Real-time measurement and estimation of Soil-tool interaction dynamics are key tools for the development of automated excavation systems. Automating a complex operation such as excavation is a challenging task that strongly hinges on a good estimate of Soil properties as well as the interaction forces between the excavator tool and the environment. This paper presents a method for identifying all the unknown Soil Parameters (density, tool-Soil friction angle, Soil-Soil friction angle, and cohesion) required for predicting the interaction forces in real time using a novel hybrid Soil model. The hybrid model consists of the Mohr-Coulomb (M-C) Soil model and the Chen and Liu upper bound (CLUB) Soil model. A switching mode is utilized to select the most appropriate Soil model to compute the failure forces that depend on the position of the excavator bucket. The Newton-Raphson method (NRM) is adapted and is used to identify the Soil Parameters by minimizing the error between the measured forces and the forces computed by the hybrid Soil model. The experimental results presented demonstrate that the proposed estimation scheme is accurate when compared to measured Soil Parameters. The Newton-Raphson estimation method is applied iteratively to efficiently and robustly identify the Parameters. The conducted comparative study shows that the proposed method is a promising way for the on-line Soil-tool interaction force identification of an automated excavator in dynamic and potentially hazardous environments, outperforming other estimation methods including the least squares method (LSM).
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Soil Parameter Identification and Driving Force Prediction for Wheel-Terrain Interaction
International Journal of Advanced Robotic Systems, 2008Co-Authors: Suksun Hutangkabodee, Yahya Zweiri, Lakmal Seneviratne, Kaspar AlthoeferAbstract:This paper considers wheeled vehicles traversing unknown terrain, and proposes an approach for identifying the unknown Soil Parameters required for vehicle driving force prediction (drawbar pull prediction). The predicted drawbar pull can potentially be employed for traversability prediction, traction control, and trajectory following which, in turn, improve overall performance of off-road wheeled vehicles. The proposed algorithm uses an approximated form of the wheel-terrain interaction model and the Generalized Newton Raphson method to identify terrain Parameters in real-time. With few measurements of wheel slip, i, vehicle sinkage, z, and drawbar pull, DP, samples, the algorithm is capable of identifying all the Soil Parameters required to predict vehicle driving forces over an entire range of wheel slip. The algorithm is validated with experimental data from a wheel-terrain interaction test rig. The identified Soil Parameters are used to predict the drawbar pull with good accuracy. The technique presented in this paper can be applied to any vehicle with rigid wheels or deformable wheels with relatively high inflation pressure, to predict driving forces in unknown environments.
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MODEL-BASED Soil Parameter IDENTIFICATION FOR WHEEL-TERRAIN INTERACTION DYNAMICS
IFAC Proceedings Volumes, 2007Co-Authors: Suksun Hutangkabodee, Yahya Zweiri, Lakmal Seneviratne, Kaspar AlthoeferAbstract:Abstract This paper presents an algorithm for identifying Soil Parameters for wheel-terrain interaction dynamics. The Soil Parameters are useful for traversability prediction, traction control, and performance optimization of a wheeled vehicle traveling on unknown terrain. The Composite Simpson's Rule (CSR) is employed to approximate integrals of the full wheel-terrain interaction dynamic model. This is to facilitate the implementation of Soil Parameter identification on this model and allow fast identification speed. The 2-stage iterative Newton Raphson (NR) method is used for Soil Parameter identification. Simulation results show successful identification of a complete set of Soil Parameters with relatively fast speed. The approach in this paper has great potential to be applied for real off-road wheeled vehicle.
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IROS - Validation of Soil Parameter identification for track-terrain interaction Dynamics
2007 IEEE RSJ International Conference on Intelligent Robots and Systems, 2007Co-Authors: Suksun Hutangkabodee, Yahya Zweiri, Lakmal Seneviratne, Kaspar AlthoeferAbstract:This paper considers a tracked vehicle traversing unknown terrain, and proposes an approach based on the Generalized Newton Raphson (GNR) method for identifying all the unknown Soil Parameters required for tractive force prediction. For the first time, the methodology, based on measurements of track slip, i, and tractive force, F, to find unknown Soil Parameters is developed. The tractive force is the force generated by a tracked vehicle to drive itself forwards. This tractive force depends to a large extent on certain Soil Parameters, namely Soil cohesion (c), Soil internal friction angle (phi), and Soil shear deformation modulus (K). Accurately identifying Parameters of the Soil on which a tracked vehicle is moving will potentially lead to accurate traversability prediction, effective traction control, and precise trajectory tracking. The Soil Parameter identification algorithm is validated with the experimental data from Wong [3] and from in-house track- terrain interaction test rig showing good identification accuracy and fast execution speed. It is also shown to be relatively robust to initial condition. The identified Soil Parameters are, in turn, used to predict the tractive forces showing good agreement with all the experimental data. The technique presented in this paper is general and can be applied to any tracked vehicle.
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Soil Parameter identification for wheel-terrain interaction dynamics and traversability prediction
International Journal of Automation and Computing, 2006Co-Authors: Suksun Hutangkabodee, Yahya Zweiri, Lakmal Seneviratne, Kaspar AlthoeferAbstract:This paper presents a novel technique for identifying Soil Parameters for a wheeled vehicle traversing unknown terrain. The identified Soil Parameters are required for predicting vehicle drawbar pull and wheel drive torque, which in turn can be used for traversability prediction, traction control, and performance optimization of a wheeled vehicle on unknown terrain. The proposed technique is based on the Newton Raphson method. An approximated form of a wheel-Soil interaction model based on Composite Simpson’s Rule is employed for this purpose. The key Soil Parameters to be identified are internal friction angle, shear deformation modulus, and lumped pressure-sinkage coefficient. The fourth Parameter, cohesion, is not too relevant to vehicle drawbar pull, and is assigned an average value during the identification process. Identified Parameters are compared with known values, and shown to be in agreement. The identification method is relatively fast and robust. The identified Soil Parameters can effectively be used to predict drawbar pull and wheel drive torque with good accuracy. The use of identified Soil Parameters to design a traversability criterion for wheeled vehicles traversing unknown terrain is presented.
Suksun Hutangkabodee - One of the best experts on this subject based on the ideXlab platform.
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Soil Parameter Identification and Driving Force Prediction for Wheel-Terrain Interaction
International Journal of Advanced Robotic Systems, 2008Co-Authors: Suksun Hutangkabodee, Yahya Zweiri, Lakmal Seneviratne, Kaspar AlthoeferAbstract:This paper considers wheeled vehicles traversing unknown terrain, and proposes an approach for identifying the unknown Soil Parameters required for vehicle driving force prediction (drawbar pull prediction). The predicted drawbar pull can potentially be employed for traversability prediction, traction control, and trajectory following which, in turn, improve overall performance of off-road wheeled vehicles. The proposed algorithm uses an approximated form of the wheel-terrain interaction model and the Generalized Newton Raphson method to identify terrain Parameters in real-time. With few measurements of wheel slip, i, vehicle sinkage, z, and drawbar pull, DP, samples, the algorithm is capable of identifying all the Soil Parameters required to predict vehicle driving forces over an entire range of wheel slip. The algorithm is validated with experimental data from a wheel-terrain interaction test rig. The identified Soil Parameters are used to predict the drawbar pull with good accuracy. The technique presented in this paper can be applied to any vehicle with rigid wheels or deformable wheels with relatively high inflation pressure, to predict driving forces in unknown environments.
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IROS - Validation of Soil Parameter identification for track-terrain interaction Dynamics
2007 IEEE RSJ International Conference on Intelligent Robots and Systems, 2007Co-Authors: Suksun Hutangkabodee, Yahya Zweiri, Lakmal Seneviratne, Kaspar AlthoeferAbstract:This paper considers a tracked vehicle traversing unknown terrain, and proposes an approach based on the Generalized Newton Raphson (GNR) method for identifying all the unknown Soil Parameters required for tractive force prediction. For the first time, the methodology, based on measurements of track slip, i, and tractive force, F, to find unknown Soil Parameters is developed. The tractive force is the force generated by a tracked vehicle to drive itself forwards. This tractive force depends to a large extent on certain Soil Parameters, namely Soil cohesion (c), Soil internal friction angle (phi), and Soil shear deformation modulus (K). Accurately identifying Parameters of the Soil on which a tracked vehicle is moving will potentially lead to accurate traversability prediction, effective traction control, and precise trajectory tracking. The Soil Parameter identification algorithm is validated with the experimental data from Wong [3] and from in-house track- terrain interaction test rig showing good identification accuracy and fast execution speed. It is also shown to be relatively robust to initial condition. The identified Soil Parameters are, in turn, used to predict the tractive forces showing good agreement with all the experimental data. The technique presented in this paper is general and can be applied to any tracked vehicle.
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MODEL-BASED Soil Parameter IDENTIFICATION FOR WHEEL-TERRAIN INTERACTION DYNAMICS
IFAC Proceedings Volumes, 2007Co-Authors: Suksun Hutangkabodee, Yahya Zweiri, Lakmal Seneviratne, Kaspar AlthoeferAbstract:Abstract This paper presents an algorithm for identifying Soil Parameters for wheel-terrain interaction dynamics. The Soil Parameters are useful for traversability prediction, traction control, and performance optimization of a wheeled vehicle traveling on unknown terrain. The Composite Simpson's Rule (CSR) is employed to approximate integrals of the full wheel-terrain interaction dynamic model. This is to facilitate the implementation of Soil Parameter identification on this model and allow fast identification speed. The 2-stage iterative Newton Raphson (NR) method is used for Soil Parameter identification. Simulation results show successful identification of a complete set of Soil Parameters with relatively fast speed. The approach in this paper has great potential to be applied for real off-road wheeled vehicle.
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Soil Parameter identification for wheel-terrain interaction dynamics and traversability prediction
International Journal of Automation and Computing, 2006Co-Authors: Suksun Hutangkabodee, Yahya Zweiri, Lakmal Seneviratne, Kaspar AlthoeferAbstract:This paper presents a novel technique for identifying Soil Parameters for a wheeled vehicle traversing unknown terrain. The identified Soil Parameters are required for predicting vehicle drawbar pull and wheel drive torque, which in turn can be used for traversability prediction, traction control, and performance optimization of a wheeled vehicle on unknown terrain. The proposed technique is based on the Newton Raphson method. An approximated form of a wheel-Soil interaction model based on Composite Simpson’s Rule is employed for this purpose. The key Soil Parameters to be identified are internal friction angle, shear deformation modulus, and lumped pressure-sinkage coefficient. The fourth Parameter, cohesion, is not too relevant to vehicle drawbar pull, and is assigned an average value during the identification process. Identified Parameters are compared with known values, and shown to be in agreement. The identification method is relatively fast and robust. The identified Soil Parameters can effectively be used to predict drawbar pull and wheel drive torque with good accuracy. The use of identified Soil Parameters to design a traversability criterion for wheeled vehicles traversing unknown terrain is presented.
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FSR - Multi-solution problem for track-terrain interaction dynamics and lumped Soil Parameter identification
Springer Tracts in Advanced Robotics, 1Co-Authors: Suksun Hutangkabodee, Yahya Zweiri, Lakmal Seneviratne, K. AlthoAbstract:A technique for identifying lumped Soil Parameters on-line while traversing with a tracked unmanned ground vehicle (UGV) on an unknown terrain is presented. This paper shows the multi-solution problem when identification of Soil Parameters — cohesion (c), shear deformation modulus (φ), and shear deformation modulus (K) are to be attempted using the track-terrain interaction dynamics model. The initiation of the idea of lumping the cohesion and internal friction angle terms and treating them as a single Parameter to solve this problem is presented. The technique used for lumped Soil Parameter identification is based on the Newton Raphson method. This method is proved to be very effective in terms of prediction accuracy, computational speed, and robustness to initial conditions and noise. These identified lumped Soil Parameters can be used to increase the autonomy of a tracked UGV. The technique presented in this paper is general and can be applied to any tracked UGV.
Chao Luo - One of the best experts on this subject based on the ideXlab platform.
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A Novel Method of Soil Parameter Identification and Force Prediction for Automatic Excavation
IEEE Access, 2020Co-Authors: Yuming Zhao, Jian Wang, Yi Zhang, Chao LuoAbstract:A shortage of Soil property Parameters is preventing the automatic excavation system from being used in a digging operation adaptively like an operator. This study proposes a new method of Soil Parameter identification and digging resistance prediction that classifies the entire excavation process into three parts: penetration, cutting, and loading. A fuzzy estimation strategy is introduced into penetration to estimate the property Parameters of the Soil depending on the peak value and the average value of Soil resistance. Furthermore, an improved FEE model was used to describe the Soil–tool interaction and predict the Soil resistive forces. The traditional way of predicting after identifying, which needs at least two excavations, can hardly equal this method in flexibility and real-time functionality. The proposed method can identify Soil Parameters and predict digging forces on one excavation, which is more adapt to Soil Parameter variation. The experimental results show that the proposed method can identify precisely and predict effectively.
