Static output feedback stabilization of T-S fuzzy active suspension systems

Jamal Mrazgua, Redouane Chaibi, El Houssaine Tissir, Mohamed Ouahi

Journal of Terramechanics, Volume 97, 2021, Pages 19-27, ISSN 0022-4898

https://doi.org/10.1016/j.jterra.2021.05.001.(https://www.sciencedirect.com/science/article/pii/S0022489821000422)

Abstract: This paper investigates, a new methodology for non-linear active suspension systems (ASS) with static output feedback (SOF) control. The nonlinear ASS is represented by a Takagi–Sugeno fuzzy control ASS in continuous time. A SOF control presented by a family of linear matrix inequalities (LMIs) guarantees the asymptotic stability and ensures certain an H∞ performance level. This methodology motivated this research, where in the context of active suspension (AS) systems, the guaranteed performance correspond to ride comfort in the presence of road disturbances. Thus, a controller is developed for a quarter-car model with active suspension. The Simulated results with root-mean-square (RMS) values illustrate the effectiveness of the proposed approach.

Keywords: Static output feedback (SOF) control; Takagi–Sugeno fuzzy systems; H∞ control; Active suspension system; root-mean-square (RMS); Linear Matrix Inequality (LMI)

Exploring cold regions autonomous operations

Michael Parker, Brian Quinn, Jordan Bates, Taylor Hodgdon, Mark Bodie, Sally Shoop, Alex Stott

Journal of Terramechanics, Volume 96, 2021, Pages 159-165, ISSN 0022-4898

https://doi.org/10.1016/j.jterra.2021.03.003.(https://www.sciencedirect.com/science/article/pii/S0022489821000240)

Abstract: The US Army must update its vehicle fleet to be better equipped for potential future military conflicts in northern climates (US Army, 2017). This process involves considering manned, optionally manned, and unmanned vehicles as viable options in the future. Optionally manned and unmanned vehicles in the armed forces have substantial benefits because they can operate without direct driver input or are able to perform missions deemed too dangerous for troops. Optionally manned vehicles allow the driver to shift some, or all, focus away from the task of driving the vehicle. In some cases, these autonomous vehicles may perform better than a human driver by rapidly sensing and reacting to terrain changes. Onboard sensing and decision making are equally applicable to both fully autonomous and teleoperated vehicles. This work will focus on the terrain sensing, waypoint navigation, and teleoperation potential of an optionally manned or unmanned vehicle. Results from a vehicle demonstration on two different terrain conditions will provide the basis for additional terrain sensing and autonomous vehicle development work in the coming year.

Keywords: Snow; Autonomous; Robotics; Terrain sensing; Mobility

A novel optimal path-planning and following algorithm for wheeled robots on deformable terrains

Hamid Taghavifar, Subhash Rakheja, Giulio Reina

Journal of Terramechanics, Volume 96, 2021, Pages 147-157, ISSN 0022-4898

https://doi.org/10.1016/j.jterra.2020.12.001.(https://www.sciencedirect.com/science/article/pii/S0022489820301099)

Abstract: An immense body of research has focused on path-planning and following of wheeled robots in unstructured surfaces. Nonholonomic robots traveling over deformable terrains together with complex operating conditions, however, pose further challenges in terms of a higher demand for robustness and optimality. In this paper, a Chaos-enhanced Accelerated Particle Swarm Optimization (CAPSO) algorithm is employed for planning an optimal path of a wheeled robot, so as to ensure shortest path from the starting point to the target location together with safety through guaranteed avoidance of collisions with static and dynamic obstacles. The fundamental terramechanics concepts are employed to derive essential forces and moments acting on the wheeled robot. Subsequently, a kineto-dynamic model of the robot is developed for designing a novel robust control algorithm based on an exponential-integral-sliding mode (EISMC) scheme and a RBF-NN approximator. The results revealed that the proposed algorithm is responsive and robust to withstand adverse effects of structured and unstructured uncertainties by using the designed adaptation law according to the Lyapunov stability theorem. The effectiveness of the proposed algorithm is also validated against several reported frameworks.

Keywords: Terramechancis; Path-planning; PSO; Terrain; Artificial Intelligence

Characterizing terrain image classification difficulties through reduced-dimension class convex hull analysis

Shawn McGrory, P. Michael Furlong, Krzysztof Skonieczny

Journal of Terramechanics, Volume 96, 2021, Pages 133-145, ISSN 0022-4898

https://doi.org/10.1016/j.jterra.2020.12.003.(https://www.sciencedirect.com/science/article/pii/S0022489820301105)

Abstract: The classification of natural terrain ahead of an autonomous vehicle can help it make decisions regarding the traversability of various paths, but remains an open research problem. Despite the explosion in popularity of deep learning networks,there is still little work available on informed neural network design procedures for specific tasks such as terrain image classification, save through performance measures. A related problem is understanding features of a dataset that lead to difficulties in separating classes of images from one another. This research proposes an algorithm and accompanying analytical procedure to characterize such image classification difficulties; identifying what makes some images easily distinguishable as their class and what makes others readily confused with other classes. This is achieved by learning reduced-dimensionality representations of the input data, constructing a convex hull of class members in the reduced dimensionality representation, then examining between-class overlap within each space, incrementally increasing the dimensionality until overlap is eliminated. Summarizing the between-class overlap statistics reveals trends and anomalies that can be linked back to visual features, characteristic of the original data. Case studies are presented of insights identified through selected example analyses: relative intensities of terrain classes from images taken by Mars rovers, and the impact of color gradients in separating sand from bedrock in color images of terrain. Such insights are discussed as steps toward a more directed approach to designing neural networks for image classification.

Keywords: Terrain classification; Image difficulty; Dataset analysis

Recurrent and convolutional neural networks for deep terrain classification by autonomous robots

Fabio Vulpi, Annalisa Milella, Roberto Marani, Giulio Reina

Journal of Terramechanics, Volume 96, 2021, Pages 119-131, ISSN 0022-4898

https://doi.org/10.1016/j.jterra.2020.12.002.(https://www.sciencedirect.com/science/article/pii/S0022489820301117)

Abstract: The future challenge for field robots is to increase the level of autonomy towards long distance (>1 km) and duration (>1h) applications. One of the key technologies is the ability to accurately estimate the properties of the traversed terrain to optimize onboard control strategies and energy efficient path-planning, ensuring safety and avoiding possible immobilization conditions that would lead to mission failure. Two main hypotheses are put forward in this research. The first hypothesis is that terrain can be effectively detected by relying exclusively on the measurement of quantities that pertain to the robot-ground interaction, i.e., on proprioceptive signals. Therefore, no visual or depth information is required. Then, artificial deep neural networks can provide an accurate and robust solution to the classification problem of different terrain types. Under these hypotheses, sensory signals are classified as time series directly by a Recurrent Neural Network or by a Convolutional Neural Network in the form of higher-level features or spectrograms resulting from additional processing. In both cases, results obtained from real experiments show comparable or better performance when contrasted with standard Support Vector Machine with the additional advantage of not requiring an a priori definition of the feature space.

Keywords: Autonomous robots; Vehicle-terrain interaction; Terrain classification; Deep-learning

A method for predicting the internal motion resistance of rubber-tracked undercarriages, Pt. 1. A review of the state-of-the-art methods for modeling the internal resistance of tracked vehicles

Piotr A. Dudziński, Jakub Chołodowski

Journal of Terramechanics, Volume 96, 2021, Pages 81-100, ISSN 0022-4898

https://doi.org/10.1016/j.jterra.2021.02.006.(https://www.sciencedirect.com/science/article/pii/S0022489821000173)

Abstract: This article summarizes the known methods for calculating the internal resistance of tracked undercarriages. The values of the coefficient of internal resistance for sample tracked vehicles are available in the literature and presented in this paper. Although they are suitable for simple computations, they cannot be used to optimize the energy efficiency of new generation tracked undercarriages. This problem might be solved by the models where every phenomenon leading to energy dissipation during vehicle motion is described by a separate submodel as a function of vehicle speed, track tension, undercarriage layout, design features of the undercarriage components, etc. This kind of model is still missing for vehicles with conventional rubber tracks. The article presents multiple state-of-the-art models describing rolling resistance of road wheels, bending resistance of rubber belts, etc., including the models of belt conveyors resistance. A vast majority of the phenomena discussed herein are described by several incompatible models whose parameters have not yet been determined for conventional rubber tracks. Consequently, in the second and the third part of the article, the authors have undertaken a theoretical and experimental studies on the methods for calculating and optimizing the internal motion resistance of vehicles with conventional rubber tracks.

Keywords: Tracked undercarriage; Rubber track; Link track; Energy efficiency; Internal motion resistance; Review

A method for predicting the internal motion resistance of rubber-tracked undercarriages, Pt. 2. A research on the motion resistance of road wheels

Jakub Chołodowski, Piotr A. Dudziński

Journal of Terramechanics, Volume 96, 2021, Pages 101-115, ISSN 0022-4898

https://doi.org/10.1016/j.jterra.2021.02.005.(https://www.sciencedirect.com/science/article/pii/S0022489821000185)

Abstract: In spite of an increasing number of rubber-tracked crawlers, the literature provides few guidelines and calculation models suitable for minimizing their internal motion resistance. This article presents a model where the internal resistance of double-flanged road wheels for rubber-tracked vehicles is calculated as a sum of the losses resulting from the indentation of the wheels into the track surface and friction of the wheels against the track guide lugs. The model allows for vertical and lateral load of the wheels, the non-uniform distribution of the wheel pressure on the track, and the relationship between the friction coefficient and normal reaction force in the interface between the wheel and track guide lugs. The model has been verified by experiments. According to the results of model computations and experiments discussed in the article, the internal losses of a given rubber-tracked undercarriage might be reduced if: the road wheels are covered with a material that exhibits low friction coefficient and mechanical hysteresis, the vehicle suspension system features oscillating bogie wheels, the undercarriage is fitted with the largest possible number of road wheels, and the vehicle weight is evenly distributed to all of the road wheels.

Keywords: Tracked undercarriage; Rubber track; Internal motion resistance; Road wheels; Rubber friction; Rubber hysteresis; Indentation losses

Next-generation NATO reference mobility model complex terramechanics – Part 2: Requirements and prototype

Tamer M. Wasfy, Paramsothy Jayakumar

Journal of Terramechanics, Volume 96, 2021, Pages 59-79, ISSN 0022-4898

https://doi.org/10.1016/j.jterra.2021.02.007.(https://www.sciencedirect.com/science/article/pii/S0022489821000227)

Abstract: In part 2 of this paper, the Complex Terramechanics (CT) software tools requirements recommended by the NATO research task group RTG-248 are presented along with example simulations from a CT prototype software tool which attempts to satisfy the requirements.

Keywords: Terramechanics; Ground Vehicle Mobility; Vehicle Dynamics; Discrete Element Method; Smooth Particle Hydrodynamics; Finite Element Method; Multibody Dynamics; Soft Soil; Vegetation Models; Pneumatic Tires; Tracked Vehicles

Next-generation NATO reference mobility model complex terramechanics – Part 1: Definition and literature review

Tamer Wasfy, Paramsothy Jayakumar

Journal of Terramechanics, Volume 96, 2021, Pages 45-57, ISSN 0022-4898

https://doi.org/10.1016/j.jterra.2021.02.002.(https://www.sciencedirect.com/science/article/pii/S0022489821000148)

Abstract: The US army along with NATO member and partner nations’ militaries need an accurate software tool for predicting ground vehicle mobility (such as speed-made-good and fuel-consumption) on world-wide terrains where military vehicles may be required to operate. Currently, the NATO Reference Mobility Model (NRMM) is the only NATO recognized tool for assessing ground vehicle mobility. NRMM was developed from the 1960s to the 1980s and relies on steady-state empirical formulas which may not be accurate for new military ground vehicles. A NATO research task group (RTG-248) was established from 2016 to 2018 to develop the NG-NRMM (next-generation NRMM) software tool requirements and an NG-NRMM prototype which uses high-fidelity “simple” or “complex” terramechanics models for the terrain/soil along with modern 3D multibody dynamics software tools for modeling the vehicle. NG-NRMM Complex Terramechanics (CT) models are those that utilize full 3D soil models capable of predicting the 3D soil reaction forces on the vehicle surfaces (including tires, tracks, legs, and under body) and the 3D flow and deformation of the soil including both elastic and plastic deformation under any 3D loading condition. In Part 1 of this paper, an overview of the full spectrum of terramechanics models from the highest fidelity to the lowest fidelity is presented along with a literature review of CT ground vehicle mobility models.

Keywords: Vehicle mobility model; Discrete element method; Smoothed particle hydrodynamics; Finite element method; Vehicle dynamics; Terramechanics; Computational mechanics; Soft soil model; Tire; Tracked vehicles