Rough Terrain Research Articles

Аналіз впливу на прохідність автобронетанкової техніки конструктивних факторів та методика її збільшення шляхом автоматичного регулювання тиску повітря в шинах коліс

The purpose of the study is to analyze and determine the influence of various factors on the patency of armored vehicles (AMT) operated by the National Guard of Ukraine. Determination of the change in patency during the movement of AMT in different road and climatic conditions. Analysis of static and dynamic loads, which are characteristic of AMT movement over rough terrain, and their impact on the efficiency of the vehicle. The theoretical component of the study was carried out using mathematical modeling of the work process of regulating the air pressure in tires of AMT wheels, using the example of changes in the characteristics of adhesion coefficients with the supporting surface and rolling resistance depending on the change in pressure and load on the tires of the wheels when the AMT is moving off-road. As a result of mathematical modeling, the dependences of air pressure in tires of AMT wheels were obtained, depending on the coefficient of rolling resistance and the load on each of the wheels. Which, in turn, makes it possible to move to a sufficiently complete mathematical modeling of the movement of AMT on off-road, taking into account the reference cross-country ability. In the process of theoretical research, the structural parameters of the impact on the patency of AMT were analyzed quite completely, and a generalized comparative indicator of the patency parameters of vehicles was proposed. When analyzing it, it was found that the AMT tire and, specifically, the change in air pressure in it, have a significant impact on the bearing capacity. The authors propose to use the following evaluation criteria as criteria for assessing the efficiency of the passability when driving on deformable surfaces: a generalized comparative indicator of the parameters of the passability of vehicles. As a result of consideration of which, a significant influence on the patency parameters of the tires and directly the air pressure in them was established. Regulation and change of the air pressure in the tires of the wheels depending on the change in the coefficient of rolling resistance as well as the change in the load on each wheel, which are the ratio of the work that is spent on the traction force when overcoming different degrees of heaviness of the resistance of the supporting surfaces with the effect of deformation. And the load that falls on each of the AMT wheels allowed us to draw conclusions about the improvement of the regular tire pressure regulation system (RPRS) of the wheels.

On the analysis and control of a bipedal legged locomotion model via partial feedback linearization

In this study, we introduce a new model for bipedal locomotion that enhances the classical spring-loaded inverted pendulum (SLIP) model. Our proposed model incorporates a damping term in the leg spring, a linear actuator serially interconnected to the leg, and a rotary actuator affixed to the hip. The distinct feature of this new model is its ability to overcome the non-integrability challenge inherent in the conventional SLIP models through the application of partial feedback linearization. By leveraging these actuators, our model enhances the stability and robustness of the locomotion mechanism, particularly when navigating across varied terrain profiles. To validate the effectiveness and practicality of this model, we conducted detailed simulation studies, benchmarking its performance against other recent models outlined in the literature. Our findings suggest that the redundancy in actuation introduced by our model significantly facilitates both open-loop and closed-loop walking gait, showcasing promising potential for the future of bipedal locomotion, especially for bio-inspired robotics applications in outdoor and rough terrains.

Smart insect-computer hybrid robots empowered with enhanced obstacle avoidance capabilities using onboard monocular camera

Insect-computer hybrid robots are receiving increasing attention as a potential alternative to small artificial robots due to their superior locomotion capabilities and low manufacturing costs. Controlling insect-computer hybrid robots to travel through terrain littered with complex obstacles of various shapes and sizes is still challenging. While insects can inherently deal with certain obstacles by using their antennae to detect and avoid obstacles, this ability is limited and can be interfered with by control signals when performing navigation tasks, ultimately leading to the robot being trapped in a specific place and having difficulty escaping. Hybrid robots need to add additional sensors to provide accurate perception and early warning of the external environment to avoid obstacles before getting trapped, ensuring smooth navigation tasks in rough terrain. However, due to insects’ tiny size and limited load capacity, hybrid robots are very limited in the sensors they can carry. A monocular camera is suitable for insect-computer hybrid robots because of its small size, low power consumption, and robust information acquisition capabilities. This paper proposes a navigation algorithm with an integrated obstacle avoidance module using a monocular camera for the insect-computer hybrid robot. The monocular cameras equipped with a monocular depth estimation algorithm based on deep learning can produce depth maps of environmental obstacles. The navigation algorithm generates control commands that can drive the hybrid robot away from obstacles according to the distribution of obstacle distances in the depth map. To ensure the performance of the monocular depth estimation model when applied to insect-computer hybrid robotics scenarios, we collected the first dataset from the viewpoint of a small robot for model training. In addition, we propose a simple but effective depth map processing method to obtain obstacle avoidance commands based on the weighted sum method. The success rate of the navigation experiment is significantly improved from 6.7% to 73.3%. Experimental results show that our navigation algorithm can detect obstacles in advance and guide the hybrid robots to avoid them before they get trapped.

Rubber tracks and tyres: a detailed insight into force analysis during obstacle negotiation

The contact of a rubber track or a tyre tread with an edge corner is a process involved in many practical applications, i.e. traversing rough terrain and stair climbing. This paper presents a rigorous analysis along with experimental results to study what happens during the contact between a running gear and an edge corner as well as what forces are exchanged between these two elements by providing also a comparison between existing theoretical methods to assess which one is the most accurate. A testbed has been specifically developed for this research work to analyse the behaviour of a rubber specimen in contact with a step-like obstacle. The experimental results highlighted that the forces exchanged between a belt and a corner edge depend only on the relative inclination between the two sides of the belt and on the coefficient of friction while a pneumatic tyre patch behaves similarly to a belt when it is in contact with a step corner edge.

Three-dimensional MFBD-DEM coupling simulation of flexible wire mesh wheel–soil over lunar rough terrain

Three-dimensional MFBD-DEM coupling simulation of flexible wire mesh wheel–soil over lunar rough terrain

Term structure of equity risk premia in rough terrain: 150 years of the French stock market

We implement a state-space modeling to capture jointly the one-year and infinite horizons equity risk premia (ERPs) over a secular period in France. Expected stock returns are represented by mixing traditional expectation processes, expected variances are from GARCHX models and risk prices are stochastic state variables estimated using the Kalman filter method. Represented by the spread between long-and short-term ERPs, the term structure strongly varies over time exhibiting a dominant downward sloping. Both expected variances and risk prices are highly at play in determining the ERPs term structure, the effects of restraints on borrowing and of international stock market contagion completing the explanation. Overall, our modeling provides rather similar results using US data. We finally show that the French ERPs term structure varies with the economic cycle, the cost of capital and the liquidity preference.

Compliant Motion Control of Wheel-Legged Humanoid Robot on Rough Terrains

Compliant Motion Control of Wheel-Legged Humanoid Robot on Rough Terrains

Evaluation of wind farm performance over heterogeneously rough terrain using large eddy simulation

We evaluate the effect of an abrupt change in the surface aerodynamic roughness height on a wind farm sited on it using the large eddy simulation (LES). Compared to a wind farm sited on a uniformly rough surface, the alteration in aerodynamic surface roughness from a rough to smooth value leads to substantial changes in the first-order and second-order turbulent statistics. Specifically, the rough-to-smooth surface roughness transition leads to an acceleration of the flow downstream of it, which affects the wake recovery and wind farm power production. Different velocity deficits are formulated considering different definitions of “upstream” velocity. The usual deficit, i.e., the difference between the overall wind farm upstream velocities and downstream of a turbine, attains negative values near the ground, rendering it difficult to model within the usual Gaussian radial-shape framework. An alternative definition, i.e., the difference in velocity at the same location with and without turbines on a heterogeneous surface, consistently yields positive values and is amenable to Gaussian shape-based modelling. The power generation decreases as the step change in surface roughness progressively moves into the wind farm. Maximum power is produced when all turbines are placed downstream of the surface roughness jump and minimum power is generated for a homogeneously rough surface when the entire wind farm is placed on the rough surface.

COMPARISON OF MECHANICAL DESIGN AND FLOW ANALYSIS OF QUADRUPED ROBOTS

In recent years, there has been a trend of developing innovative technologies inspired by living creatures in nature. Quadruped robots, in particular, have emerged as walking mobile systems with articulated leg structures that can skilfully perform dynamic movements that wheeled systems are limited to. These robots offer advantages in technical criteria such as manoeuvrability, cross-capability, controllability, terrain adaptability and stability. It is important to note that this evaluation is based on objective technical criteria rather than subjective opinions. This study compares the advantages and disadvantages of quadruped robots to wheeled systems. It highlights that quadruped robots outperform wheeled systems in manoeuvrability, obstacle overcoming, and speed, particularly in rough terrains. The study also suggests that designers of quadruped robots should consider aerodynamic factors, which are often overlooked. Flow analysis using the finite element method is crucial in robot design to enhance aerodynamic performance. This paper aims to comprehensively analyse the flow structure around quadruped robots using Computational Fluid Dynamics and investigate passive flow control methods to reduce the drag coefficient (Cd). The study examines four different robots, and the resulting Cd average percentage calculations are presented. The aerodynamic efficiency of Robot 4 compared to Robot 2 was found to be 95%. Similarly, the aerodynamic efficiency of Robot 3 was determined to be 28% compared to Robot 2. Additionally, it was determined that the aerodynamic efficiency of Robot 2 was 76% compared to Robot 1. These results provide an important comparison to understand the energy efficiency, differences in aerodynamic performance and relative effectiveness of quadruped robots.

Machine Learning-Based Control of Autonomous Vehicles for Solar Panel Cleaning Systems in Agricultural Solar Farms

This paper presents a machine learning (ML)-based approach for the intelligent control of Autonomous Vehicles (AVs) utilized in solar panel cleaning systems, aiming to mitigate challenges arising from uncertainties, disturbances, and dynamic environments. Solar panels, predominantly situated in dedicated lands for solar energy production (e.g., agricultural solar farms), are susceptible to dust and debris accumulation, leading to diminished energy absorption. Instead of labor-intensive manual cleaning, robotic cleaners offer a viable solution. AVs equipped to transport and precisely position these cleaning robots are indispensable for the efficient navigation among solar panel arrays. However, environmental obstacles (e.g., rough terrain), variations in solar panel installation (e.g., height disparities, different angles), and uncertainties (e.g., AV and environmental modeling) may degrade the performance of traditional controllers. In this study, a biologically inspired method based on Brain Emotional Learning (BEL) is developed to tackle the aforementioned challenges. The developed controller is implemented numerically using MATLAB-SIMULINK. The paper concludes with a comparative analysis of the AVs’ performance using both PID and developed controllers across various scenarios, highlighting the efficacy and advantages of the intelligent control approach for AVs deployed in solar panel cleaning systems within agricultural solar farms. Simulation results demonstrate the superior performance of the ML-based controller, showcasing significant improvements over the PID controller.

Decentralized Swarm Control in Communication-Constrained Environments Using a Blended Leader Follower-Artificial Potential Field with Biologically Inspired Interactions

Abstract This paper presents a study of how communication ranges influence the performance of a new decentralized control method for swarms of autonomously navigating ground vehicles that uses a blended leader-follower / artificial potential field approach. While teams of autonomous ground vehicles (AGV) that can navigate autonomously through off-road terrain have a variety of potential uses, it may be difficult to control the team in low-infrastructure environments that lack long-range radio communications capabilities. In this work, we propose a novel decentralized swarm control algorithm that combines the potential-field planning method with the leader-follower control algorithm and biologically-inspired inter-robot interactions to effectively control the navigation of a team of AGV (swarm) through rough terrain using only a single lead vehicle. We use simulated experimentation to demonstrate the robustness of this approach using only point-to-point wireless communication with realistic communication ranges. Furthermore, we analyze the range requirements of the communication network as the number in the swarm increases. We find that wireless communication range must increase as the number of agents in the swarm increases in order to effectively control the swarm. Our analysis showed that mission success decreased by 40% when the communication range was reduced from 100 meters to 200 meters, with the exact reduction also depending on the number of vehicles.

Comparison of in-shoe plantar pressure between Korean combat boots and running shoes

IntroductionCombat boots are special shoes designed for soldiers to wear during activities in rough terrain, such as long marches or military training. Combat boots have been known to cause high...

Estimation of Soil Characteristic Parameters for Electric Mountain Tractor Based on Gauss–Newton Iteration Method

Future field work tasks will require mountain tractors to pass through rough terrain with limited human supervision. The wheel–soil interaction plays a critical role in rugged terrain mobility. In this paper, an algorithm for the estimation of soil characteristic parameters based on the Simpson numerical integration method and Gauss–Newton iteration method is presented. These parameters can be used for passability prediction or in a traction control algorithm to improve tractor mobility and to plan safe operation paths for autonomous navigation systems. To verify the effectiveness of the solving algorithm, different initial values and soils were selected for simulation calculations of soil characteristic parameters such as internal friction angle, settlement index, and the joint parameter of soil cohesion modulus and friction modulus. The results show that the error was kept within 2%, and the calculation time did not exceed 0.84 s, demonstrating high robustness and real-time performance. To test the applicability of the algorithm model, further research was conducted using different wheel parameters of electric mountain tractors under wet clay conditions. The results show that these parameters also have high accuracy and stability with only a few iterations. Thus, the estimation algorithm can meet the requirements of quickly and accurately identifying soil characteristic parameters during tractor operation. A criterion for the passability of wheeled tractors through unknown terrain is proposed, utilizing identified soil parameters.

Remote Sensing and Modeling of the Cryosphere in High Mountain Asia: A Multidisciplinary Review

Over the past decades, the cryosphere has changed significantly in High Mountain Asia (HMA), leading to multiple natural hazards such as rock–ice avalanches, glacier collapse, debris flows, landslides, and glacial lake outburst floods (GLOFs). Monitoring cryosphere change and evaluating its hydrological effects are essential for studying climate change, the hydrological cycle, water resource management, and natural disaster mitigation and prevention. However, knowledge gaps, data uncertainties, and other substantial challenges limit comprehensive research in climate–cryosphere–hydrology–hazard systems. To address this, we provide an up-to-date, comprehensive, multidisciplinary review of remote sensing techniques in cryosphere studies, demonstrating primary methodologies for delineating glaciers and measuring geodetic glacier mass balance change, glacier thickness, glacier motion or ice velocity, snow extent and water equivalent, frozen ground or frozen soil, lake ice, and glacier-related hazards. The principal results and data achievements are summarized, including URL links for available products and related data platforms. We then describe the main challenges for cryosphere monitoring using satellite-based datasets. Among these challenges, the most significant limitations in accurate data inversion from remotely sensed data are attributed to the high uncertainties and inconsistent estimations due to rough terrain, the various techniques employed, data variability across the same regions (e.g., glacier mass balance change, snow depth retrieval, and the active layer thickness of frozen ground), and poor-quality optical images due to cloudy weather. The paucity of ground observations and validations with few long-term, continuous datasets also limits the utilization of satellite-based cryosphere studies and large-scale hydrological models. Lastly, we address potential breakthroughs in future studies, i.e., (1) outlining debris-covered glacier margins explicitly involving glacier areas in rough mountain shadows, (2) developing highly accurate snow depth retrieval methods by establishing a microwave emission model of snowpack in mountainous regions, (3) advancing techniques for subsurface complex freeze–thaw process observations from space, (4) filling knowledge gaps on scattering mechanisms varying with surface features (e.g., lake ice thickness and varying snow features on lake ice), and (5) improving and cross-verifying the data retrieval accuracy by combining different remote sensing techniques and physical models using machine learning methods and assimilation of multiple high-temporal-resolution datasets from multiple platforms. This comprehensive, multidisciplinary review highlights cryospheric studies incorporating spaceborne observations and hydrological models from diversified techniques/methodologies (e.g., multi-spectral optical data with thermal bands, SAR, InSAR, passive microwave, and altimetry), providing a valuable reference for what scientists have achieved in cryosphere change research and its hydrological effects on the Third Pole.

Simplified Strategy for Trajectory Tracking Application of a Passive Suspension Rover-Type Mobile Robot

In the present work, based on an approximate modelling of a rover-type robot and a proportional control law, a simplified trajectory tracking strategy for a passive suspension rover-type mobile robot was developed. This strategy achieves trajectory tracking and the autonomous displacement of a rover, of which its configuration involves complex kinematics and dynamics. All these lineaments reduce the complexity of the analysis, the number of electronic components to implement, the computational requirements and the energy consumption. The robotic system used is based on the Shrimp rover, which is a robot with a passive suspension that is capable of carrying out displacements over rough terrain. The tests were performed using numerical simulations with different desired trajectories, and also using experimental tests using a passive suspension rover-type mobile robot.

Transport and deposition of the 39.8 ka Campanian Ignimbrite large-scale pyroclastic density currents (Italy)

Processes of transport and deposition of large-scale pyroclastic density currents, which transport 102−103 km3 of material, are still poorly understood. The volcanic explosivity index (VEI) 7 Campanian Ignimbrite pyroclastic density current is one such poorly understood case that is subject to lively scientific debates about its origin and processes. The Campanian Ignimbrite is related to the 39.8 ka eruption that formed the partly submarine and active Campi Flegrei caldera in Italy. The related pyroclastic density current extended over an area of more than 6000 km2 on land around the caldera, with a final runout of ∼75 km, and surmounted mountain ridges higher than 1000 m above sea level, even at distal reaches. We used physical characteristics of the ignimbrite to understand the transport and emplacement dynamics of the Campanian Ignimbrite pyroclastic density current. Thickness, mass, and clast-size distributions across paleotopography were used to infer that the main transport system was a dilute, density-stratified pyroclastic density current, which was at least 1.5 km thick, which was short-lived (possibly as short as ∼20 min), and which had a mass flow rate of ∼1011 kg/s. At its base, high-concentration undercurrents formed and interacted with the rough paleotopography, laying down a low-aspect-ratio ignimbrite sheet when on flat plains, but generating back-flows from ridges and channeling in paleovalleys. The deposit is predominantly a massive valley-pond ignimbrite facies, even across rough mountain terrains. The absence of veneer facies on steep slopes reflects the near-Newtonian rheology of the undercurrents, interpreted as fluidized dense granular flows. This work advances the interpretations of pyroclastic density current deposits, which do not necessarily directly reflect conditions in the transport system.

Developing an explanatory hypothesis for urban graphic heritage through the observation of physical traces

Urban graphic heritage has recently come to the fore connected to cities’ economic, social, cultural, and environmental development through design. This paper charts the development of urban graphic heritage through a working hypothesis that traces progress from the rough terrain of exploration towards the more nuanced domain of explanation. Graphic heritage is shown to have evolved across several unconnected fields each in need of better designation. At the core of the argument is the importance attached to naming fields to ensure that intellectual histories may be better understood in the service of design research and heritage studies.

Terrain-Vechicle Traction Control Parameters for Tracked-Robot

Abstract: Here we have the systems of :- 1) Advancements in Vehicle Control :- This summary highlights two recent advancements in vehicle control technology which are the enhanced Handling for Passenger Cars: Researchers have developed a novel control system inspired by expert driver techniques. This system optimizes a car's handling during maneuvers requiring simultaneous acceleration and sharp turns. Simulation testing demonstrates significant performance improvements. Improved Unmanned Ground Vehicle (UGV) Operation on Unstructured Terrain: A new approach addresses the challenges of controlling UGVs on rough terrain. The solution combines sensor fusion for terrain characterization, a neural network for terrain classification, and an adaptive control system for real-time stability adjustments. This multi-pronged approach has been validated through successful experimentation on a UGV platform. 2) Enhancing UGV Control with Haptic Feedback :- A separate study explores the use of haptic feedback to improve UGV control in difficult environments. Traditional operator interfaces often lack sufficient sensory information, leading to reduced situational awareness and operational difficulties. This research proposes a haptic tablet interface that provides the operator with a tactile sense of the UGV's traction state. User studies confirm that this approach significantly reduces the time a UGV spends immobilized due to traction loss. These advancements collectively represent significant progress in vehicle control technology, with potential benefits for both manned and unmanned vehicles operating in challenging scenarios

Need for structured conservation inputs: a geospatial approach for habitat prioritization to restrict disturbance regime in the protected areas of the Western Himalayan region

AbstractThe population boom increases land-use requirements, and shrinking regions are available to preserve natural biodiversity. Unfortunately, scientific knowledge does not seem to have and might never get a correct solution to how much territory will be sufficient for satisfying the various biodiversity conservation goals. Prioritization of habitat, primarily for threatened species, is a critical management challenge. Habitat management efforts are gradually being performed over broader geographic regions, emphasizing the necessity of spatial and strategic prioritization. Habitat prioritization is a technique popular for identifying critical areas needing conservation initiatives. To develop a strategic conservation framework, key issues must be identified based on the ground knowledge and present land use status, which are difficult to gather for highly rough terrain areas. Incorporating spatial data sets thus helps to get the desired knowledge of the site on a large scale. This study used a grid-based multicriteria approach to create a spatial conservation management framework for the Rajaji-Corbett landscape (RCL) in Northern India. A grid-based prioritization assessment was conducted to identify priority areas for the RCL, keeping invasive species and fire as significant threats. The indicators or criteria were selected by looking at present vital concerns in the landscape. We took 20 indicators for our multicriteria assessment for landscape prioritization of RCL using 2 × 2 km grids for the evaluation and classified them according to low, medium, and high priority areas using Arc GIS Pro. The results identified 931 sq. km area of this landscape needs immediate management interventions where frequent fire incidents and presence of invasive species are high. RCL is well known for its biodiversity wealth and assemblage of various wildlife populations.

Learning robust autonomous navigation and locomotion for wheeled-legged robots.

Autonomous wheeled-legged robots have the potential to transform logistics systems, improving operational efficiency and adaptability in urban environments. Navigating urban environments, however, poses unique challenges for robots, necessitating innovative solutions for locomotion and navigation. These challenges include the need for adaptive locomotion across varied terrains and the ability to navigate efficiently around complex dynamic obstacles. This work introduces a fully integrated system comprising adaptive locomotion control, mobility-aware local navigation planning, and large-scale path planning within the city. Using model-free reinforcement learning (RL) techniques and privileged learning, we developed a versatile locomotion controller. This controller achieves efficient and robust locomotion over various rough terrains, facilitated by smooth transitions between walking and driving modes. It is tightly integrated with a learned navigation controller through a hierarchical RL framework, enabling effective navigation through challenging terrain and various obstacles at high speed. Our controllers are integrated into a large-scale urban navigation system and validated by autonomous, kilometer-scale navigation missions conducted in Zurich, Switzerland, and Seville, Spain. These missions demonstrate the system's robustness and adaptability, underscoring the importance of integrated control systems in achieving seamless navigation in complex environments. Our findings support the feasibility of wheeled-legged robots and hierarchical RL for autonomous navigation, with implications for last-mile delivery and beyond.