Terrain Obstacles Research Articles

Human-robot collaborative decision method of hexapod robot based on prior knowledge and negotiation strategy

Against the unstructured terrain environment, human drivers often rely on remote operating patterns to accomplish tasks with robots. With the gradual improvement in robot intelligence, a key problem that urgently requires attention is how to bridge cognitive and perceptual differences between human and robot in collaborative decision-making processes. Particularly effective resolution of differences in decision outcomes is crucial. Therefore, this paper utilizes the decision logic process and prior knowledge model of human drivers to form a robot's decision framework, narrowing the cognitive differences between human and robot. Besides, embedding the human-robot-environment features that affect the decision-making process as prior knowledge into the robot's decision-making system can effectively reduce perceptual gaps. Under the same decision framework, a negotiation strategy is proposed for forming a unified decision outcome between human and robot by finding an optimal concession rate. This study utilizes a hexapod robot simulated driving platform to gather experimental data and develop prior knowledge for robots. It also leverages virtual reality equipment and visual augmentation technologies to establish a remote human-robot collaborative decision-making experimental system. The experimental results conducted in complex obstacle terrain demonstrate the effectiveness of adopting the negotiation strategy for human-robot collaborative decision-making. Compared with the individual human or robot decision, the human-robot collaborative decision method significantly enhances robot stability, reduces energy consumption, and minimizes collisions.

Selected issues of hydropneumatic suspension design for a high-speed tracked vehicle.

The effective operation of high-speed tracked vehicles in a combat vehicle configuration depends primarily on the correct identification of the expected operating conditions, already in the design or modernisation phase. This applies to both terrain, meteorological and climatic conditions. This approach considers the most unfavourable driving conditions and the level of dynamic loads acting on the vehicle hull, crew and equipment when making calculations. The dominant source of dynamic loads is movement at high speed on unpaved roads, off-road and when overcoming natural and artificial terrain obstacles of a predetermined or random nature. An important system responsible for the safety and reliability of this type of vehicle is the suspension. An appropriate design of the suspension parameters allows to reduce the negative dynamic loads (research hypothesis). The aim of the research was to develop a preliminary project of an initial suspension element – a hydropneumatic spring for a high-speed tracked vehicle - that allows minimising dynamic loads. The study used both theoretical and empirical research methods, including analysis, synthesis, deduction, induction, comparison and inference. The paper presents selected issues from the suspension design process for a high-speed tracked vehicle equipped with a hydropneumatic suspension. The problem was solved in three stages. In the first stage, the design of the hydropneumatic suspension unit, placed in the control arm, was made and the basic parameters were selected. In the second, the spring characteristic of the hydropneumatic unit was determined on the example of a hypothetical vehicle with assumed tactical and technical parameters. In the third stage, the strength of the control arm structure, under the heaviest load conditions, was checked using the Finite Element Method (FEM). The results obtained showed that the advantages of the hydropneumatic suspension, such as: progressive spring characteristics, improved vibration damping, possibility of introducing a semi-active or active suspension system, modularity of the structure and ease of maintenance and repair, make it more and more widely used in modern high-speed tracked vehicles.

Imprint of a dead-ice environment on lidar imagery—Spatial patterns at the last Scandinavian Ice Sheet margin (N Poland)

There is a unique glacial landscape system in the southern Żarnowiec Moraine Plateau of northern Poland. Here the terrain is characterized by a very high concentration of ridges that are often adjacent to pits, which together constitutes a special type of hummocky topography. The morphological diversity of the study-area ridges and pits was determined from high-resolution lidar imagery that was qualitatively and quantitatively processed, then analysed. Fourteen test fields of spatial dimensions 0.5 km × 0.5 km were selected within the moraine plateau for detailed studies. The results show that the ridges and pits constitute a very dense network of approximately circular, diverse-oriented forms of various sizes, most of which are linked to each other in a step-wise fashion. The pits are surrounded by ridges of various heights and widths that separate one pit from another. A characteristic feature of the studied pits is the orientation of their long axis. Form-based morphological analysis revealed that their genesis was associated with a glacial rather than a periglacial environment. It is believed that ridge orientation reflects a spatial pattern related to ice crevasses. A moraine plateau constituted a terrain obstacle that acted as a counterslope to the advancing ice sheet. The studied landforms and landscape resulted from the ice sheet's breaking up into dead-ice blocks of various sizes. The very dense system of perpendicular, parallel, and diagonal crevasses was related to ice sheet advance from a northerly direction, resulting in a flow around the obstacle and differences in ice-mass speed.

Power quality validation in micro off-grid daily load using modular differential, LSTM deep, and probability statistics models processing NWP-data

Load corrections with respect to power quality (PQ) after the first pre-estimate of Renewable Energy (RE) power consumption must ensure system-tolerant performance without malfunctions. First, acceptable daily load sequences for the attached equipment are combined and determined according to the RE potential and charge states in accommodation to user needs and normal operation. The main motivation is a consequent day-to-day verification of algorithmically scheduled power consumption tasks in the proposed two-stage optimisation according to the system resources and user needs. Statistical artificial intelligence (AI) is employed, as local atmospheric turbulences with terrain obstacles and unexpected user activity result in various operational states in real microsystems. A new unconventional neurocomputing strategy, called Differential Learning (DfL), was applied in the modelling and prediction of the high dynamical PQ parameters in an experimental RE based system according to input-output training data, without an exact specification of its behaviour. The DfL models were compared with recent deep and machine learning techniques. Prediction models were formed after an initial detection of adequate daily training intervals. The AI models are finally tested to process the complete 24-hour forecast series of related input variables used in learning, to estimate the PQ target output at the corresponding times.

Implementing dog-like quadruped robot turning motion based on key movement joints extraction

For biomimetic robots to be used in the real world, it is very important to have animal-like turning motion. Excellent turning ability can help quadruped robots navigate more complex obstacle terrain. To solve this problem, we conducted the corresponding study by collecting multiple sets of data on different movements of Marinua dogs. First, we extracted the key movement joints of dogs and captured the motion trajectories of the key joints by a reduced order model. Then, on the basis of systematic analysis and mathematical modeling of the experimental dog’s turning motion, we propose an N-step turning strategy to enable the quadruped robot to realize a wide range of multi-angle movements. Meanwhile, based on the extracted joint trajectory and considering the influence of the actual turning speed, radius and stability of the quadruped robot, the more reasonable biomimetic turning trajectory is obtained through optimization. In terms of motion control, we propose a virtual spine control method to realize turning motion, and plan the foothold adjustment according to different speeds. Finally, experiments show that the proposed method can effectively realize multi-angle turning and U-turn and O-circle turning motion of quadruped robot, which proves the effectiveness of the proposed method. At the same time, the biomimetic turning control method proposed in this paper can be easily extended to other biomimetic robot movements inspired by other organisms.

An Integrated Route and Path Planning Strategy for Skid–Steer Mobile Robots in Assisted Harvesting Tasks with Terrain Traversability Constraints

This article presents a combined route and path planning strategy to guide Skid–Steer Mobile Robots (SSMRs) in scheduled harvest tasks within expansive crop rows with complex terrain conditions. The proposed strategy integrates: (i) a global planning algorithm based on the Traveling Salesman Problem under the Capacitated Vehicle Routing approach and Optimization Routing (OR-tools from Google) to prioritize harvesting positions by minimum path length, unexplored harvest points, and vehicle payload capacity; and (ii) a local planning strategy using Informed Rapidly-exploring Random Tree (IRRT*) to coordinate scheduled harvesting points while avoiding low-traction terrain obstacles. The global approach generates an ordered queue of harvesting locations, maximizing the crop yield in a workspace map. In the second stage, the IRRT* planner avoids potential obstacles, including farm layout and slippery terrain. The path planning scheme incorporates a traversability model and a motion model of SSMRs to meet kinematic constraints. Experimental results in a generic fruit orchard demonstrate the effectiveness of the proposed strategy. In particular, the IRRT* algorithm outperformed RRT and RRT* with 96.1% and 97.6% smoother paths, respectively. The IRRT* also showed improved navigation efficiency, avoiding obstacles and slippage zones, making it suitable for precision agriculture.

Cooperative Coverage Path Planning for Multi-Mobile Robots Based on Improved K-Means Clustering and Deep Reinforcement Learning

With the increasing complexity of patrol tasks, the use of deep reinforcement learning for collaborative coverage path planning (CPP) of multi-mobile robots has become a new hotspot. Taking into account the complexity of environmental factors and operational limitations, such as terrain obstacles and the scope of the task area, in order to complete the CPP task better, this paper proposes an improved K-Means clustering algorithm to divide the multi-robot task area. The improved K-Means clustering algorithm improves the selection of the first initial clustering point, which makes the clustering process more reasonable and helps to distribute tasks more evenly. Simultaneously, it introduces deep reinforcement learning with a dueling network structure to better deal with terrain obstacles and improves the reward function to guide the coverage process. The simulation experiments have confirmed the advantages of this method in terms of balanced task assignment, improvement in strategy quality, and enhancement of coverage efficiency. It can reduce path duplication and omission while ensuring coverage quality.

Analysis of the Functionality of a Mobile Network of Sensors in a Construction Project Supervision System Based on Unmanned Aerial Vehicles

This manuscript presents the results of a project related to the construction and testing of selected devices included in a space inspection and worker supervision system. The most important components of this system are a swarm of unmanned aerial vehicles, a docking station for the automatic charging of many drones, monitoring sensors, and user software that integrates all components responsible for mission planning (UAV raids) and measurement data processing. All components were built according to an original solution. The main part of this manuscript is a description of tests used to verify the functionality of a sensor network for monitoring infrastructural elements and moving objects, including people working on a construction project. As part of this research, procedures for testing sensor networks under laboratory and field conditions were developed. The tests performed demonstrated the ability of the MESH network to self-organize depending on the location of the elements in the network. The system that was built ensured the transmission of data from telemetric devices during UAV flights, regardless of the coverage of terrain by other networks, such as Wi-Fi and GSM networks. Data were sent to the end user via a LAN network based on the IP protocol. The maximum range between devices forming the network and the range limitations caused by various terrain obstacles were also determined.

The running gear construction impact on overcoming obstacles by light high-mobility tracked UGV

Rubber tracked running gears are widely used in high-mobility Unmanned Ground Vehicles (UGV) to increase obstacle negotiation possibility in urban and rural terrain. The paper proposes a method of assessing the mobility level of the light UGV‘s tracked running gears in terms of their ability to overcome terrain obstacles. A model of rubber track system was created in the MSC ADAMS environment. A track-ground contact was also modeled, defining the traction force based on the Wong equations. For four different chassis models (rigid construction, bogies solution – rigid and elastically mounted to the frame and rocker-bogie construction), with two track tension variants, the ability to overcome five terrain obstacles was checked, taking into account three different types of soil. The solutions were accessed on the basis of parameters of general efficiency of overcoming obstacles, driving force and slip values, as well as the distribution of track pressures on the ground. The best solutions for each criterion were indicated. The simulation results showed an improvement in the driving properties with the use of elastically suspended elements. The results also emphasized the negative impact of increased track tension on overcoming obstacles and the impact of ground characteristics on the slip values of the running gear.

3D simulation model for IoD-to-vehicles communication in IoD-assisted VANET

Vehicle ad hoc networks (VANETs) have gradually emerged to enhance transportation information, entertainment, safety, and other services. However, such infrastructures have certain limitations, causing intermittent network disconnection. Further, in urban areas, terrain heights act as obstacles and hinder or attenuate transmitted signals. In this study, we propose a dynamic 3D internet of drones collaborative communication approach for efficient VANET-assistance (3DIoDAV) by integrating the IoD network and VANET to support terrestrial communication. We model IoD locations as an optimization problem to optimize the IoD nodes in three-dimensional terrain. Improved particle swarm optimization is used to optimally deploy IoD nodes in 3D terrain for minimizing the number of isolated vehicles. The proposed approach considers the terrain profile influence on communication. Therefore, we propose a 3D propagation model for efficient IoD-to-vehicle (IoD2V) communication in 3D space. Experiments are performed based on the received signal from ground vehicles to examine the performance of the proposed model and the 3DIoDAV approach. Simulation results show different behaviors of IoD nodes in two-dimensional (2D) and 3D scenarios. Comparison with 2D VANET-assisted and IoDAV approaches demonstrates the proposed 3DIoDAV approach’s ability to detect terrain obstacles, which guarantees the dispatching of IoD nodes into the most appropriate locations in 3D space, thereby minimizing the impact of terrain obstacles on communication.

3D path planning problem for fighter aircraft with multiple constraints

AbstractPath planning is a crucial component for ensuring the safety and efficiency of flight missions, especially for fighter aircraft. To enhance the combat effectiveness of fighter aircraft, it is important to consider how to avoid danger sources an terrain obstacles, reduce fuel consumption, and utilize the aircraft's own performance to accomplish the mission objectives. In the modern battlefield environment, the shortest path is not the only criterion for planning, but also other factors such as the threat level to the aircraft, fuel consumption, mission completion time, and minimum turning radius. In this paper, the authors propose a multi‐constraint path planning method for fighter aircraft that incorporates these factors into an improved particle swarm algorithm. The authors transform the constraints of three‐dimensional terrain, threat source, fuel consumption, and mission time into an aggregated fitness function. The authors construct a limit curvature matrix to evaluate the feasibility of the generated path. The authors also introduce an adaptive adjustment strategy based on the activation function for the parameters in the particle swarm algorithm. The weights of each constraint are determined according to the actual demand. The experiment results show that the authors’ method can efficiently plan the optimal path that satisfies the requirements. Compared with other improved particle swarm algorithms, the authors’ method has higher optimal search efficiency and better convergence effect. The authors also provide optimal values for important parameters such as mission energy consumption, mission time, flight speed and others to support the overall mission planning. The authors’ method has a certain practical application value.

The Effects of Structural Characteristics of the Rollator on the Elderly’s Gait Strategies in Various Walking Environments

A rollator, one of the most widely used among walking assistance devices, can assist the elderly with stable walking in their daily lives. In this study, we investigated how the structural characteristics of two types of rollators affect the upper and lower extremity muscle activity and plantar pressure of the elderly in various walking environments. We quantified muscle activity (upper and lower limbs) and plantar pressure (mean force, peak pressure, and contact area) of 11 older adults walking in various environments (flat, obstacle, uneven, and sloped terrain) using two types of rollators. Upper extremity muscle activity was highest in the obstacle terrain and the uneven terrain, and a significant difference was found due to the structural differences of the rollator. Additionally, it was observed that lower extremity muscle activity and plantar pressure patterns appeared in accordance with the gait strategy to maintain stability in an unstable or inclined walking environment. In other words, it was confirmed that the weight of the rollator, the size of the wheel, grip type, and the auxiliary tools had a great effect on the upper and lower extremity muscle activity and plantar pressure of the elderly during walking. From the results of this study, it can be suggested that it is absolutely necessary to consider the biomechanical characteristics of the elderly and the structure of the rollator, which appear differently depending on the walking environment, in the development of walking aids. In the future, more clinical data will be collected, and based on this a rollator that can safely assist the elderly in various walking environments will be developed.

Research on Terrain Mobility of UGV with Hydrostatic Wheel Drive and Slip Control Systems

The article explored the potential for enhancing the off-road mobility of unmanned ground vehicles (UGV) equipped with a hydrostatic drive system. The analysis showed that effectively overcoming rough or soft terrain demands a slip limitation. In the UGVs with hydrostatic drives, flow dividers are used for this purpose. Unfortunately, they have certain drawbacks, such as reduced efficiency due to pressure losses. In order to minimize this phenomenon, an external braking system was used as a new slip control system. Therefore, simulation studies were carried out to assess the new slip control system while overcoming terrain obstacles due to the reduction of energy consumption and improving the mobility of the UGV.

Muscle force-length dynamics during walking over obstacles indicates delayed recovery and a shift towards more 'strut-like' function in birds with proprioceptive deficit.

Recent studies of in vivo muscle function in guinea fowl revealed that distal leg muscles rapidly modulate force and work to stabilize running in uneven terrain. Previous studies focused on running only, and it remains unclear how muscular mechanisms for stability differ between walking and running. Here, we investigated in vivo function of the lateral gastrocnemius (LG) during walking over obstacles. We compared muscle function in birds with intact (iLG) versus self-reinnervated LG (rLG). Self-reinnervation results in proprioceptive feedback deficit due to loss of monosynaptic stretch reflex. We tested the hypothesis that proprioceptive deficit results in decreased modulation of EMG activity in response to obstacle contact, and a delayed obstacle recovery compared with that for iLG. We found that total myoelectric intensity (Etot) of iLG increased by 68% in obstacle strides (S0) compared with level terrain, suggesting a substantial reflex-mediated response. In contrast, Etot of rLG increased by 31% in S0 strides compared with level walking, but also increased by 43% in the first post-obstacle (S+1) stride. In iLG, muscle force and work differed significantly from level walking only in the S0 stride, indicating a single-stride recovery. In rLG, force increased in S0, S+1 and S+2 compared with level walking, indicating three-stride obstacle recovery. Interestingly, rLG showed little variation in work output and shortening velocity in obstacle terrain, indicating a shift towards near-isometric strut-like function. Reinnervated birds also adopted a more crouched posture across level and obstacle terrains compared with intact birds. These findings suggest gait-specific control mechanisms in walking and running.

Optimal Computation Offloading in Collaborative LEO-IoT Enabled MEC: A Multiagent Deep Reinforcement Learning Approach

Recently, Low Earth Orbit (LEO) satellite-based Internet of Things (LEO-IoT) becomes attractive for computation offloading in mobile-edge computing (MEC) since it can overcome terrain obstacles, such as in depopulated villages and disaster sites. However, it is extremely hard to allocate bandwidth and power resources jointly with multiple users and satellites. In this paper, we study offloading in collaborative LEO-IoT where satellites forward data from users to the MEC server, with the goal of making offloading fast and energy efficient. To achieve this goal, we first define the data offloading in collaborative LEO-IoT as an optimization problem with resource constraints. Then we formulate the optimization problem as a Partially Observable Markov Decision Processes (POMDP), which differs from the existing Markov Decision Processes (MDP) work for the offloading scenario. We further propose a novel Multi-Agent Information Broadcasting and Judging (MAIBJ) algorithm to allocate resources in a collaborative manner. Finally, extensive experiments are conducted with various configurations and the results show that MAIBJ can shorten at least 33% of transmission latency and save at least 42% of energy consumption compared with several baseline algorithms.

An Efficient Wireless Propagation Loss Prediction Model Based on 3-D Terrain Features Extracted by Deep Learning

This paper proposes a path loss prediction method based on convolutional neural network (CNN) by extracting features such as terrain obstacles and building distribution. 20 prediction tasks are carried out for different configurations of wave propagation mode, the number and height distribution of buildings and whether there is blocking effect, and a good accuracy is achieved. Meanwhile, the prediction time cost of the CNN and that of ray tracing are comprehensively compared, and in general the CNN has stable and higher computing efficiency. It can be seen that the CNN has greater advantages when dealing with complex environments and multiple propagation mechanisms by processing topographic maps attributed to its better feature extraction and generalization capabilities.

Kinematic modeling and motion characterization of an articulated mobile robot

In response to the outstanding problems of poor motion flexibility and poor terrain adaptability of conventional mobile robots in unstructured environments, a multi-vehicle modular articulated mobile robot capable of adapting to complex and variable obstacle terrain was developed by designing and introducing a flexible articulation device. By constructing a kinematic model of the single body module of the robot, integrating the motion constraint relations among the modules, and establishing the overall robot attitude equations, we propose a robot soft motion control method. Based on the analysis of robot motion characteristics under special obstacle terrain, the relationship model between obstacle parameters and robot pose is constructed, and the correction strategy under unilateral unstable motion state is proposed through the analysis of robot motion state under unilateral unstable condition. Theoretical analysis and experimental results show that the robot has good motion flexibility and complex terrain adaptability.

Adaptive Robust Autonomous Obstacle Traversal Controller for Novel Six-Track Robot

The separate control method of flippers and the movement of the mass center makes the active articulated tracked robot unable to realize higher-level motion and difficult to adapt to rough and complex obstacle terrain. In this paper, a new design method of distributed autonomous obstacle traversal controller for the novel six-track robot is proposed. The controller establishes a unified control framework that includes all degrees of freedom of the robot so that the center of mass tracking error and flipper motion tracking error can converge simultaneously to achieve obstacle traversal independently of specific terrain or tasks. First, the forward kinematics model and differential kinematics model of tracked robot are established to generate 3D motion, including flipper angular velocity and body traction velocity. Then, the differential drive robot model is extended into the differential kinematic model to eliminate the slip effect during obstacle traversal. Finally, the feedback control law of the control system and the optimal solution for the singular position of the robot structure are established. In addition, several simulation experiments and physical prototype experiments in different obstacle terrains are executed. In the virtual simulation experiment, the average trajectory error of the flipper is about 0.029 m. In the physical prototype experiment, compared to the manual remote controller and the prior art controller, the average error norm of the center of mass is reduced by 40.7% and 13.5%, respectively; the maximum slip norm is reduced by 34.6% and 19.9%, respectively; and the obstacle crossing time is reduced by 21.3% and 9.3%, respectively, and they validate the accuracy and effectiveness of the designed controller.

Light‐Driven Folding Liquid Crystal Elastomer Catapult with Improved Morphing Velocity

As a typical behavior of releasing energy in a short time, catapulting is a way for organisms to adapt to complex and changeable environments, such as crossing terrain obstacles and spreading seeds. The essence of catapulting behavior is that plants and animals use their structural characteristics to control elastic strain energy locking, triggering, and releasing under external stimulation. At present, bistable or multistable structures are attractive for optimizing the system energy and improving the performance of liquid crystal elastomer (LCE) soft actuators. However, the overelaborate designation of such structures makes it a great challenge to achieve fast morphing LCE soft actuators. Here, a light‐driven azobenzene liquid crystal elastomer (azo‐LCE) soft actuator mimicking the catapulting behavior of fern sporangia is reported. 2D azo‐LCE monoliths are simply folded to construct V‐shaped structures as 3D catapults, which exhibit higher morphing velocity upon UV light, and the morphing velocity is related to the aspect ratio of films. Furthermore, asymmetric morphing behavior occurs by constructing localized UV irradiation conditions, which greatly improves the morphing velocity. It is envisioned that the origami‐inspired azo‐LCE catapults can be further designed as the throwing component in fast‐morphing soft robots for the remote delivery of microcargoes.

Trajectory optimization of UAV based on Hp-adaptive Radau pseudospectral method

<p style='text-indent:20px;'>Unmanned Aerial Vehicles (UAVs) have been extensively studied to complete the missions in recent years. The UAV trajectory planning is an important area. Different from the commonly used methods based on path search, which are difficult to consider the UAV state and dynamics constraints, so that the planned trajectory cannot be tracked completely. The UAV trajectory planning problem is considered as an optimization problem for research, considering the dynamics constraints of the UAV and the terrain obstacle constraints during flight. An hp-adaptive Radau pseudospectral method based UAV trajectory planning scheme is proposed by taking the UAV dynamics into account. Numerical experiments are carried out to show the effectiveness and superior of the proposed method. Simulation results show that the proposed method outperform the well-known RRT* and A* algorithm in terms of tracking error.</p>