Terrain Cost Research Articles

An Enhanced Multiple Unmanned Aerial Vehicle Swarm Formation Control Using a Novel Fractional Swarming Strategy Approach

This paper addresses the enhancement of multiple Unmanned Aerial Vehicle (UAV) swarm formation control in challenging terrains through the novel fractional memetic computing approach known as fractional-order velocity-pausing particle swarm optimization (FO-VPPSO). Existing particle swarm optimization (PSO) algorithms often suffer from premature convergence and an imbalanced exploration–exploitation trade-off, which limits their effectiveness in complex optimization problems such as UAV swarm control in rugged terrains. To overcome these limitations, FO-VPPSO introduces an adaptive fractional order β and a velocity pausing mechanism, which collectively enhance the algorithm’s adaptability and robustness. This study leverages the advantages of a meta-heuristic computing approach; specifically, fractional-order velocity-pausing particle swarm optimization is utilized to optimize the flying path length, mitigate the mountain terrain costs, and prevent collisions within the UAV swarm. Leveraging fractional-order dynamics, the proposed hybrid algorithm exhibits accelerated convergence rates and improved solution optimality compared to traditional PSO methods. The methodology involves integrating terrain considerations and diverse UAV control parameters. Simulations under varying conditions, including complex terrains and dynamic threats, substantiate the effectiveness of the approach, resulting in superior fitness functions for multi-UAV swarms. To validate the performance and efficiency of the proposed optimizer, it was also applied to 13 benchmark functions, including uni- and multimodal functions in terms of the mean average fitness value over 100 independent trials, and furthermore, an improvement at percentages of 29.05% and 2.26% is also obtained against PSO and VPPSO in the case of the minimum flight length, as well as 16.46% and 1.60% in mountain terrain costs and 55.88% and 31.63% in collision avoidance. This study contributes valuable insights to the optimization challenges in UAV swarm-formation control, particularly in demanding terrains. The FO-VPPSO algorithm showcases potential advancements in swarm intelligence for real-world applications.

基于EMSDBO算法农业植保无人机三维航迹多目标优化研究

Both cruising ability and safety should be considered in the 3D inspection path planning of agricultural unmanned aerial vehicles (UAVs). Specific to a complex working environment, the 3D inspection environment of agricultural UAVs was simulated through terrain modeling and threat modeling. First, the dynamic constraints of flight approaching rate and response time were added to the threat cost, and the 3D mission space model and flight path cost function were constructed considering the influence of UAVs’ turning performance. Second, the offset estimation strategy, variable spiral search strategy, quasi-reverse learning strategy and dimension-by-dimension mutation strategy were introduced into the dung beetle optimizer (DBO) algorithm to improve the global optimization ability and convergence rate of the algorithm. By establishing a three-dimensional trajectory planning model for unmanned aerial vehicles, the trajectory planning is transformed into a multi-objective function optimization problem, and an improved algorithm is used to solve the three-dimensional trajectory planning of unmanned aerial vehicles. The fitness is evaluated by considering the objective function of trajectory cost, terrain cost, and danger level, and the trajectory planning is iteratively optimized. The results indicate that the proposed improved dung beetle algorithm for trajectory planning has lower overall cost and stability in adapting to different complex terrain environments.

How Rough Is the Path? Terrain Traversability Estimation for Local and Global Path Planning

Perception and interpretation of the terrain is essential for robot navigation, particularly in off-road areas, where terrain characteristics can be highly variable. When planning a path, features such as the terrain gradient and roughness should be considered, and they can jointly represent the traversability cost of the terrain. Despite this range of contributing factors, most cost maps are currently binary in nature, solely indicating traversible versus non-traversible areas. This work presents a joint local and global planning methodology for building continuous cost maps using LIDAR, based on a novel traversability representation of the environment. We investigate two approaches. The first, a statistical approach, computes terrain cost directly from the point cloud. The second, a learning-based approach, predicts an IMU response solely from geometric point cloud data using a 2D-Convolutional-LSTM neural network. This allows us to estimate the cost of a patch without directly driving over it, based on a data set that maps IMU signals to point cloud patches. Based on the terrain analysis, two continuous cost maps are generated to jointly select the optimal path considering distance and traversability cost for local navigation. We present a real-time terrain analysis strategy applicable for local planning, and furthermore demonstrate the straightforward application of the same approach in batch mode for global planning. Off-road autonomous driving experiments in a large and hybrid site illustrate the applicability of the method. We have made the code available online for users to test the method.

‘Benefit Maximizing Routes’: Development and Evaluation Using the Historical Roads of Korea’s Joseon Dynasty (1392–1910)

Least cost paths (LCPs) have been widely used by archaeologists and geographers to reconstruct probable routes of movement within past landscapes using GIS-based modelling. By definition, LCP modelling is based on the premise that ‘least cost’ features as the primary factor in the decision-making of movement. It can be argued, however, that movement within the landscape was structured not only by the need to <em>minimize costs</em> but also by the desire to <em>maximize benefits</em>. This study introduces a new way of estimating terrain costs that can factor in this tendency towards ‘benefit maximization’ when modelling routes. This alternative methodology features a distinctive way of perceiving the landscape, which differs from the grid-based division of the landscape generally used in LCP modelling. The landscape is seen to be made up of ten different ‘hillslope position units’ (which are generic landscape units widely used in landscape evolution studies) and ‘movement suitability’ values are estimated for each of the hillslope position units. These values are then used to produce a ‘hillslope position unit movement suitability’ (HPMS) cost surface. The evaluation of the HPMS cost surface is undertaken by comparing HPMS-based routes and slope gradient-based LCPs against the historical roads of Korea’s Joseon Dynasty (1392–1910). It is observed that HPMS-based routes demonstrate a greater degree of correspondence with historically confirmed routes, compared to slope gradient-based LCPs. The similarity between HPMS-based routes and historically confirmed routes is found to be greater in mountainous regions <em>vis-à-vis</em> non-mountainous regions. It is proposed that, by taking into account human desire to utilize beneficial landscapes, HPMS-based cost surfaces may result in modelled routes that are closer to actual past experiences.

Pathfinding and Abstraction with Dynamic Terrain Costs

Abstraction and refinement is a common approach used in games to improve the speed of pathfinding by planning in an abstract space and then refining abstract paths to traversable paths. While there are many variants of this approach that have been developed and studied, research on this problem has largely ignored the problem of pathfinding with terrain types, terrain costs, and dynamic terrain. This paper studies the problem of pathfinding in domains with terrain costs and proposes an abstraction approach that is built around handling terrain costs and dynamic terrain. The resulting approach is able to handle costs in a way that existing approaches do not, and provides a good balance between memory usage, path quality, and pathfinding speed.

Response.

Dear Editor-in-Chief, We are grateful for the opportunity to respond to the insightful critiques provided by our colleagues and clarify our study’s rationale (1). Load carriage over complex terrain is a common scenario for dismounted military. However, it is challenging to study as it involves prolonged outdoor walking at a varying pace over a series of route segments of different distances, grades, and surfaces. Global positioning systems (GPS) offer a practical solution for obtaining data that can be input into walking equations to estimate energy expenditure (EE). We made estimates of EE using GPS-based modeling as a pragmatic means of providing needed solutions to our military customers for use in real-world, non–steady-state activities. Our study attempted to estimate EE contributed by aerobic metabolism in dynamic field conditions. When taken in the context of exercise with a discrete on and off phase, we see how our methodology has led to some confusion. Because of respiratory gas exchange kinetics, measures obtained in real time via gas exchange display a classic half-time response, both on and off; therefore, they do not directly compare with steady-state estimates. The approach we took sought to adjust for this half-time response. As part of our rationale, we included references describing delayed respiratory gas exchange kinetics (2,3) and excess postexercise oxygen consumption (EPOC) (4,5). Typically, estimates of EE are made using a square-wave prediction; however, using this approach avoids accounting for these half-time responses. To address these time offsets, we applied a simple exponential smoothing filter to better align walking equation estimates with aerobic EE calculated from measured oxygen uptake. This approach assumes the steady-state energy cost for a given work is correct but adjusts the time period during which the cost is expended to closer approximate how the energy cost is met by aerobic metabolism. For example, exponential smoothing a basic on–off square-wave results in a curve similar to the oxygen deficit/EPOC response to exercise. As with any method, there are limitations and room for improvement. On one hand, exponential smoothing does not immediately account for the contribution of all anaerobic energy stores when walking speed, grade, or terrain costs increase. On the other hand, instantaneous EE estimates are difficult to verify, and solely using them ignores order effects such as additional aerobic EE during trail segments that follow more metabolically taxing ones (e.g., downhill slope after a climb to the summit). To address other raised concerns in order: (i) GPS device details were listed in the original manuscript; (ii) we did state the need for downhill correction factor(s) but do note our colleagues have since developed a series of equations estimating EE during level, uphill, and downhill walking from GPS, heart rate, and accelerometry (6); (iii) the same conversion factor was applied to both measured and estimated V˙O2; and (iv) our colleagues’ equation estimates V˙O2 from combined mass of the individual plus all equipment. We appreciate the constructive feedback and look forward to future work in this area. Adam W. Potter David P. Looney William R. Santee U.S. Army Research Institute of Environmental MedicineNatick, MA

Procurements (Purchase or R&amp;D) for Ballistic Missile Defense Systems

We perform cost analysis for evaluating alternatives to effectively defend the threat of North Korean ballistic missiles. As alternatives, THAAD, SM-3, and L-SAM are considered, and we analyze the factors affecting to the cost of acquiring alternatives depending on the way to procure them, e.g., by purchasing, or research & development (R&D). Assuming that each alternative can be operated for 30 years, the total cost, which consists of acquiring cost, operation/maintenance cost, and R&D cost, is compared using net present value. Furthermore, we perform sensitivity analysis for maintenance rate, terrain cost and increase rate of R&D cost that involve uncertainty for the future. Cost analysis results show that THAAD incurs the highest cost and SM-3 is the most economic alternative. However, sensitivity analysis shows that priority can be changed depending on uncertainty parameters. Although our results do not directly support decision-making on the best alternative due to its various uncertainties, these can provide a guideline on economic evaluation required for public policy decision-making process, especially under the situation that all the three alternatives cannot be acquired at the same time due to the limitation of defense budget.

Humans, water, and the colonization of Australia

The Pleistocene global dispersal of modern humans required the transit of arid and semiarid regions where the distribution of potable water provided a primary constraint on dispersal pathways. Here, we provide a spatially explicit continental-scale assessment of the opportunities for Pleistocene human occupation of Australia, the driest inhabited continent on Earth. We establish the location and connectedness of persistent water in the landscape using the Australian Water Observations from Space dataset combined with the distribution of small permanent water bodies (springs, gnammas, native wells, waterholes, and rockholes). Results demonstrate a high degree of directed landscape connectivity during wet periods and a high density of permanent water points widely but unevenly distributed across the continental interior. A connected network representing the least-cost distance between water bodies and graded according to terrain cost shows that 84% of archaeological sites >30,000 y old are within 20 km of modern permanent water. We further show that multiple, well-watered routes into the semiarid and arid continental interior were available throughout the period of early human occupation. Depletion of high-ranked resources over time in these paleohydrological corridors potentially drove a wave of dispersal farther along well-watered routes to patches with higher foraging returns.

Research on Route Planning of Cruise Missile Based on Improved Particle Swarm Optimization Algorithm

For the route planning problem of cruise missile, a method based on PSO is proposed and simulated. The route cost model is established which in-tegrates the terrain cost, the threating cost and the stratagem obviation cost. With the model, digital maps and battlefield information can be transformed to the grid space for route planning. For the premature defect in the standard PSO algorithm, a method based on similarity variation is proposed for route planning. Simulation results show that the searching effect of the improved algorithm is better than which of the standard algorithm in route planning, the improved algorithm can get better route.

Multiresolution rough terrain motion planning

We describe a new approach to the problem of motion planning for mobile robots on natural rough terrain. Our approach computes a multiresolution representation of the terrain using wavelets, and hierarchically plans the path through sections which are well approximated on coarser levels and relatively smooth. Unlike most methods, the hierarchical approximation errors are used explicitly in a cost function to distinguish preferred terrain sections. The error is computed using the corresponding wavelet coefficients. We also propose a new nonscalar path cost measure based on the sorted terrain costs along the path. This measure can be incorporated into standard global path search algorithms and yields paths which avoid high cost terrain areas when possible. Additional constraints for specific robots can be integrated into this approach for efficient hierarchical motion planning on rough terrain. We present the algorithms and experimental results for real terrain data.