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A terrain risk assessment method for military surveillance applications for mobile assets
Abstract
Abstract This study proposes an analytical and flexible terrain risk assessment method for military surveillance applications for mobile assets. Considering the risk as the degree of possibility of insurgent presence, the assessment method offers an efficient evaluation of risk in the surrounding terrain for military combat operating posts or observation posts. The method is designed for unmanned aerial vehicles as the surveillance assets of choice to improve the effectiveness of their use. Starting with the area map and geographical data, the target terrain is first digitized for space representation. Then the data of nine geographical parameters are used to formulate five contributing risk factors. These factors are incorporated in an analytical framework to generate a composite map with risk scores that reveal the potential high-risk spots in the terrain. The proposed method is also applied to a real-life case study of COP Kahler in Afghanistan, which was a target for insurgent attacks in 2008. The results confirm that when evaluated with the developed method, the region that the insurgents used to approach COP Kahler has high concentration of high-risk cells.
... In trafficability studies, the emphasis is on the interaction between vehicle and the surface material, including obstacles and topography on vehicle mobility. The properties of terrain surface influence off-road vehicle performance a lot [20], especially for vehicle trafficability. ...
... Ant colony algorithm is used to obtain the shortest path which is recorded in table Path. Path is an m 9 2n matrix recording the x and y coordinate along the path ant travelled which satisfying following condition: (20). Here for the offroad path planning problem, the distance of path is a comprehensive metric of geographic length and terrain surface properties. ...
... When ant m ' finishes its search. Judge the feasibility of Path(m 0 ,:) according to formula (20). If it is feasible, calculate the length Dist(m 0 ) of this Path(m 0 ,:) according to formula (21). ...
Optimal vehicle off-road path planning problem must consider surface physical properties of terrain and soil. In this paper, we firstly analyse the comprehensive influence of terrain slope and soil strength to vehicle’s off-road trafficability. Given off-road area, the GO or NO-GO tabu table of terrain gird is determined by slope angle and soil remolding cone index (RCI). By applying tabu table and grid weight table, the influence of terrain slope and soil RCI are coordinated to reduce the search scope of algorithm and improve search efficiency. Simulation results based on tracked vehicle M1A1 in off-road environment show that, improved ant colony path planning algorithm not only considers the influence of actual terrain and soil, but also improves computation efficiency. The time cost of optimal routing computation is much lower which is essential for real time off-road path planning scenarios.
... Surveillance systems have been evolving from simple local close-circuit television systems, aeronautical/aerospace systems [1][2][3], and integrated cities infrastructure systems [4] to complex military systems [5]. With the evolution of technology, the surveillance systems have become more powerful, with complex autonomous tasks delivered by Artificial Intelligence (AI) platforms [6]. ...
This paper proposes an aerial data network infrastructure for large geographical area surveillance systems. The work presents a review of previous works from the authors, existing technologies in the market and other scientific work, with the goal of creating a data network supported by Autonomous Tethered Aerostat Airships used for sensor fixing, drones deployment base and meshed data network nodes installation. The proposed approach for data network infrastructure supports several independent and heterogeneous services from independent, private and public companies. The presented solution employs Edge Artificial Intelligence (AI) systems for autonomous infrastructure management. The Edge AI used in the presented solution enables the AI management solution to work without the need of a permanent connection to cloud services and is constantly feed by the locally generated sensor data. These systems interact with other network AI services to accomplish coordinated tasks. Blockchain technology services are deployed to ensure secure and auditable decisions and operations, validated by the different involved ledgers.
... With the development of science and technology, intelligent robots have become a key research field and have been widely used in industry [1], agriculture [2], military [3], mining [4], medical services [5], planetary exploration [6], search and rescue [7], domestic service [8,9], and so on. Intelligent robots must have stable and reliable behavioral decision-making capabilities in performing tasks in unknown environments. ...
How to improve the behavioral decision-making ability and adaptability to unknown environments is of great importance for an agent. The traditional decision-making methods usually suffer from long training time, due to the large amount of training samples, or low adaptability to the unknown environments, or lack of the continuous learning capacities, etc. In response to these problems, this work proposes a novel motivated developmental network (MDN) to improve the decision-making ability of the agent. During the environment exploration, if the agent encounters an unknown environment, new layers and neurons are dynamically inserted to the MDN, according to the task requirements. Through the interaction between internal neurons and the inserted new neurons, the agent can autonomously develop and learn in the unknown environments without training data, but the behavioral decision-making at this stage is random. To further improve the agent’s decision-making ability, in the off-task process, through the gated self-organization mechanism, the agent can selectively recall the specific knowledge in its “brain”, and the MDN will transfer and generate a large amount of new data, according to the recalled knowledge, then the new layers and neurons will be inserted to memorize the new knowledge. Hence the knowledge base of the MDN becomes more and more complete, thereby improving its decision-making ability and adaptability to the new environment. To demonstrate the performance of the MDN model, a mobile robot navigation in different environments are executed. The experimental results illustrate that the agent can not only autonomously learn in static environments, but also has better decision-making ability in unknown dynamic environment, i.e., better adaptability to the new environment. Comparison with other algorithms further demonstrate the potential of the proposed MDN model.
... A high weight value refers to a strategically more important (and dangerous) target whereas a low weight refers to relatively less dangerous one. These weights can be estimated by considering information from different sources [see for example, Buyurgan and Lehlou (2015) for such a method] and intelligence related to the possibility of target presence, type of target (e.g. terrorists, extremists, infantry-type targets, intruders, smugglers, human and/or drug traffickers), threat level, size of the threat organization, and types of weapons and other equipment owned by them. ...
In this paper we introduce a bi-objective location-allocation problem for Unmanned Aircraft Systems (UASs) operating in a hostile environment. The objective is to find the locations to deploy UASs and assign Unmanned Aerial Vehicles to regions for surveillance. One of the objectives is to maximize search effectiveness, while the second is the minimization of the threats posed to the UASs. These two objectives are in conflict, because they are affected differently by the proximity between the UAS locations and the target regions. First, we have formulated this problem as a mixed integer nonlinear program. Next, we have developed its linearization which can be solved by a commercial optimizer for small-scale problem instances. To solve large-scale problems, we have adopted a well-known metaheuristic for multi-objective problems, namely the elitist non-dominated sorting genetic algorithm. We have also developed a hybrid approach, which has proven to be more effective than each approach alone.
... Interested readers are referred to Coutinho, Battarra, and Fliege (2018) for a detailed review on the UAV routing problems. Buyurgan and Lehlou (2015) explicitly study the properties of the area of surveillance to assess the risk posed to aerial vehicle. They propose a method to determine the potential high-risk area in the terrain using geographical parameters. ...
In Aerial Surveillance Problem (ASP), an air platform with surveillance sensors searches a number of rectangular areas by covering the rectangles in strips and turns back to base where it starts. In this paper, we present a multiobjective extension to ASP, for which the aim is to help aerial mission planner to reach his/her most preferred solution among the set of efficient alternatives. We consider two conflicting objectives that are minimizing distance travelled and maximizing minimum probability of target detection. Each objective can be used to solve single objective ASPs. However, from mission planner’s perspective, there is a need for simultaneously optimizing both objectives. To enable mission planner reaching his/her most desirable solution under conflicting objectives, we propose exact and heuristic methods for multiobjective ASP (MASP). We also develop an interactive procedure to help mission planner choose the most satisfying solution among all Pareto optimal solutions. Computational results show that the proposed methods enable mission planner to capture the trade-offs between the conflicting objectives for large number of alternative solutions and to eliminate the undesirable solutions in small number of iterations.
... One limitation of this paper is that it has examined the consequences of fuel sharing in expeditionary logistics, but not the methods of accomplishing it. Risk-minimizing routing has been studied before (Bae, Kim & Han, 2015) and of course, evolving technology has been applied to help find safe routes in contested areas for military vehicles (Buyurgan & Lehlou, 2015). That modeling could be applied to the problem of reducing the risk of expeditionary transshipments. ...
Full text at: https://calhoun.nps.edu/handle/10945/61117
Expeditionary units operate beyond the normal supply chain. We examine a situation in which expeditionary units are dispersed and moving forward to achieve some mission objective. The logistics decision must be made: should resources (fuel) be shared between those expeditionary units. Our criterion is mission risk, or the probability the expeditionary mission succeeds. Mission risk is modeled as a function of both logistics risk and operational risk. We present a model of fuel sharing which incorporates this balance of risks and we demonstrate the effectiveness of fuel sharing in an extended numerical example, based on historical data from the Ardennes campaign in WWII.
Autonomous navigation of mobile robot in unknown environment has attracted much attention of scholars over the past decades, and many bio-inspired heuristic navigation models have been presented, such as the cerebellum and basal ganglia models. Pervious cerebellum and basal ganglia model, however, treats them as parallel and independent system, without interaction between them, or combines their function together, but with only unidirectional communication between them. Based on the cognition and developmental mechanism of the biology, this paper proposes a novel navigation model, which uses the motivated developmental network (MDN) to mimic the supervised learning of the cerebellum and the reinforcement learning based on the radial basis function neural network (RBFNN) to simulate the reward-based learning of the basal ganglia, and integrates them together to construct a hybrid complex cognition model, to navigate a mobile robot in unknown environment. During the environment exploration, for the unexplored places, the artificial agent uses the cerebellum model to choose action, instead of the ∊-greedy method, to accelerate the learning convergence speed of the basal ganglia. For the explored places, it directly uses the basal ganglia based on the RBFNN to explore, then update and perfect the knowledge base of the cerebellum, which enable the cerebellum to achieve better decision in the following exploration. Hence, it realizes not only the two way communication between the cerebellum and basal ganglia, but also the co-development of them. Experimental results show that this model can enable the agent to autonomously development its intelligence through the hybrid learning. As far as we know, this is the first work to realize the direct communication between the basal ganglia and the cerebellum.
This paper presents a hybrid fuzzy-optimization methodology to solve the large-scale disaster relief distribution problem with the main goal of minimizing the total number of fatalities. The proposed method mainly contains a three-stage operational algorithm. Here fuzzy clustering techniques are used in the first stage to classify the damaged areas based on respective relief demand attributes and priority, followed by the second and the third stage, where fuzzy linear programming is employed to deal with the situation when the demand for victims is greater than the available resources. Results of a numerical study are illustrated to demonstrate the applicability of the proposed method.
Last mile distribution is the final stage of a humanitarian relief chain; it refers to delivery of relief supplies from local distribution centers (LDCs) to beneficiaries affected by disasters. In this study, we consider a vehicle-based last mile distribution system, in which an LDC stores and distributes emergency relief supplies to a number of demand locations. The main decisions are allocating the relief supplies at the LDCs among the demand locations and determining the delivery schedules/routes for each vehicle throughout the planning horizon. We propose a mixed integer programming model that determines delivery schedules for vehicles and equitably allocates resources, based on supply, vehicle capacity, and delivery time restrictions, with the objectives of minimizing transportation costs and maximizing benefits to aid recipients. We show how the proposed model optimizes resource allocation and routing decisions and discuss the tradeoffs between these decisions on a number of test problems. Finally, we identify opportunities for the use of intelligent transportation systems in last mile distribution.
Analytical and discrete optimization approaches for routing an aircraft in a threat environment have been developed. Using
these approaches, an aircraft’s optimal risk trajectory with a constraint on the path length can be efficiently calculated.
The analytical approach based on calculus of variations reduces the original risk optimization problem to the system of nonlinear
differential equations. In the case of a single radarinstallation, the solution of such a system is expressed by the elliptic
sine. The discrete optimization approach reformulates the problem as the Weight Constrained Shortest Path Problem (WCSPP)
for a grid undirected graph. The WCSPP is efficiently solved by the Modified Label Setting Algorithm (MLSA). Both approaches
have been tested with several numerical examples. Discrete nonsmooth solutions with high precision coincide with exact continuous
solutions. For the same graph, time in which the discrete optimization algorithm computes the optimal trajectory is independent
of the number of radars. The discrete approach is also efficient for solving the problem using different risk functions.
We describe a real-time system that supports design of optimal flight paths over terrains. These paths either maximize view
coverage or minimize vehicle exposure to ground. A volume-rendered display of multi-viewpoint visibility and a haptic interface
assists the user in selecting, assessing, and refining the computed flight path. We design a three-dimensional scalar field
representing the visibility of a point above the terrain, describe an efficient algorithm to compute the visibility field,
and develop visual and haptic schemes to interact with the visibility field. Given the origin and destination, the desired
flight path is computed using an efficient simulation of an articulated rope under the influence of the visibility gradient.
The simulation framework also accepts user input, via the haptic interface, thereby allowing manual refinement of the flight
path.
This study presents an approach for semi-automated classification of tree species in different types of forests using first and second generation ADS40 and RC30 images from two study areas located in the Swiss Alps. In a first step, high-resolution canopy height models (CHMs) were generated from the ADS40 stereo-images. In a second step, multi-resolution image segmentation was applied. Based on image segments seven different tree species for study area 1 and four for study area 2 were classified by multinomial regression models using the geometric and spectral variables derived from the ADS40 and RC30 images. To deal with the large number of explanatory variables and to find redundant variables, model diagnostics and step-wise variable selection were evaluated. Classifications were ten-fold cross-validated for 517 trees that had been visited in field surveys and detected in the ADS40 images. The overall accuracies vary between 0.76 and 0.83 and Cohen's kappa values were between 0.70 and 0.73. Lower accuracies (kappa < 0.5) were obtained for small samples of species such as non-dominant tree species or less vital trees with similar spectral properties. The usage of NIR bands as explanatory variables from RC30 or from the second generation of ADS40 was found to substantially improve the classification results of the dominant tree species. The present study shows the potential and limits of classifying the most frequent tree species in different types of forests, and discusses possible applications in the Swiss National Forest Inventory.
The constrained shortest-path problem (CSPP) gener- alizes the standard shortest-path problem by adding one or more path-weight side constraints. We present a new algorithm for CSPP that Lagrangianizes those constraints, optimizes the resulting Lagrangian func- tion, identifies a feasible solution, and then closes any optimality gap by enumerating near-shortest paths, measured with respect to the Lagrangianized length. "Near-shortest" implies � -optimal, with a varying � that equals the current optimality gap. The algorithm exploits a variety of techniques: a new path-enumeration method; aggregated constraints; preprocessing to elim- inate edges that cannot form part of an optimal solu- tion; "reprocessing" that reapplies preprocessing steps as improved solutions are found; and, when needed, a "phase-I procedure" to identify a feasible solution before searching for an optimal one. The new algorithm is often an order of magnitude faster than a state-of-the-art label-setting algorithm on singly constrained randomly generated grid networks. On multiconstrained grid networks, road networks, and networks for aircraft routing the advantage varies but, overall, the new algorithm is competitive with the label- setting algorithm. © 2008 Wiley Periodicals, Inc.∗ NETWORKS, Vol. 00(0), 000-000 2008
This paper addresses the weighted anisotropic shortest-path problem on a continuous domain, i.e., the computation of a path between two points that minimizes the line integral of a cost-weighting function along the path. The cost-weighting depends both on the instantaneous position and direction of motion. We propose an algorithm for the computation of shortest-path that reduces the problem to an optimization over a finite graph. This algorithm restricts the search to paths formed by the concatenation of straight-line segments between points, from a suitably chosen discretization of the continuous region. To maximize efficiency, the discretization of the continuous region should not be uniform. We propose a novel "honeycomb" sampling algorithm that minimizes the cost penalty introduced by discretization. The resulting path is not optimal but the cost penalty can be made arbitrarily small at the expense of increased computation. This methodology is applied to the computation of paths for groups of unmanned air vehicles (UAVs) that minimize the risk of being destroyed by ground defenses. We show that this problem can be formulated as a weighted anisotropic shortest-path optimization and show that the algorithm proposed can efficiently produce low-risk paths.
The soldiers at the Combat Outpost Keating, established in 2006, were outnumbered and surrounded by a heavily armed Taliban force. Initially troops were charged with befriending the locals and stopping illegal movement across the border, but conditions around the post soon began to deteriorate. Eight US soldiers were killed and 22 wounded during the massive, complex attack in 2009. Nineteen aircraft, both Air Force fixed wing and Army helicopters, flew close air support missions during the desperate battle. Intelligence-surveillance- reconnaissance assets also were reprioritized to support Barg-e Matal, a village located just north of the Kamdesh Valley, where Keating was based. The ISR assets also were to be used to help locate a missing US soldier in southern Afghanistan. The steep mountains were making it difficult for aircraft to communicate directly with Keating, so one of them had to circle above the joint terminal attack controller at FOB Bostick, about 20 miles away.