Terrain Avoidance Research Articles

A line-of-sight zoning method for intervisibility computation by considering terrain relief

Existing intervisibility analysis methods suffer from computational inefficiency due to redundant sampling points. To address this issue, we propose a new approximate method called line-of-sight (LoS) zoning, which leverages continuous terrain relief to identify potentially obscuring zones (POZ) of LoS. By limiting the sampling range to a much smaller POZ, the number of sampling points is significantly reduced. The optimal sampling interval of 6 is determined by striking a balance between computational efficiency and accuracy. Through experiments in both mountainous and plain areas, regardless of the height range and resolution conditions, we demonstrate the high efficiency of the LoS zoning method, especially in scenarios with a high proportion of visible LoS. To account for potential visibility errors caused by sharp peaks in the terrain, we conducted experiments under fixed time intervals to assess the calculation quality of different methods. The results show that in mountainous and plain areas, the improvement in detection rate compared to the hopping strategy method is around 4–6 times in most scenarios. This significant performance enhancement highlights the superiority of the LoS zoning method, and shows great promise in terrain avoidance, path planning in the military, and detection of dangerous targets.

Robust Controller Design for a Generic Helicopter Model: An AI-Aided Application for Terrain Avoidance

This paper focuses on robust controller design for a generic helicopter model and terrain avoidance problem via artificial intelligence (AI). The helicopter model is presented as a hybrid system that covers hover and forward dynamics. By defining a set of easily accessible parameters, it can be used to simulate the motion of different helicopter types. A robust control structure based on reinforcement learning is proposed to ensure the system is robust against model parameter uncertainties. The developed generic model can be utilized in many helicopter applications that have been attempted to be solved with sampling-based algorithms or reinforcement learning approaches that take the dynamical constraints into consideration. This study also focuses on the helicopter terrain avoidance problem to illustrate how the model can be useful in these types of applications and provide an artificial intelligence-aided solution to terrain avoidance.

The Terprom® Digital Terrain System (DTS)

Developed specifically for tactical military aircraft operations, TERPROM® is the world leading Digital Terrain System, offering a range of capabilities that can provide both safety and tactical benefits to the aircraft. The system is an established and well-proven product that has been in production since 1991. To date it has been selected by fourteen Nations worldwide for use on numerous platforms. The number of military aircraft equipped with TERPROM® is now over 5000 and growing. TERPROM® provides the following functions: Terrain Referenced Navigation (TRN), Predictive Ground Collision Avoidance System (PGCAS), Advanced Terrain Avoidance Cueing (ATAC), Obstruction and Wires Warning and Cueing (OWWC), Database Terrain Following (DBTF), Air to Ground Ranging Functions, Terrain Awareness Display (TAD). At the core of the system is the Terrain Referenced Navigation (TRN) capability which navigates by combining aircraft navigation data (e.g. inertial and, or GPS) and radar altimeter measurements of height above ground level (AGI), with terrain heights alms from the stored map data, to produce a navigation solution referenced to the terrain map data. This navigation solution then allows the other TERPROM® functions to use the map data to provide their safety and tactical capabilities. Three variants of TERPROM® have been developed to satisfy the airborne platform market: - Fast Jet, Military Transport, Helicopter. TERPROM® is a living product that has and continues to receive significant internal investment to increase its capability, functionality and performance to support future opportunities. Current areas of development include: Use of higher resolution map data which will increase the performance of TERPROM® and offer enhanced capability to platforms flying nap of the earth, enhanced capability in urban areas and could also reduce any platform reliance on GPS. Integration with active forward looking sensors to provide additional information to the Navigation, Situational Awareness and Ranging functions and also to provide new capabilities such as real time mapping of unmapped terrain features and obstructions.

Exercise therapy guides cortical reorganization after midthoracic spinal contusion to enhance control of lower thoracic muscles, supporting functional recovery

Postural control is critical for locomotion, allowing for gait changes, obstacle avoidance and navigation of rough terrain. A major problem after spinal cord injury (SCI) is regaining the control of balance to prevent falls and further injury. While the circuits for locomotor pattern generation reside in the spinal cord, postural control consists of multiple, complex networks that interact at the spinal, brainstem and cortical levels. After complete SCI, cortical reorganization establishes novel control of trunk musculature that is required for weight-supported stepping. In this study, we examined the impact of exercise therapy on cortical reorganization in the more clinically relevant models of both moderate and severe midthoracic contusion injury in the rat. Results demonstrate that both spontaneous recovery and therapy induced recovery of weight-supported stepping utilize cortical reorganization. Moreover, exercise therapy further improves outcome by enhancing cortical control of lower thoracic muscles enabling improvements in interlimb coordination associated with improved balance that increases weight-supported stepping. The outcome of this study suggest that cortical control of posture is key to functional improvement in locomotion. This information can be used to improve the timing and type of therapy after SCI by considering changes along the entire neural axis.

Algorithmic support of the adaptive system of controlled flight into terrain avoidance (CFITA)

The article considers the restrictions algorithms for the trajectory parameters of aircraft motion. Three groups of algorithms are under consideration. They are distinguished by the volume of airborne database about terrain, modes of aircraft operation. The first group (the full SFITA mode) uses the maximum digital cartographic information (DCI) about the terrain. In the SFITA algorithms, the terrain is approximated in the form of planes in space in some predictable, pre-emptive area in the flight path direction. Terrain following is carried out in space in the assigned direction. The equation of the adjacent plane is solved. The distance, the approach speed, and the time to reach the plane are enumerated. The net acceleration to reduce the approach speed to the plane of restriction is calculated under the specified constraint controls. The required time is calculated to reduce the approach speed towards the obstacle to zero. Having equated the expressions for the time of reaching and the required time, transition towards the distance to the plane of restriction, on which it is imperative to utilize constraint controls. The second group (the major SFITA mode) uses the DCI about the terrain in the direction of track in a pre-emptive area. The terrain is approximated by a line in the plane. The specified normal overload is used as a means of control. Terrain following is conducted in the vertical or slant planes. Subsequently, the same procedures are used as in the first group. The third group (the minimum SFITA mode) does not use DCI. The radio and barometric pressure altimeters are used as information systems about the terrain. The given SFITA mode is selected only for flights in a flat terrain. The algorithm includes the similar procedures as in the first and in the second groups. The analytical analysis, confirming the adaptive properties of algorithmic support based on the fundamental law of uniformly retarded motion, is given. The considered algorithms efficiency is confirmed by a comprehensive amount of simulation. The presented algorithms can become the foundation for developing Russian TAWS analogues.

A Resilient Forward-Looking Terrain Avoidance Warning Method for Helicopters

To reduce nuisance alarms caused by a fixed forward-looking boundary, this paper proposes a resilient forward-looking terrain avoidance method. The method constructed the resilient and adjustable forward-looking boundary model by using roll angle and navigation error, which makes the terrain data employed by the forward-looking alert closer to the terrain data overflown by the anticipated flight trajectory, reduces the nuisance alarm, and improves the reliability of the alert. Using real digital elevation maps and simulated flight testing, the proposed method was observed to provide alerting that is superior to the traditional forward-looking terrain avoidance technique.

An Autonomous Control Framework of Unmanned Helicopter Operations for Low-Altitude Flight in Mountainous Terrains

Low-altitude flight in mountainous terrains is a difficult flight task applied in both military and civilian fields. The helicopter has to maintain low altitude to realize search and rescue, reconnaissance, penetration, and strike operations. It contains complex environment perception, multilevel decision making, and multi-objective flight control; thus, flight is currently mainly conducted by human pilots. In this work, a control framework is implemented to realize autonomous flight for unmanned helicopter operations in an unknown mountainous environment. The identification of targets and threats is introduced using a deep neural network. A 3D vector field histogram method is adopted for local terrain avoidance based on airborne Lidar sensors. In particular, we propose an intuitive direct-viewing method to judge and change the visibilities of the helicopter. On this basis, a finite state machine is built for decision making of the autonomous flight. A highly realistic simulation environment is established to verify the proposed control framework. The simulation results demonstrate that the helicopter can autonomously complete flight missions including a fast approach, threat avoidance, cover concealment, and circuitous flight operations similar to human pilots. The proposed control framework provides an effective solution for complex flight tasks and expands the flight control technologies for high-level unmanned helicopter operations.

Three-dimensional low-altitude flight trajectory planning technology based on digital map

Aiming at the intricate terrain of Yunnan, a three-dimensional low-altitude flight trajectory planning technology based on digital maps is proposed. First, divide the map of Yunnan into 36 digital maps, use VC++ to write a program to read the longitude and latitude coordinates of each point in the digital map and the height of each point into the memory, generate a grid coordinate system based on the start and end points, and find the grid from the memory The height data corresponding to each point in the coordinate system; then, the dynamic planning algorithm and the simple method are used to perform terrain avoidance and terrain following in the grid coordinate system, and finally, use MATLAB to draw the planning path map in the geographic coordinate system.

Ground Control System for UAS Safe Landing Area Determination (SLAD) in Urban Air Mobility Operations.

The use of the Unmanned Aerial Vehicles (UAV) and Unmanned Aircraft System (UAS) for civil, scientific, and military operations, is constantly increasing, particularly in environments very dangerous or impossible for human actions. Many tasks are currently carried out in metropolitan areas, such as urban traffic monitoring, pollution and land monitoring, security surveillance, delivery of small packages, etc. Estimation of features around the flight path and surveillance of crowded areas, where there is a high number of vehicles and/or obstacles, are of extreme importance for typical UAS missions. Ensuring safety and efficiency during air traffic operations in a metropolitan area is one of the conditions for Urban Air Mobility (UAM) operations. This paper focuses on the development of a ground control system capable of monitoring crowded areas or impervious sites, identifying the UAV position and a safety area for vertical landing or take-off maneuvers (VTOL), ensuring a high level of accuracy and robustness, even without using GNSS-derived navigation information, and with on-board terrain hazard detection and avoidance (DAA) capabilities, in particular during operations conducted in BVLOS (Beyond Visual Line Of Sight). The system is composed by a mechanically rotating real-time LiDAR (Light Detection and Ranging) sensor, linked to a Raspberry Pi 3 as SBC (Session Board Controller), and interfaced to a GCS (Ground Control Station) by wireless connection for data management and 3-D information transfer.

A quasi-reflection based SC-PSO for ship path planning with grounding avoidance

To improve the performance of particle swarm optimization (PSO), PSO is further improved by being combined with the sine cosine algorithm (SCA), which maintains a balance in exploration and exploitation. Furthermore, to speed up searching for the optimal path with grounding avoidance, a quasi-reflection-based sine cosine particle swarm optimization (SC-PSO) is designed. Its convergence is then proven. In the early stage, it can roam around the whole space and enhance the global search ability. In the later stage of the optimization process, the convergence ability of global optimization is strengthened. The quasi-reflection operation is introduced into the algorithm to improve the diversity of solutions. A new fitness function is developed to transform the path planning into an optimization problem, combined with the optimization standards and constraints of path length, grounding threat, collision avoidance with water targets and path smoothness, so as to realize the safe and efficient operation of ships. For grounding avoidance, the distance of the closest point of pass (DCPP), the distance of close-quarters situation of ground (DCQG), the time of close-quarters situation of ground (TCQG) and the distance from the ship to the shallows (DIS) are considered. To verify the shallow and collision avoidance of the actual ship operation, the terrain fluctuation data with the highest resolution built from global and regional data sets is used. EETOPO1 is a 1 arc-minute global relief model of Earth's surface that integrates land topography and ocean bathymetry. 3D seabed terrain and 3D collision avoidance are used for grounding avoidance in ship track planning. The results show that, compared with SCA and PSO, the new algorithm has the fastest convergence speed and the highest calculation accuracy.

GENERATION OF AN OPTIMAL LOW-ALTITUDE TRAJECTORY FOR A FIXED-WING UNMANNED AERIAL VEHICLE IN A MOUNTAINOUS AREA

In this study, the three-dimensional optimal trajectory planning of an unmanned fixed-wing aerial vehicle was investigated for Terrain Following – Terrain Avoidance (TF-TA) purposes using the Direct Collocation method. For this purpose, firstly, the appropriate equations representing the translational movement of the aircraft were described. The three-dimensional optimal trajectory planning of the flying vehicle was formulated in the TF-TA manoeuvre as an optimal control problem. The terrain profile, as the main allowable height constraint was modelled using the Fractal Generation Method. The resulting optimal control problem was discretized by applying the Direct Collocation numerical technique and then, was transformed into a Nonlinear Programming Problem (NLP). The efficacy of the proposed method was demonstrated by extensive simulations, and it was particularly verified that the purposed approach can produce a solution satisfying almost all the performance and environmental constraints encountering in a low -altitude flight.

Time-series interval prediction under uncertainty using modified double multiplicative neuron network

This paper presents a hybrid intelligent approach for constructing prediction intervals (PIs) of terrain profiles over time under uncertainty. It utilizes the double multiplicative neuron (DMN) model and the modified particle swarm optimization (MPSO) algorithm to calculate the upper and lower bounds of unknown elevations ahead on terrain profiles based on the vehicles’ track. MPSO withholds particles generating the positive PIs in the training epochs, in order to prevent the occurrence of unreasonable upside-down PIs that are brought by conventional methods. MPSO adjusts the parameters of the DMN model iteratively by minimizing the value of the proposed cost function. The fitness function aims to enhance DMN’s capability of forecasting terrain trends by integrating a trend indicator with PIs coverage probability and interval widths. This study utilizes the terrain profiles of 3 arc-seconds resolution to verify the effectiveness of the proposed MPSO-DMNT approach for one-step and multi-step PIs estimation. Experimental results demonstrate that the proposed approach (1) overcomes the limitations of the conventional PIs indicators; (2) improves the prediction accuracy for terrain trends by 18.8% in the training data and 15.4% in the testing data, and reduces the computational burden by 31.6% in the training data and 8% in the testing data over the lower upper bound estimation (LUBE) method; (3) achieves comparative coverage probability and interval widths to LUBE using a low-complexity single-layered network. The proposed hybrid approach can be used as an auxiliary decision-making tool for terrain avoidance and terrain following in flight.

Path Planning for UGVs Based on Traversability Hybrid A*

In this letter, a new method of path planning for unmanned ground vehicles (UGVs) on terrain is developed. For UGVs moving on terrain, path traversability and collision avoidance are important factors. If traversability is not considered, the planned path may lead a UGV into areas that will cause rough vehicle motion or lead to the UGV getting stuck if the traversability is low. The proposed path planning method is based on the Hybrid A* algorithm and uses estimated terrain traversability to find the path that optimizes both traversability and distance for the UGV. The path planning method is demonstrated using simulated traversability maps and is compared to the original Hybrid A* algorithm. The method is also verified through real-time experiments in real terrain, further demonstrating the benefits of terrain traversability optimization using the proposed path planning method. In the experiments, the proposed method was successfully applied for autonomous driving over distances of up to 270 m in rough terrain. Compared with the existing Hybrid A* method, the proposed method produces more traversable paths.

Aircraft three-dimensional hardconstrained trajectory planning using pseudospectral optimization method

This paper is concerned with the three-dimensional constrained optimal path planning. To this end, an objective function is defined as the shortest trajectory length in such a way that some constraints including rough terrain avoidance, forbidden zone avoidance, initial and terminal constraints, allowable altitude constraint and maximum allowable curvature of trajectory are satisfied. These leads to a challenging problem for aircraft guidance and control that solution requires great experience, skill, accuracy, and a powerful strategy. For this purpose, pseudospectral technique, which is one of the direct methods, is applied in this paper based on the Chebyshev nodes through which the complexity of the problem is reduced by transforming the optimal control problem into the parametric optimization one. The efficacy of the proposed method is demonstrated by extensive simulations, and it is particularly verified that this method is able to produce a solution satisfying all hard constraints of the underlying problem.

Analysis of Civil Aircraft Terrain Avoidance Warning System “Terrain Terrain” Issue Based on QAR Data

As an important instruction recording device for civil aircraft, Quick Access Recorder (QAR) records the operating status of various aircraft systems. Therefore, QAR data is an important basis for aircraft safety assessment, maintenance inspection and failure analysis[1][2]. The Terrain Avoidance Warning System (TAWS) provides the crew with a near-ground warning based on the aircraft’s current track information (including the aircraft’s current position, air pressure altitude, ground speed and track angle, etc.) and referring to terrain altitude data, obstacle data and airport data information. Generally, the QAR data can be used to obtain a more accurate judgment when a warning occurs in the TAWS system. Through the quantitative analysis of QAR data, this paper finds out the root cause of over speed approaching terrain warning in the flight process of an aircraft.

An optimal‐multiphase homing methodology for powered parafoil systems

SummaryIn order to achieve the precise and agile homing control of the powered parafoil system, an optimal‐multiphase homing methodology is explored in this article. The proposed methodology is composed by a trajectory optimization method based on quantum genetic algorithm and a trajectory tracking method based on an improved active disturbance rejection control (ADRC). First, an improved optimization method combining the optimal and multiphase theory is proposed. The optimized trajectory consists of the simple trajectories, the standard lines and circles, while also satisfying the multiple constraints such as the wind disturbance, terrain avoidance, flared landing, and so on. Then, an improved active disturbance rejection controller is presented. For improving the agility of the system during the path switching and the disturbance rejection ability in windy environment, based on ADRC, a disturbance rejection control algorithm is designed for the horizontal controller of the system. By analyzing the aerodynamic characteristic of the system, the wind disturbance will be compensated previously with a feedforward compensation unit. It will be largely reduce the observation error of the extended state observer, while improving the control effect and the reaction speed. Finally, the hardware‐in‐the‐loop simulation is presented to prove the effectiveness of the proposed methodology. The results show that the optimization method can be achieved successfully and the optimized trajectory can be tracked by the improved ADRC controller precisely and agilely. The landing error is less than 15 m with the proposed homing methodology.

Constrained Hopping Traversability Analysis on Non-Uniform Polygonal Chains

We analyze the existence of constrained ballistic hopping paths on a nonuniform polygonal chain. This analysis has practical significance in constrained path planning applications for jumping robots where robot dynamic constraints, uneven surface structure, and non-uniform surface properties are considered. We derive closed-form conditions to satisfy i) damage-free robot landing ii) non-sliding of the robot iii) actuator saturation, and iv) intermediate terrain avoidance constraints. Using the closed-form conditions, we propose a traversability algorithm to determine the path existence between two given points on the polygonal chain. Correspondingly, a path generation approach is discussed to generate an optimal constrained hopping path with minimum number of intermediate hops and least take-off speed per hop. Applicability and viability of the proposed algorithms are demonstrated through computer simulations in a realistic terrain scenario with robot jumping motion uncertainties and disturbances.

Soil Type Impacts Macrohabitat Selection and Spatiotemporal Activity Patterns of Testudo graeca in an Eastern Mediterranean Ecosystem

Habitat preference is determined by complex interactions between biotic and abiotic habitat features and species-specific requirements. In many terrestrial organisms, studies of habitat preference emphasize the role of vegetation. Yet, in land-dwelling ectotherms, an often-overlooked characteristic that may have a strong effect on habitat preference is soil type. We followed 18 Spur-thighed Tortoises (Testudo graeca) mounted with radio transmitters to quantify their seasonal activity patterns, home-range sizes, and preferred habitats, including soil type. We used Maximum Entropy to quantify the spatial distribution of Testudo graeca in the study area based on species occurrence records and nine environmental variables derived from LiDAR. We describe for the first time a strong preference for soil type in Testudo graeca, as well as preference for flat terrain (avoidance of steep slopes). Individuals avoided areas with dense woody vegetation and chose habitats containing heterogeneous vegetation, including isolated trees, shrubs, and open patches. We further found that individuals in the study area do not hibernate but decrease activity in summer-autumn. Finally, home-range size was comparable between the sexes. The overlapping home ranges within and between the sexes throughout the year, including the mating season, suggest that this species is not territorial.

An analysis of human factors in fifty controlled flight into terrain aviation accidents from 2007 to 2017

Introduction: Controlled Flight Into Terrain (CFIT) account for a considerable amount of fatalities when compared to other accident categories. Human factors are deemed significant contributory causes in these accidents. This paper aims to identify the human factors involved with aviation accidents that resulted in CFIT. Method: The study used the Human Factors Analysis and Classification System (HFACS) framework to determine the factors involved in 50 CFIT accidents from 24 counties over a 10 year period, i.e. 2007–2017. Interviews with five senior aviation safety experts were used to provide a better comprehension of the human factors affecting the flight safety. Results: The study identified 1289 individual causal and contributory human factors with unsafe actions and preconditions for unsafe actions being the main subcategories of the accidents. The study found that CFIT occur across a range of pilot experience and 44% of accidents occurred in cruise flight. Distraction, complacency and fatigue are all elements that flight crews may experience as contributors to CFIT during cruising. Conclusions: Human factors represent a major component of CFIT accidents. The analysis revealed a similar pattern of contributory and causal human factors across the various flight categories, with some noteworthy isolated variations. The prevalent factors were decision and skill-based errors along with communication, coordination and planning issues. Practical applications: Provision of specific CFIT awareness, pilot training focusing on improved decision-making and revision of basic flight skills, development of specific Global Positioning System routes for transiting high terrain areas are necessary to prevent CFIT accidents. Installation of Terrain Avoidance and Warning System and Ground Proximity Warning System and appropriate equipment training, specific CFIT Crew Resource Management training and improvement of organizational knowledge on the elements involved in CFIT are also recommended.

TF/TA Optimal Flight Trajectory Planning Using a Novel Regenerative Flattener Mapping Method

In this paper, a new methodology has been proposed to enhance the conformal mapping applications in the process of optimum trajectory planning in Terrain Following (TF) and Terrain Avoidance (TA) Flights. The new approach uses the conformal mapping concept as a flattener tool to transform the constrained trajectory-planning problem with flight altitude restrictions due to the presence of obstacles into a regenerated problem with no obstacle and minimal height constraints. In this regard, the Schwarz–Christoffel theorem has been utilized to incorporate the height constraints into the aircraft dynamic equations of motion. The regenerated optimal control problem then is solved employing a numerical method namely the direct Legendre-Gauss-Radau pseudospectral algorithm. A composite performance index of flight time, terrain masking, and aerodynamic control effort is optimized. Furthermore, to obtain realistic trajectories, the aircraft maximum climb and descent rates are imposed as inequality constraints in the solution algorithm. Several case studies for two-dimensional flight scenarios show the applicability of this approach in TF/TA trajectory-planning. Extensive simulations confirm the efficiency of the proposed approach and verify the feasibility of solutions satisfying all of the constraints underlying the problem