Intelligent Autonomous Decision-Making and Cooperative Control Technology of High-Speed Vehicle Swarms

This book is a reprint of the Special Issue “Intelligent Autonomous Decision-Making and Cooperative Control Technology of High-Speed Vehicle Swarms”,which was published in Applied Sciences

Improving particle swarm optimization path planning through inclusion of flight mechanics

Military engagements are continuing the movement toward automated and unmanned vehicles for a variety of simple and complex tasks. This allows humans to stay away from dangerous situations and use their skills for more difficult tasks. One important piece of this strategy is the use of automated path planners for unmanned aerial vehicles (UAVs). Current UAV operation requires multiple individuals to control a single plane, tying up important human resources. Often paths are planned by creating waypoints for a vehicle to fly through, with the intention of doing reconnaissance while avoiding as much danger to the plane as possible. Path planners often plan routes without taking into consideration the UAV\u27s ability to perform the maneuvers required to fly the specified waypoints, instead relying upon them to fly as close as possible. This thesis presents a path planner solution incorporating vehicle mechanics to insure feasible flight paths. This path planner uses Particle Swarm Optimization (PSO) and digital pheromones to generate multiple three-dimensional flight paths for the operator to choose from. B-spline curves are generated using universal interpolation with each path waypoint representing a control point. The b-spline curve represents the flight path of the UAV. Each point along the curve is evaluated for fuel efficiency, threat avoidance, reconnaissance, terrain avoidance, and vehicle mechanics. Optimization of the flight path occurs based on operator defined performance characteristics, such as maximum threat avoidance or minimum vehicle dynamics cost. These performance characteristics can be defined for each unique aircraft, allowing the same formulation to be used for any aircraft. The vehicle mechanics conditions considered are pull-out, glide, climb, and steady, level, co-ordinate turns. Calculating the flight mechanics requires knowing the velocity and angle of the plane, calculated using the derivative of the point on the curve. The flight mechanics of the path allows the path planner to determine whether the path exceeds the maximum load factor (G-force), minimum velocity (stall velocity), or the minimum turning radius. Comparing the results between PSO Path Planner with flight mechanics and PSO Path Planner without flight mechanics over five scenarios indicates an increase in the feasibility of the returned paths. Visualizing the flight paths was improved by changing the original waypoint based visualization to a b-spline curve representation. Using b-spline curves allows for an accurate representation of the actual UAV flight path especially when considering turns. Operators no longer must create a mental representation of the flight path to match the waypoints

Robust UAV Mission Planning

Unmanned Areal Vehicles (UAVs) can provide significant contributions to information gathering in military missions. UAVs can be used to capture both full motion video and still imagery of specific target locations within the area of interest. In order to improve the effectiveness of a reconnaissance mission, it is important to visit the largest number of interesting target locations possible, taking into consideration operational constraints related to fuel usage between target locations, weather conditions and endurance of the UAV. We model this planning problem as the well-known orienteering problem, which is a generalization of the traveling salesman problem. Given the uncertainty in the military operational environment, robust planning solutions are required. As such, our model takes into account uncertainty in the fuel usage between targets (for instance due to weather conditions) as well as uncertainty in the importance of visiting specific target locations. We report results using different uncertainty sets that specify the degree of uncertainty against which any feasible solution will be protected. We also compare the probability that a solution is feasible for the robust solution on one hand and the solution found with average fuel usage and expected value of information on the other. In doing so, we show how the sustainability of a UAV mission can be significantly improved

A survey on fractional order control techniques for unmanned aerial and ground vehicles

In recent years, numerous applications of science and engineering for modeling and control of unmanned aerial vehicles (UAVs) and unmanned ground vehicles (UGVs) systems based on fractional calculus have been realized. The extra fractional order derivative terms allow to optimizing the performance of the systems. The review presented in this paper focuses on the control problems of the UAVs and UGVs that have been addressed by the fractional order techniques over the last decade

UAV Optimal Cooperative Obstacle Avoidance and Target Tracking in Dynamic Stochastic Environments

Cette thèse propose une stratégie de contrôle avancée pour guider une flotte d'aéronefs sans pilote (UAV) dans un environnement à la fois stochastique et dynamique. Pour ce faire, un simulateur de vol 3D a été développé avec MATLAB® pour tester les algorithmes de la stratégie de guidage en fonctions de différents scénarios. L'objectif des missions simulées est de s'assurer que chaque UAV intercepte une cible ellipsoïdale mobile tout en évitant une panoplie d'obstacles ellipsoïdaux mobiles détectés en route. Les UAVs situés à l'intérieur des limites de communication peuvent coopérer afin d'améliorer leurs performances au cours de la mission. Le simulateur a été conçu de façon à ce que les UAV soient dotés de capteurs et d'appareils de communication de portée limitée. De plus, chaque UAV possède un pilote automatique qui stabilise l'aéronef en vol et un planificateur de trajectoires qui génère les commandes à envoyer au pilote automatique. Au coeur du planificateur de trajectoires se trouve un contrôleur prédictif à horizon fuyant qui détermine les commandes à envoyer à l'UAV. Ces commandes optimisent un critère de performance assujetti à des contraintes. Le critère de performance est conçu de sorte que les UAV atteignent les objectifs de la mission, alors que les contraintes assurent que les commandes générées adhèrent aux limites de manoeuvrabilité de l'aéronef. La planification de trajectoires pour UAV opérant dans un environnement dynamique et stochastique dépend fortement des déplacements anticipés des objets (obstacle, cible). Un filtre de Kalman étendu est donc utilisé pour prédire les trajectoires les plus probables des objets à partir de leurs états estimés. Des stratégies de poursuite et d'évitement ont aussi été développées en fonction des trajectoires prédites des objets détectés. Pour des raisons de sécurité, la conception de stratégies d'évitement de collision à la fois efficaces et robustes est primordiale au guidage d'UAV. Une nouvelle stratégie d'évitement d'obstacles par approche probabiliste a donc été développée. La méthode cherche à minimiser la probabilité de collision entre l'UAV et tous ses obstacles détectés sur l'horizon de prédiction, tout en s'assurant que, à chaque pas de prédiction, la probabilité de collision entre l'UAV et chacun de ses obstacles détectés ne surpasse pas un seuil prescrit. Des simulations sont présentées au cours de cette thèse pour démontrer l'efficacité des algorithmes proposés

improving path planning of unmanned aerial vehicles in an immersive environment using meta-paths and terrain information

Effective command and control of unmanned aerial vehicles (UAVs) is an issue under investigation as the military pushes toward more automation and incorporation of technology into their operational strategy. UAVs require the intelligence to maneuver safely along a path to an intended target while avoiding obstacles such as other aircraft or enemy threats. To date, path-planning algorithms (designed to aid the operator in the control of semi-autonomous UAVs) have been limited to providing only a single solution (alternate path) without utilizing input or feedback from the UAV operator. The work presented in this thesis builds off of and improves an existing path planner. The original path planner presents a unique platform for decision making in a three-dimensional environment where multiple solution paths are generated using Particle Swarm Optimization (PSO) and returned to the operator for evaluation. The paths are optimized to minimize risk due to enemy threats, to minimize fuel consumption incurred by deviating from the original path, and to maximize reconnaissance over predefined targets. The work presented in this thesis focuses on improving the mathematical models of these objectives. Terrain data is also incorporated into the path planner to ensure that the generated alternate paths are feasible and at a safe height above ground. An effective interface is needed to evaluate the alternate paths returned by PSO. A meta-path is a new concept presented in this thesis to address this issue. Meta-paths allow an operator to explore paths in an efficient and organized manner by displaying multiple alternate paths as a single path cloud. The interface was augmented with more detailed information on these paths to allow the operator to make a more informed decision. Two other interaction techniques were investigated to allow the operator more interactive control over the results displayed by the path planner. Viewing the paths in an immersive environment enhances the operator\u27s understanding of the situation and the options while facilitating better decision making. The problem formulation and solution implementation are described along with the results from several simulated scenarios. Preliminary assessments using simulated scenarios show the usefulness of these features in improving command and control of UAVs. Finally, a user study was conducted to gauge how different visualization capabilities affect operator performance when using an interactive path planning tool. The study demonstrates that viewing alternate paths in 3D instead of 2D takes more time because the operator switches between multiple views of the paths but also suggests that 3D is better for allowing the operator to understand more complex situations

Scalable Approach to Uncertainty Quantification and Robust Design of Interconnected Dynamical Systems

Development of robust dynamical systems and networks such as autonomous aircraft systems capable of accomplishing complex missions faces challenges due to the dynamically evolving uncertainties coming from model uncertainties, necessity to operate in a hostile cluttered urban environment, and the distributed and dynamic nature of the communication and computation resources. Model-based robust design is difficult because of the complexity of the hybrid dynamic models including continuous vehicle dynamics, the discrete models of computations and communications, and the size of the problem. We will overview recent advances in methodology and tools to model, analyze, and design robust autonomous aerospace systems operating in uncertain environment, with stress on efficient uncertainty quantification and robust design using the case studies of the mission including model-based target tracking and search, and trajectory planning in uncertain urban environment. To show that the methodology is generally applicable to uncertain dynamical systems, we will also show examples of application of the new methods to efficient uncertainty quantification of energy usage in buildings, and stability assessment of interconnected power networks