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

Minimum Distance and Minimum Time Optimal Path Planning with Bioinspired Machine Learning Algorithms for Impaired Unmanned Air Vehicles

Unmanned air vehicles operate in highly dynamic and unknown environments where they can encounter unexpected and unseen failures. In the presence of emergencies, autonomous unmanned air vehicles should be able to land at a minimum distance or minimum time. Impaired unmanned air vehicles define actuator failures and this impairment changes their unstable and uncertain dynamics; henceforth, path planning algorithms must be adaptive and model-free. In addition, path planning optimization problems must consider the unavoidable actuator saturations, kinematic and dynamic constraints for successful real-time applications. Therefore, this paper develops 3D path planning algorithms for quadrotors with parametric uncertainties and various constraints. In this respect, this paper constructs a multi-dimensional particle swarm optimization and a multi-dimensional genetic algorithm to plan paths for translational, rotational, and Euler angles and generates the corresponding control signals. The algorithms are assessed and compared both in the simulation and experimental environments. Results show that the multi-dimensional genetic algorithm produces shorter minimum distance and minimum time paths under the constraints. The real-time experiments prove that the quadrotor exactly follows the produced path utilizing the available maximum rotor speeds

Beetle Colony Optimization Algorithm and its Application

Massive data sets and complex scheduling processes have high-dimensional and non-convex features bringing challenges on various applications. With deep insight into the bio-heuristic opinion, we propose a novel Beetle Colony Optimization (BCO) being able to adapt NP-hard issues to meet growing application demands. Two important mechanisms are introduced into the proposed BCO algorithm. The first one is Beetle Antennae Search (BAS), which is a mechanism of random search along the gradient direction but not use gradient information at all. The second one is swarm intelligence, which is a collective mechanism of decentralized and self-organized agents. Both of them have reached a performance balance to elevate the proposed algorithm to maintain a wide search horizon and high search efficiency. Finally, our algorithm is applied to traveling salesman problem, and quadratic assignment problem and possesses excellent performance, which also shows that the algorithm has good applicability from the side. The effectiveness of the algorithm is also substantiated by comparing the results with the original ant colony optimization (ACO) algorithm in 3D simulation model experimental path planning

Motion Planning

Motion planning is a fundamental function in robotics and numerous intelligent machines. The global concept of planning involves multiple capabilities, such as path generation, dynamic planning, optimization, tracking, and control. This book has organized different planning topics into three general perspectives that are classified by the type of robotic applications. The chapters are a selection of recent developments in a) planning and tracking methods for unmanned aerial vehicles, b) heuristically based methods for navigation planning and routes optimization, and c) control techniques developed for path planning of autonomous wheeled platforms

A Consolidated Review of Path Planning and Optimization Techniques: Technical Perspectives and Future Directions

In this paper, a review on the three most important communication techniques (ground, aerial, and underwater vehicles) has been presented that throws light on trajectory planning, its optimization, and various issues in a summarized way. This kind of extensive research is not often seen in the literature, so an effort has been made for readers interested in path planning to fill the gap. Moreover, optimization techniques suitable for implementing ground, aerial, and underwater vehicles are also a part of this review. This paper covers the numerical, bio-inspired techniques and their hybridization with each other for each of the dimensions mentioned. The paper provides a consolidated platform, where plenty of available research on-ground autonomous vehicle and their trajectory optimization with the extension for aerial and underwater vehicles are documented

Optimizing the flight route of UAV using biology migration algorithm

Funding Information: This work is supported by the National Natural Science Foundation of China under Grants 62006103 and 61872168, in part by the Jiangsu national science research of high education under Grand 20KJB110021.Peer reviewedPublisher PD