Online 3D path planning for Tri-copter drone using GWO-IBA algorithm

Robots at present are involved in many parts of life, especially mobile robots, which are two parts, ground robots and flying robots, and the best example of a flying robot is the drone. Path planning is a fundamental part of UAVs because the drone follows the path that leads it to goal with obstacle avoidance. Therefore, this paper proposes a hybrid algorithm (grey wolf optimization - intelligent bug algorithm GWO-IBA) to determine the best, shortest and without obstacles path. The hybrid algorithm was implemented and tested in the MATLAB program on the Tri-copter model, and it gave different paths in different environments. The paths obtained were characterized by being free of obstacles and the shortest paths available to reach the target

Comparative Analysis of Control Barrier Functions and Artificial Potential Fields for Obstacle Avoidance

Artificial potential fields (APFs) and their variants have been a staple for collision avoidance of mobile robots and manipulators for almost 40 years. Its model-independent nature, ease of implementation, and real-time performance have played a large role in its continued success over the years. Control barrier functions (CBFs), on the other hand, are a more recent development, commonly used to guarantee safety for nonlinear systems in real-time in the form of a filter on a nominal controller. In this paper, we address the connections between APFs and CBFs. At a theoretic level, we prove that APFs are a special case of CBFs: given a APF one obtains a CBFs, while the converse is not true. Additionally, we prove that CBFs obtained from APFs have additional beneficial properties and can be applied to nonlinear systems. Practically, we compare the performance of APFs and CBFs in the context of obstacle avoidance on simple illustrative examples and for a quadrotor, both in simulation and on hardware using onboard sensing. These comparisons demonstrate that CBFs outperform APFs

Artificial Potential Field Algorithm for Obstacle Avoidance in UAV Quadrotor for Dynamic Environment

Artificial potential field (APF) is the effective real-time guide, navigation, and obstacle avoidance for UAV Quadrotor. The main problem in APF is local minima in an obstacle or multiple obstacles. In this paper, some modifications and improvements of APF will be introduced to solve one-obstacle local minima, two-obstacle local minima, Goal Not Reachable Near Obstacle (GNRON), and dynamic obstacle. The result shows that the improved APF gave the best result because it made the system reach the goal position in all of the examinations. Meanwhile, the APF with virtual force has the fastest time to reach the goal; however, it still has a problem in GNRON. It can be concluded that the APF needs to be modified in its algorithm to pass all of the local minima problems

Autonomous Rendezvous with Non-cooperative Target Objects with Swarm Chasers and Observers

Space debris is on the rise due to the increasing demand for spacecraft for com-munication, navigation, and other applications. The Space Surveillance Network (SSN) tracks over 27,000 large pieces of debris and estimates the number of small, un-trackable fragments at over 1,00,000. To control the growth of debris, the for-mation of further debris must be reduced. Some solutions include deorbiting larger non-cooperative resident space objects (RSOs) or servicing satellites in or-bit. Both require rendezvous with RSOs, and the scale of the problem calls for autonomous missions. This paper introduces the Multipurpose Autonomous Ren-dezvous Vision-Integrated Navigation system (MARVIN) developed and tested at the ORION Facility at Florida Institution of Technology. MARVIN consists of two sub-systems: a machine vision-aided navigation system and an artificial po-tential field (APF) guidance algorithm which work together to command a swarm of chasers to safely rendezvous with the RSO. We present the MARVIN architec-ture and hardware-in-the-loop experiments demonstrating autonomous, collabo-rative swarm satellite operations successfully guiding three drones to rendezvous with a physical mockup of a non-cooperative satellite in motion.Comment: Presented at AAS/AIAA Spaceflight Mechanics Meeting 2023, 17 pages, 9 figures, 3 table

Enfoque de navegación global para un robot asistente

Context: This work shows a novel navigation approach based on images for an assistant hybrid robot composed by a humanoid and an omnidirectional platform.Method: This approach introduces a complex space analysis, using Zeros and Poles attraction-repulsion principle. In order to perform the algorithm, an integrated system is developed; this system includes: an external camera to take a global navigation surface view, the assistant robot, and communication devices. Navigation is supported by some digital image processing algorithms and performed using the root location technique.Results: An integrated system of global navigation with external sensors was successfully implemented for the proposed hybrid robot.Conclusions: Some simulation and experimental tests will be discussed in order to validate this proposal and the whole system. Additionally, some suggestions for future research are proposed.Contexto: En este trabajo se muestra un enfoque de navegación novedoso basado en imágenes para un robot asistente híbrido compuesto por un humanoide y una plataforma omnidireccional.Método: Este enfoque presenta un análisis del espacio complejo, usando el principio de atracción y repulsión de polos y ceros. Para desarrollar el algoritmo se desarrolla un sistema integrado, el cual incluye: una cámara externa (para tomar la vista de la superficie global de navegación), el robot asistente, y los algunos dispositivos de comunicación. La navegación está soportada por algoritmos de procesamiento digital de imágenes y llevada a cabo usando la técnica de localización de raíces.Resultados: Se obtuvo un sistema integrado de navegación global con sensórica externa para el robot híbrido propuesto.Conclusiones: Algunas simulaciones y pruebas experimentales se discuten con el fin de validar esta propuesta y el sistema entero. También se dan sugerencias para trabajos futuros

Impact angle control guidance synthesis for evasive maneuver against intercept missile

This paper proposes a synthesis of new guidance law to generate an evasive maneuver against enemy’s missile interception while considering its impact angle, acceleration, and field-of-view constraints. The first component of the synthesis is a new function of repulsive Artificial Potential Field to generate the evasive maneuver as a real-time dynamic obstacle avoidance. The terminal impact angle and terminal acceleration constraints compliance are based on Time-to-Go Polynomial Guidance as the second component. The last component is the Logarithmic Barrier Function to satisfy the field-of-view limitation constraint by compensating the excessive total acceleration command. These three components are synthesized into a new guidance law, which involves three design parameter gains. Parameter study and numerical simulations are delivered to demonstrate the performance of the proposed repulsive function and guidance law. Finally, the guidance law simulations effectively achieve the zero terminal miss distance, while satisfying an evasive maneuver against intercept missile, considering impact angle, acceleration, and field-of-view limitation constraints simultaneously

UAV path planning using artificial potential field method updated by optimal control theory

The unmanned aerial vehicle (UAV) path planning problem is an important assignment in the UAV mission planning. Based on the artificial potential field (APF) UAV path planning method, it is reconstructed into the constrained optimisation problem by introducing an additional control force. The constrained optimisation problem is translated into the unconstrained optimisation problem with the help of slack variables in this paper. The functional optimisation method is applied to reform this problem into an optimal control problem. The whole transformation process is deduced in detail, based on a discrete UAV dynamic model. Then, the path planning problem is solved with the help of the optimal control method. The path following process based on the six degrees of freedom simulation model of the quadrotor helicopters is introduced to verify the practicability of this method. Finally, the simulation results show that the improved method is more effective in planning path. In the planning space, the length of the calculated path is shorter and smoother than that using traditional APF method. In addition, the improved method can solve the dead point problem effectively

Kontrol Formasi Kooperatif dan Penghindaran Rintangan pada Multiple Unmanned Aerial Vehicle dengan Guidance Route dan Artificial Potential Field

Dalam beberapa tahun terakhir, kontrol kooperatif sistem multi-UAV (Unmanned Aerial Vehicle) telah menjadi topik penelitian yang hangat di bidang kontrol penerbangan. Diantaranya, pengendalian formasi dan penghindaran rintangan adalah salah dua tema yang penting untuk diteliti karena kompleksitas kondisi permasalahan yang ingin diselesaikan selalu meningkat seiring waktu. Problema riil ini dapat dimodelkan sebagai permasalahan kontrol penghindaran rintangan pada formasi quadcopter. Sekelompok quadcopter ditugaskan untuk membentuk formasi (berupa bentuk V), bergerak dalam formasi menuju titik tujuan, menghindari tabrakan antar robot, dan menghindari tabrakan dengan rintangan. Model quadcopter yang digunakan adalah Quanser Qdrone dengan enam derajat kebebasan. Quadcopter dikontrol menggunakan fuzzy state feedback controller untuk melacak tujuan. Pada tugas akhir ini dirancang suatu sistem pengaturan formasi menggunakan pendekatan guidance route dengan penghindaran rintangan menggunakan metode Artificial Potential Field (APF). Selain itu, akan dibandingkan dua strategi penghindaran, penghindaran total dan penghindaran minimal. Berdasarkan hasil simulasi, algoritma kontrol yang dikembangkan berhasil melaksanakan tugas pengaturan formasi dan penghindaran rintangan pada sekelompok quadcopter. Hal ini dibuktikan dengan rata-rata indeks performansi formasi bernilai 0.800025 untuk strategi penghindaran total dan 1.2227125 untuk strategi penghindaran minimal serta trayektori masing-masing quadcopter yang bebas tabrakan. ============================================================================================== In recent years, cooperative control of multi-UAV (Unmanned Aerial Vehicle) systems has become a hot research topic in the field of flight control. Among them, formation control and obstacle avoidance are two important themes to study because the complexity of the problem conditions to be solved always increases with time. This real problem can be modeled as an obstacle avoidance control problem in a quadcopter formation. A group of quadcopters is assigned to form a formation (in the form of a V shape), move in formation towards a destination point, avoid collisions between robots, and avoid collisions with obstacles. The quadcopter model used is the Quanser Qdrone with six degrees of freedom. The quadcopter is controlled using a fuzzy state feedback controller to track objectives. In this final project, a formation management system is designed using the guidance route approach with obstacle avoidance using the Artificial Potential Field (APF) method. Moreover, two avoidance strategies will be compared, total avoidance and minimum avoidance. Based on the simulation results, the developed control algorithm successfully performs the task of setting formation and obstacle avoidance on a group of quadcopters. This is evidenced by the average formation performance index of 0.800025 for total avoidance strategy and 1.2227125 for minimum avoidance strategy with the collision-free trajectories of each quadcopter

Mission-based mobility models for UAV networks

Las redes UAV han atraído la atención de los investigadores durante la última década. Las numerosas posibilidades que ofrecen los sistemas single-UAV aumentan considerablemente al usar múltiples UAV. Sin embargo, el gran potencial del sistema multi-UAV viene con un precio: la complejidad de controlar todos los aspectos necesarios para garantizar que los UAVs cumplen la misión que se les ha asignado. Ha habido numerosas investigaciones dedicadas a los sistemas multi-UAV en el campo de la robótica en las cuales se han utilizado grupos de UAVs para diferentes aplicaciones. Sin embargo, los aspectos relacionados con la red que forman estos sistemas han comenzado a reclamar un lugar entre la comunidad de investigación y han hecho que las redes de UAVs se consideren como un nuevo paradigma entre las redes multi-salto. La investigación de redes de UAVs, de manera similar a otras redes multi-salto, se divide principalmente en dos categorías: i) modelos de movilidad que capturan la movilidad de la red, y ii) algoritmos de enrutamiento. Ambas categorías han heredado muchos algoritmos que pertenecían a las redes MANET, que fueron el primer paradigma de redes multi-salto que atrajo la atención de los investigadores. Aunque hay esfuerzos de investigación en curso que proponen soluciones para ambas categorías, el número de modelos de movilidad y algoritmos de enrutamiento específicos para redes UAV es limitado. Además, en el caso de los modelos de movilidad, las soluciones existentes propuestas son simplistas y apenas representan la movilidad real de un equipo de UAVs, los cuales se utilizan principalmente en operaciones orientadas a misiones, en la que cada UAV tiene asignados movimientos específicos. Esta tesis propone dos modelos de movilidad basados en misiones para una red de UAVs que realiza dos operaciones diferentes. El escenario elegido en el que se desarrollan las misiones corresponde con una región en la que ha ocurrido, por ejemplo, un desastre natural. La elección de este tipo de escenario se debe a que en zonas de desastre, la infraestructura de comunicaciones comúnmente está dañada o totalmente destruida. En este tipo de situaciones, una red de UAVs ofrece la posibilidad de desplegar rápidamente una red de comunicaciones. El primer modelo de movilidad, llamado dPSO-U, ha sido diseñado para capturar la movilidad de una red UAV en una misión con dos objetivos principales: i) explorar el área del escenario para descubrir las ubicaciones de los nodos terrestres, y ii) hacer que los UAVs converjan de manera autónoma a los grupos en los que se organizan los nodos terrestres (también conocidos como clusters). El modelo de movilidad dPSO-U se basa en el conocido algoritmo particle swarm optimization (PSO), considerando los UAV como las partículas del algoritmo, y también utilizando el concepto de valores dinámicos para la inercia, el local best y el neighbour best de manera que el modelo de movilidad tenga ambas capacidades: la de exploración y la de convergencia. El segundo modelo, denominado modelo de movilidad Jaccard-based, captura la movilidad de una red UAV que tiene asignada la misión de proporcionar servicios de comunicación inalámbrica en un escenario de mediano tamaño. En este modelo de movilidad se ha utilizado una combinación del virtual forces algorithm (VFA), de la distancia Jaccard entre cada par de UAVs y metaheurísticas como hill climbing y simulated annealing, para cumplir los dos objetivos de la misión: i) maximizar el número de nodos terrestres (víctimas) que se encuentran bajo el área de cobertura inalámbrica de la red UAV, y ii) mantener la red UAV como una red conectada, es decir, evitando las desconexiones entre UAV. Se han realizado simulaciones exhaustivas con herramientas software específicamente desarrolladas para los modelos de movilidad propuestos. También se ha definido un conjunto de métricas para cada modelo de movilidad. Estas métricas se han utilizado para validar la capacidad de los modelos de movilidad propuestos de emular los movimientos de una red UAV en cada misión.UAV networks have attracted the attention of the research community in the last decade. The numerous capabilities of single-UAV systems increase considerably by using multiple UAVs. The great potential of a multi-UAV system comes with a price though: the complexity of controlling all the aspects required to guarantee that the UAV team accomplish the mission that it has been assigned. There have been numerous research works devoted to multi-UAV systems in the field of robotics using UAV teams for different applications. However, the networking aspects of multi-UAV systems started to claim a place among the research community and have made UAV networks to be considered as a new paradigm among the multihop ad hoc networks. UAV networks research, in a similar manner to other multihop ad hoc networks, is mainly divided into two categories: i) mobility models that capture the network mobility, and ii) routing algorithms. Both categories have inherited previous algorithms mechanisms that originally belong to MANETs, being these the first multihop networking paradigm attracting the attention of researchers. Although there are ongoing research efforts proposing solutions for the aforementioned categories, the number of UAV networks-specific mobility models and routing algorithms is limited. In addition, in the case of the mobility models, the existing solutions proposed are simplistic and barely represent the real mobility of a UAV team, which are mainly used in missions-oriented operations. This thesis proposes two mission-based mobility models for a UAV network carrying out two different operations over a disaster-like scenario. The reason for selecting a disaster scenario is because, usually, the common communication infrastructure is malfunctioning or completely destroyed. In these cases, a UAV network allows building a support communication network which is rapidly deployed. The first mobility model, called dPSO-U, has been designed for capturing the mobility of a UAV network in a mission with two main objectives: i) exploring the scenario area for discovering the location of ground nodes, and ii) making the UAVs to autonomously converge to the groups in which the nodes are organized (also referred to as clusters). The dPSO-U mobility model is based on the well-known particle swarm optimization algorithm (PSO), considering the UAVs as the particles of the algorithm, and also using the concept of dynamic inertia, local best and neighbour best weights so the mobility model can have both abilities: exploration and convergence. The second one, called Jaccard-based mobility model, captures the mobility of a UAV network that has been assigned with the mission of providing wireless communication services in a medium-scale scenario. A combination of the virtual forces algorithm (VFA), the Jaccard distance between each pair of UAVs and metaheuristics such as hill climbing or simulated annealing have been used in this mobility model in order to meet the two mission objectives: i) to maximize the number of ground nodes (i.e. victims) under the UAV network wireless coverage area, and ii) to maintain the UAV network as a connected network, i.e. avoiding UAV disconnections. Extensive simulations have been performed with software tools that have been specifically developed for the proposed mobility models. Also, a set of metrics have been defined and measured for each mobility model. These metrics have been used for validating the ability of the proposed mobility models to emulate the movements of a UAV network in each mission