Fast Second-order Cone Programming for Safe Mission Planning

This paper considers the problem of safe mission planning of dynamic systems operating under uncertain environments. Much of the prior work on achieving robust and safe control requires solving second-order cone programs (SOCP). Unfortunately, existing general purpose SOCP methods are often infeasible for real-time robotic tasks due to high memory and computational requirements imposed by existing general optimization methods. The key contribution of this paper is a fast and memory-efficient algorithm for SOCP that would enable robust and safe mission planning on-board robots in real-time. Our algorithm does not have any external dependency, can efficiently utilize warm start provided in safe planning settings, and in fact leads to significant speed up over standard optimization packages (like SDPT3) for even standard SOCP problems. For example, for a standard quadrotor problem, our method leads to speedup of 1000x over SDPT3 without any deterioration in the solution quality. Our method is based on two insights: a) SOCPs can be interpreted as optimizing a function over a polytope with infinite sides, b) a linear function can be efficiently optimized over this polytope. We combine the above observations with a novel utilization of Wolfe's algorithm to obtain an efficient optimization method that can be easily implemented on small embedded devices. In addition to the above mentioned algorithm, we also design a two-level sensing method based on Gaussian Process for complex obstacles with non-linear boundaries such as a cylinder

Real-time model predictive control for quadrotors

This paper presents a solution to on-board trajectory tracking control of quadrotors. The proposed approach combines the standard hierarchical control paradigm that separates the control into low-level motor control, mid-level attitude dynamics control, and a high-level trajectory tracking with a model predictive control strategy. We use dynamic reduction of the attitude dynamics and dynamic extension of the thrust control along with feedback linearisation to obtain a linear system of McMillan degree three that models force controlled position and trajectory tracking for the quadrotor. Model predictive control is then used on the feedback equivalent system and its control outputs are transformed back into the inputs for the original system. The proposed structure leads to a low complexity model predictive control algorithm that is implemented in real-time on an embedded hardware. Experimental results on different position and trajectory tracking control are presented to illustrate the application of the derived linear system and controllers

Piecewise Bézier curve trajectory generation and control for quadrotors

Quadrotors have the capability of being immensely useful vehicles to aid humans in labor intensive tasks. The critical challenge of using quadrotors inside homes is the efficient navigation of these vehicles in tight spaces while avoiding obstacles. Although methods exists to generate collision free trajectories, they often do not account for the dynamics of quadrotor. This thesis presents an approach for trajectory generation and control that can harness the complete dynamics of the quadrotor to achieve efficient navigation in cluttered spaces. First, the equations of motion for a quadrotor model is derived. It is also shown that the quadrotor system is differentially flat, which allows the analytical conversion of a time parameterized trajectory to states and outputs of the vehicle. Next, the thesis describes a control design approach in the non-Euclidean state space of quadrotors to achieve improved tracking performance for complex trajectories. A novel trajectory generation method is presented to achieve smooth and graceful paths for quadrotors. The trajectory generation is formulated as an optimization problem that generates piecewise Bézier curves which minimize snap over the complete trajectory. The optimization method generates these trajectories from collision-free waypoints indicative of the constrained environment. Further, the Bézier curves are time parameterized to satisfy the dynamic constraints and ensure feasibility

TRAJECTORY GENERATION BASED GUIDANCE AND CONTROL OF ROTORCRAFT UNMANNED AERIAL VEHICLES

Ph.DDOCTOR OF PHILOSOPH

Eyes in the sky: multi-drones surveillance technology

Neste projeto pretende-se desenvolver uma rede de segurança baseada no trabalho cooperativo entre vários UAVs. Sabendo que os UAVs podem variar na sua autonomia, velocidade de voo, estabilidade e muitos outros fatores, será feito um estudo onde tentaremos potenciar as melhores características para a rede de segurança a desenvolver. Em simultâneo com este estudo serão aplicados algoritmos de controlo de distribuição aos vários agentes para que a cobertura da área seja máxima. O resultado final esperado deste projeto é conseguir criar um miniprograma capaz de comunicar com vários agentes de patrulha, receber as suas localizações, calcular as suas posições ideais ou, no caso de não conseguirem cobrir por completo a área, calcular uma rota de patrulha e, enviar as informações calculadas. Esperamos também que este programa possa ser usado em simulação e se possível no terreno.In this project, we will develop a security network based on the cooperation between several UAVs. Knowing that UAV's autonomy, speed, stability and many other factors, a study will be made where we will leverage the best characteristics for our goals. Simultaneously, we will design and apply a coverage algorithm to control the distribution of the agents in the area to maximize their coverage. As result of this project we wish to have a mini-program capable of communicate with several agents, read their locations, calculate their optimal positions or patrolling routes, if they can't cover all the area with their sensor range, and send them the information needed. We also want this program to be at least simulated and if possible on the field

Adaptive and learning-based formation control of swarm robots

Autonomous aerial and wheeled mobile robots play a major role in tasks such as search and rescue, transportation, monitoring, and inspection. However, these operations are faced with a few open challenges including robust autonomy, and adaptive coordination based on the environment and operating conditions, particularly in swarm robots with limited communication and perception capabilities. Furthermore, the computational complexity increases exponentially with the number of robots in the swarm. This thesis examines two different aspects of the formation control problem. On the one hand, we investigate how formation could be performed by swarm robots with limited communication and perception (e.g., Crazyflie nano quadrotor). On the other hand, we explore human-swarm interaction (HSI) and different shared-control mechanisms between human and swarm robots (e.g., BristleBot) for artistic creation. In particular, we combine bio-inspired (i.e., flocking, foraging) techniques with learning-based control strategies (using artificial neural networks) for adaptive control of multi- robots. We first review how learning-based control and networked dynamical systems can be used to assign distributed and decentralized policies to individual robots such that the desired formation emerges from their collective behavior. We proceed by presenting a novel flocking control for UAV swarm using deep reinforcement learning. We formulate the flocking formation problem as a partially observable Markov decision process (POMDP), and consider a leader-follower configuration, where consensus among all UAVs is used to train a shared control policy, and each UAV performs actions based on the local information it collects. In addition, to avoid collision among UAVs and guarantee flocking and navigation, a reward function is added with the global flocking maintenance, mutual reward, and a collision penalty. We adapt deep deterministic policy gradient (DDPG) with centralized training and decentralized execution to obtain the flocking control policy using actor-critic networks and a global state space matrix. In the context of swarm robotics in arts, we investigate how the formation paradigm can serve as an interaction modality for artists to aesthetically utilize swarms. In particular, we explore particle swarm optimization (PSO) and random walk to control the communication between a team of robots with swarming behavior for musical creation

Distributed formation control for autonomous robots

This thesis addresses several theoretical and practical problems related to formation-control of autonomous robots. Formation-control aims to simultaneously accomplish the tasks of forming a desired shape by the robots and controlling their coordinated collective motion. This kind of robot performance is a cornerstone in the emerging field of swarm robotics, in particular with applications in precision agriculture, coverage of sport/art events, communication networks, area surveillance or vehicle platooning for energy efficiency and many others. One of the most important outcomes of this thesis is that the provided algorithms are completely distributed. This means that there is no central unit commanding the robots, but they have their own intelligence which allows them to make their own decisions based only on the local information. A distributed scheme entails a striking feature about the scalability and maintenance of a team of robots. Moreover, we also address the scenario of having wrongly calibrated sensors, which has a profound impact in the performance of the robots. The provided algorithms make the robots robust against such a practical and very common problem in real applications

ICTERI 2020: ІКТ в освіті, дослідженнях та промислових застосуваннях. Інтеграція, гармонізація та передача знань 2020: Матеріали 16-ї Міжнародної конференції. Том II: Семінари. Харків, Україна, 06-10 жовтня 2020 р.

This volume represents the proceedings of the Workshops co-located with the 16th International Conference on ICT in Education, Research, and Industrial Applications, held in Kharkiv, Ukraine, in October 2020. It comprises 101 contributed papers that were carefully peer-reviewed and selected from 233 submissions for the five workshops: RMSEBT, TheRMIT, ITER, 3L-Person, CoSinE, MROL. The volume is structured in six parts, each presenting the contributions for a particular workshop. The topical scope of the volume is aligned with the thematic tracks of ICTERI 2020: (I) Advances in ICT Research; (II) Information Systems: Technology and Applications; (III) Academia/Industry ICT Cooperation; and (IV) ICT in Education.Цей збірник представляє матеріали семінарів, які були проведені в рамках 16-ї Міжнародної конференції з ІКТ в освіті, наукових дослідженнях та промислових застосуваннях, що відбулася в Харкові, Україна, у жовтні 2020 року. Він містить 101 доповідь, які були ретельно рецензовані та відібрані з 233 заявок на участь у п'яти воркшопах: RMSEBT, TheRMIT, ITER, 3L-Person, CoSinE, MROL. Збірник складається з шести частин, кожна з яких представляє матеріали для певного семінару. Тематична спрямованість збірника узгоджена з тематичними напрямками ICTERI 2020: (I) Досягнення в галузі досліджень ІКТ; (II) Інформаційні системи: Технології і застосування; (ІІІ) Співпраця в галузі ІКТ між академічними і промисловими колами; і (IV) ІКТ в освіті