Aerial Remote Sensing in Agriculture: A Practical Approach to Area Coverage and Path Planning for Fleets of Mini Aerial Robots

In this paper, a system that allows applying precision agriculture techniques is described. The application is based on the deployment of a team of unmanned aerial vehicles that are able to take georeferenced pictures in order to create a full map by applying mosaicking procedures for postprocessing. The main contribution of this work is practical experimentation with an integrated tool. Contributions in different fields are also reported. Among them is a new one-phase automatic task partitioning manager, which is based on negotiation among the aerial vehicles, considering their state and capabilities. Once the individual tasks are assigned, an optimal path planning algorithm is in charge of determining the best path for each vehicle to follow. Also, a robust flight control based on the use of a control law that improves the maneuverability of the quadrotors has been designed. A set of field tests was performed in order to analyze all the capabilities of the system, from task negotiations to final performance. These experiments also allowed testing control robustness under different weather conditions

Reinforcement Learning and Planning for Preference Balancing Tasks

Robots are often highly non-linear dynamical systems with many degrees of freedom, making solving motion problems computationally challenging. One solution has been reinforcement learning (RL), which learns through experimentation to automatically perform the near-optimal motions that complete a task. However, high-dimensional problems and task formulation often prove challenging for RL. We address these problems with PrEference Appraisal Reinforcement Learning (PEARL), which solves Preference Balancing Tasks (PBTs). PBTs define a problem as a set of preferences that the system must balance to achieve a goal. The method is appropriate for acceleration-controlled systems with continuous state-space and either discrete or continuous action spaces with unknown system dynamics. We show that PEARL learns a sub-optimal policy on a subset of states and actions, and transfers the policy to the expanded domain to produce a more refined plan on a class of robotic problems. We establish convergence to task goal conditions, and even when preconditions are not verifiable, show that this is a valuable method to use before other more expensive approaches. Evaluation is done on several robotic problems, such as Aerial Cargo Delivery, Multi-Agent Pursuit, Rendezvous, and Inverted Flying Pendulum both in simulation and experimentally. Additionally, PEARL is leveraged outside of robotics as an array sorting agent. The results demonstrate high accuracy and fast learning times on a large set of practical applications

Quadrotor team modeling and control for DLO transportation

94 p.Esta Tesis realiza una propuesta de un modelado dinámico para el transporte de sólidos lineales deformables (SLD) mediante un equipo de cuadricópteros. En este modelo intervienen tres factores: - Modelado dinámico del sólido lineal a transportar. - Modelo dinámico del cuadricóptero para que tenga en cuenta la dinámica pasiva y los efectos del SLD. - Estrategia de control para un transporte e ciente y robusto. Diferenciamos dos tareas principales: (a) lograr una con guración cuasiestacionaria de una distribución de carga equivalente a transportar entre todos los robots. (b) Ejecutar el transporte en un plano horizontal de todo el sistema. El transporte se realiza mediante una con guración de seguir al líder en columna, pero los cuadricópteros individualmente tienen que ser su cientemente robustos para afrontar todas las no-linealidades provocadas por la dinámica del SLD y perturbaciones externas, como el viento. Los controladores del cuadricóptero se han diseñado para asegurar la estabilidad del sistema y una rápida convergencia del sistema. Se han comparado y testeado estrategias de control en tiempo real y no-real para comprobar la bondad y capacidad de ajuste a las condiciones dinámicas cambiantes del sistema. También se ha estudiado la escalabilidad del sistema

Comprehensive review on controller for leader-follower robotic system

985-1007This paper presents a comprehensive review of the leader-follower robotics system. The aim of this paper is to find and elaborate on the current trends in the swarm robotic system, leader-follower, and multi-agent system. Another part of this review will focus on finding the trend of controller utilized by previous researchers in the leader-follower system. The controller that is commonly applied by the researchers is mostly adaptive and non-linear controllers. The paper also explores the subject of study or system used during the research which normally employs multi-robot, multi-agent, space flying, reconfigurable system, multi-legs system or unmanned system. Another aspect of this paper concentrates on the topology employed by the researchers when they conducted simulation or experimental studies

Real-time UAV Complex Missions Leveraging Self-Adaptive Controller with Elastic Structure

The expectation of unmanned air vehicles (UAVs) pushes the operation environment to narrow spaces, where the systems may fly very close to an object and perform an interaction. This phase brings the variation in UAV dynamics: thrust and drag coefficient of the propellers might change under different proximity. At the same time, UAVs may need to operate under external disturbances to follow time-based trajectories. Under these challenging conditions, a standard controller approach may not handle all missions with a fixed structure, where there may be a need to adjust its parameters for each different case. With these motivations, practical implementation and evaluation of an autonomous controller applied to a quadrotor UAV are proposed in this work. A self-adaptive controller based on a composite control scheme where a combination of sliding mode control (SMC) and evolving neuro-fuzzy control is used. The parameter vector of the neuro-fuzzy controller is updated adaptively based on the sliding surface of the SMC. The autonomous controller possesses a new elastic structure, where the number of fuzzy rules keeps growing or get pruned based on bias and variance balance. The interaction of the UAV is experimentally evaluated in real time considering the ground effect, ceiling effect and flight through a strong fan-generated wind while following time-based trajectories.Comment: 18 page

Aerial Vehicles

This book contains 35 chapters written by experts in developing techniques for making aerial vehicles more intelligent, more reliable, more flexible in use, and safer in operation.It will also serve as an inspiration for further improvement of the design and application of aeral vehicles. The advanced techniques and research described here may also be applicable to other high-tech areas such as robotics, avionics, vetronics, and space