Decision-Theoretic Planning with Person Trajectory Prediction for Social Navigation

Robots navigating in a social way should reason about people intentions when acting. For instance, in applications like robot guidance or meeting with a person, the robot has to consider the goals of the people. Intentions are inherently nonobservable, and thus we propose Partially Observable Markov Decision Processes (POMDPs) as a decision-making tool for these applications. One of the issues with POMDPs is that the prediction models are usually handcrafted. In this paper, we use machine learning techniques to build prediction models from observations. A novel technique is employed to discover points of interest (goals) in the environment, and a variant of Growing Hidden Markov Models (GHMMs) is used to learn the transition probabilities of the POMDP. The approach is applied to an autonomous telepresence robot

Threat Management Methodology for Unmanned Aerial Systems operating in the U-space

This paper presents a threat management methodology for Unmanned Aircraft Systems (UAS) operating in the civil airspace. The work is framed within an Unmanned Traffic Management (UTM) system based on the U-space initiative. We propose a new method that focuses on providing the required automated decision-making during real-time threat management and conflict resolution, which is one of the main gaps in the current U-space ecosystem. Our method is capable of handling all commonplace UTM threats, as well as selecting optimal mitigation actions, trading off efficiency and safety. Our implementation is open-source and fully integrated in a UTM software architecture, implementing U-space services related to emergency management and tactical deconfliction. We demonstrate our methodology through a set of realistic use cases with actual UAS operating in civil airspace. For that, we performed field experiments in an aerodrome with segregated airspace, and we showcased that the methodology is capable of autonomously managing heterogeneous threats in real time.Unión Europea - Horizonte 2020 77629

Study of the electronic structure of electron accepting cyano-films: TCNQ: Versus TCNE

In this article, we perform systematic research on the electronic structure of two closely related organic electron acceptor molecules (TCNQ and TCNE), which are of technological interest due to their outstanding electronic properties. These studies have been performed from the experimental point of view by the use electron spectroscopies (XPS and UPS) and supported theoretically by the use of ab-initio DFT calculations. The cross-check between both molecules allows us to identify the characteristic electronic features of each part of the molecules and their contribution to the final electronic structure. We can describe the nature of the band gap of these materials, and we relate this with the appearance of the shake-up features in the core level spectra. A band bending and energy gap reduction of the aforementioned electronic structure in contact with a metal surface are seen in the experimental results as well in the theoretical calculations. This behavior implies that the TCNQ thin film accepts electrons from the metal substrate becoming a Schottky n-junctionThis work was supported by the Spanish MICyT under grants No. FIS2016-74893-P and MAT2013-47869-C4-3-

Optimal Trajectory Planning for Cinematography with Multiple Unmanned Aerial Vehicles

This paper presents a method for planning optimal trajectories with a team of Unmanned Aerial Vehicles (UAVs) performing autonomous cinematography. The method is able to plan trajectories online and in a distributed manner, providing coordination between the UAVs. We propose a novel non-linear formulation for this challenging problem of computing multi-UAV optimal trajectories for cinematography; integrating UAVs dynamics and collision avoidance constraints, together with cinematographic aspects like smoothness, gimbal mechanical limits and mutual camera visibility. We integrate our method within a hardware and software architecture for UAV cinematography that was previously developed within the framework of the MultiDrone project; and demonstrate its use with different types of shots filming a moving target outdoors. We provide extensive experimental results both in simulation and field experiments. We analyze the performance of the method and prove that it is able to compute online smooth trajectories, reducing jerky movements and complying with cinematography constraints.Comment: This paper has been published as: Optimal trajectory planning for cinematography with multiple Unmanned Aerial Vehicles. Alfonso Alcantara and Jesus Capitan and Rita Cunha and Anibal Ollero. Robotics and Autonomous Systems. 103778 (2021) 10.1016/j.robot.2021.10377

Decentralized 3D Collision Avoidance for Multiple UAVs in Outdoor Environments

The use of multiple aerial vehicles for autonomous missions is turning into commonplace. In many of these applications, the Unmanned Aerial Vehicles (UAVs) have to cooperate and navigate in a shared airspace, becoming 3D collision avoidance a relevant issue. Outdoor scenarios impose additional challenges: (i) accurate positioning systems are costly; (ii) communication can be unreliable or delayed; and (iii) external conditions like wind gusts affect UAVs’ maneuverability. In this paper, we present 3D-SWAP, a decentralized algorithm for 3D collision avoidance with multiple UAVs. 3D-SWAP operates reactively without high computational requirements and allows UAVs to integrate measurements from their local sensors with positions of other teammates within communication range. We tested 3D-SWAP with our team of custom-designed UAVs. First, we used a Software-In-The-Loop simulator for system integration and evaluation. Second, we run field experiments with up to three UAVs in an outdoor scenario with uncontrolled conditions (i.e., noisy positioning systems, wind gusts, etc). We report our results and our procedures for this field experimentation.European Union’s Horizon 2020 research and innovation programme No 731667 (MULTIDRONE

Relación entre el nivel de liderazgo del director y el clima institucional de la Asociación Educativa Particular Liceo Santo Domingo 2011

El presente estudio tuvo por objetivo demostrar la relación entre el nivel de liderazgo del director y el clima institucional de la asociación educativa particular Liceo Santo Domingo 2011. Dicho estudio empleo la metodología descriptiva de diseño no experimental, transversal. La población estuvo constituida por los docentes de la asociación educativa particular Liceo Santo Domingo 2011. Se utilizó una muestra censal. Para construir, validar y demostrar la confiabilidad de los instrumentos se ha considerado la validez de contenido, mediante la Técnica de Opinión de Expertos y su instrumento es el informe de juicio de Expertos de las variables de estudio; se utilizó la técnica de la encuesta y su instrumento el cuestionario, con preguntas tipo Escala de Likert. Para la confiabilidad de los instrumentos se usó Alpha de Cronbach, se utilizó encuestas para determinar si hay relación entre el nivel de liderazgo del director y el clima institucional de la asociación educativa particular Liceo Santo Domingo 2011. Concluyéndose que el nivel de liderazgo del director influye en el clima institucional en la asociación educativa particular Liceo Santo Domingo 2011, cuyo coeficiente de 84.9% y un nivel de significancia p=0.000

An extension of GHMMs for environments with occlusions and automatic goal discovery for person trajectory prediction

This work is partially funded by the EC-FP7 under grant agreement no. 611153 (TERESA) and the project PAIS-MultiRobot, funded by the Junta de Andalucía (TIC-7390). I. Perez-Hurtado is also supported by the Postdoctoral Junior Grant 2013 co-funded by the Spanish Ministry of Economy and Competitiveness and the Pablo de Olavide University.Robots navigating in a social way should use some knowledge about common motion patterns of people in the environment. Moreover, it is known that people move intending to reach certain points of interest, and machine learning techniques have been widely used for acquiring this knowledge by observation. Learning algorithms such as Growing Hidden Markov Models (GHMMs) usually assume that points of interest are located at the end of human trajectories, but complete trajectories cannot always be observed by a mobile robot due to occlusions and people going out of sensor range. This paper extends GHMMs to deal with partial observed trajectories where people's goals are not known a priori. A novel technique based on hypothesis testing is also used to discover the points of interest (goals) in the environment. The approach is validated by predicting people's motion in three different datasets.Universidad Pablo de Olavide. Departamento de Deporte e InformáticaPostprin

Autonomous Execution of Cinematographic Shots with Multiple Drones

This paper presents a system for the execution of autonomous cinematography missions with a team of drones. The system allows media directors to design missions involving different types of shots with one or multiple cameras, running sequentially or concurrently. We introduce the complete architecture, which includes components for mission design, planning and execution. Then, we focus on the components related to autonomous mission execution. First, we propose a novel parametric description for shots, considering different types of camera motion and tracked targets; and we use it to implement a set of canonical shots. Second, for multi-drone shot execution, we propose distributed schedulers that activate different shot controllers on board the drones. Moreover, an event-based mechanism is used to synchronize shot execution among the drones and to account for inaccuracies during shot planning. Finally, we showcase the system with field experiments filming sport activities, including a real regatta event. We report on system integration and lessons learnt during our experimental campaigns

From Perception to Navigation in Environments with Persons: An Indoor Evaluation of the State of the Art

Research in the field of social robotics is allowing service robots to operate in environments with people. In the aim of realizing the vision of humans and robots coexisting in the same environment, several solutions have been proposed to (1) perceive persons and objects in the immediate environment; (2) predict the movements of humans; as well as (3) plan the navigation in agreement with socially accepted rules. In this work, we discuss the different aspects related to social navigation in the context of our experience in an indoor environment. We describe state-of-the-art approaches and experiment with existing methods to analyze their performance in practice. From this study, we gather first-hand insights into the limitations of current solutions and identify possible research directions to address the open challenges. In particular, this paper focuses on topics related to perception at the hardware and application levels, including 2D and 3D sensors, geometric and mainly semantic mapping, the prediction of people trajectories (physics-, pattern- and planning-based), and social navigation (reactive and predictive) in indoor environments

Data fusion in ubiquitous networked robot systems for urban services

There is a clear trend in the use of robots to accomplish services that can help humans. In this paper, robots acting in urban environments are considered for the task of person guiding. Nowadays, it is common to have ubiquitous sensors integrated within the buildings, such as camera networks, and wireless communications like 3G or WiFi. Such infrastructure can be directly used by robotic platforms. The paper shows how combining the information from the robots and the sensors allows tracking failures to be overcome, by being more robust under occlusion, clutter, and lighting changes. The paper describes the algorithms for tracking with a set of fixed surveillance cameras and the algorithms for position tracking using the signal strength received by a wireless sensor network (WSN). Moreover, an algorithm to obtain estimations on the positions of people from cameras on board robots is described. The estimate from all these sources are then combined using a decentralized data fusion algorithm to provide an increase in performance. This scheme is scalable and can handle communication latencies and failures. We present results of the system operating in real time on a large outdoor environment, including 22 nonoverlapping cameras, WSN, and several robots.Universidad Pablo de Olavide. Departamento de Deporte e InformáticaPostprin