Forming friendship with peers from diverse backgrounds is key to children’s social emotional development. In this study, we explored the use of social robot, Haru, as mediator for remote communication in children group, to support connection and friendship building. We invited children from different countries aged from 10 to 15 to participate in two interaction sessions with peers from other countries, after which we conducted interviews with children from three countries, focusing on their experiences, and perceptions of the robot’s roles in the process. The findings indicated that social robot Haru effectively served as an icebreaker and entertainer; However, improvements are needed in conversation flow, transitions between different roles, and supporting children’s autonomy in guiding the conversation and the depth of their communication.

This paper presents a 3D safe heuristic planner for aerial robot indoor navigation incorporating semantic information of the environment. Our interest lies in how the inclusion of semantic information in industrial environments or buildings can positively affect planning to ensure 3D safe navigation within the environment. The planner is based on an any-angle path planning algorithm, particularly Lazy Theta* algorithm. There are many works in the literature that include non-uniform costs related to distance to the closest obstacles in order to achieve a safe planner, but they generally do not consider the semantic information which is relevant in factories. Semantic-aware planning opens the door to safer plans, adapting the distance to obstacles depending on the category of the objects. Our Semantic Heuristic planner considers the semantic information of all the nodes along the line of sight between an initial and final node. Tests in a 3D environment with corridors, stairs, doors, columns and walls are performed to evaluate the proposed planner with respect to Lazy Theta* algorithms and a cost-aware Lazy-Theta* over Euclidean distance functions. The results show advantages of the Semantic Heuristic planner in terms of safety depending on the type of obstacle, the length of the path and efficiency with respect to the cost-aware Lazy-Theta* which only considers the distance to obstacles in its cost function.

Social robots for children have focused mainly on conventional education domains such as teaching language, science, and math, while applications focusing on the enhancement of cultural competency are quite scarce. In this paper, we present a prototype of a robot-mediation framework for cross-cultural communication. This framework paves the way for a social robot to act as a mediator between groups of schoolchildren from different countries. First, we conducted a participatory design activity by an interdisciplinary team, resulting in the extraction of the design, robot’s roles, and technical requirements. Based on these requirements, we built the robot-mediation system prototype. We conducted a pilot study using the system with groups of high school children in Japan and Australia and our results show the potential of the system to drive children’s interest in communicating, sharing, and discussing cultural themes with their remote peers through the social robot.

Human-aware navigation is a complex task for mobile robots, requiring an autonomous navigation system capable of achieving efficient path planning together with socially compliant behaviors. Social planners usually add costs or constraints to the objective function, leading to intricate tuning processes or tailoring the solution to the specific social scenario. Machine Learning can enhance planners’ versatility and help them learn complex social behaviors from data. This work proposes an adaptive social planner, using a Deep Reinforcement Learning agent to dynamically adjust the weighting parameters of the cost function used to evaluate trajectories. The resulting planner combines the robustness of the classic Dynamic Window Approach, integrated with a social cost based on the Social Force Model, and the flexibility of learning methods to boost the overall performance on social navigation tasks. Our extensive experimentation on different environments demonstrates the general advantage of the proposed method over static cost planners.

In this paper, we identify a set of socio-emotional cues and signals that are promoted by a tabletop social mediator robot in the context of a school setting. The robot adopts different roles to enhance such socio-emotional features, consequently aiding their identification by a structured annotation system. Various socio-emotional signals were observed for different robot roles, as well as different cues (gaze, speech). Future work will analyze cultural nuances as it expands the pilot to more schools worldwide.

The development and integration of robots capable of expressing gaze directionality through eye-head movements are crucial for effective human-robot interaction, especially for those with eye designs on 2D screens. Our proposed mutual eye-head gaze model aligns eye movements with head/body rotation, incorporating an attention engine for estimating the most saliency location, and a retina-fovea engine for precise gaze alignment. Additionally, the eye-head engine controls head movements, enhancing the robot’s ability to perform responsive coordinated eye-head gaze behaviors. This improvement leads to enhanced human subjective perception of direct gaze from the robot, ultimately holding potential for advancing human-robot interaction in social dynamics and human-centered robot development research.

This work presents a framework for a visual-auditory attention-driven robot eye-head gaze movement, which combines visual and auditory inputs to determine the direction of gaze movement for a social robot. The framework computes the most salient changes in position by considering both visual and auditory cues. The proposed system was implemented on Haru, a tabletop social robot, where eye-head gaze movement was controlled using visual input from a camera positioned above the eyes and auditory input from a seven-channel microphone. This allowed for eye movement on a two-dimensional flat screen and body rotation towards the person who is speaking. This framework provides a representation of the robot’s attentional gaze that leverages both visual and auditory cues, resulting in more natural and responsive coordinated eye-head gaze movements of the social robot. The potential benefits include improved communication, increased engagement, and a stronger sense of connection with the robot.

In this article, we deal with the lattice-compatibility between several classes of extended fuzzy sets. Concretely, we treat the problem of finding a lattice structure on set-valued fuzzy sets ($\operatorname{SVFS}$s) whose restriction to interval-valued fuzzy sets ($\operatorname{IVFS}$s) and (type-1) fuzzy sets ($\operatorname{FS}$s) match Zadeh's classical lattice operations. A prominent approach to this problem was given by Torra by means of the so-called hesitant fuzzy sets ($\operatorname{HFS}$s). Nevertheless, despite their usefulness in group decision-making problems, it is well-known that Torra's operations do not produce a lattice. Here, we mend partially this handicap by giving two lattice orders. Each of them preserves one of the Torra's operations and, additionally, reduces to Zadeh's orders on$\operatorname{FS}$s and on$\operatorname{IVFS}$s. As a counterpart, they cannot be defined on the whole class of$\operatorname{HFS}$s, or$\operatorname{SVFS}$s. Finally, we provide a full answer combining both orders. We define a partial order, that we call the symmetric order, on the whole class of nonempty subsets of [0,1]. This order extends the usual ones on [0,1] and on closed intervals of [0,1]. As a consequence, we find a lattice structure on$\operatorname{HFS}$s whose restriction to$\operatorname{FS}$s and$\operatorname{IVFS}$s reduces to Zadeh's operations.

This paper describes the development of a 3- stage signalling framework to trigger a social robot's bottom- up reactive behavior inspired by a biological model. In the first stage, low-level firing of stimuli due to external sources is constructed through perception grounding. This is followed by a saliency classifier which fires-up high level salient signals that require attention and are used to trigger the robot's reactive behavior. The whole framework evolves primarily on the knowledge ontology that defines the characteristics of the social robot and the querying mechanism that correlates the perceived stimuli with the ontology to trigger the reactive behavior. We evaluated the performance of our system with timing metrics and we achieved good results for our application.

This paper presents a fast cost-aware any-angle path planning algorithm for aerial robots in 3D building-like environments. The approach integrates Euclidean Distance Fields (EDF) and Lazy Theta* algorithm to compute safe and smooth paths. We show how to consider the analytical proper-ties of EDFs for polygonal obstacles to get an approximation of the cost along the line of sight segments of the planner, reducing the computational requirements. Numerous tests in a realistic building-like environment are performed to evaluate the proposed algorithm with respect to other heuristic search algorithms considering the distance cost by using an EDF. The results show that the proposed algorithm considerably reduces the computation time in indoor and outdoor environments enabling fast, safe and smooth paths.

Human collaboration is more likely to lead to cognitive growth when all group-members are actively involved in the collaborative process. However, there are cases that intragroup relationships need support. In this paper, we present an autonomous robotic system designed to interact with a pair of children in a problem-solving setting, aiming to understand how the robot behaviour impacts the group-members’ social dynamics. We developed an autonomous system with the Haru robot which we evaluated with an experimental study with 5-8yo children (N =84) to test the impact of the robot’s cognitive reliability and social positioning on human-to-human social dynamics, task performance and help-seeking behaviour. All participants took part in a baseline session (without the robot), an intervention (with the robot in a turn-taking setting) and an evaluation session (with a robot in a voluntary interaction setting). Results indicate that children who interacted with the reliable robot had a better task performance but children who interacted with the unreliable robot exhibited more task-related social interactions. Based on the results, we propose an interaction design concept which combines the set of the evaluated robot behaviours for an adaptive targeted support of child-robot teaming.

This paper presents a non-linear optimization method for trajectory planning of tethered aerial robots. Particularly, the paper addresses the planning problem of an unmanned aerial vehicle (UAV) linked to an unmanned ground vehicle (UGV) by means of a tether. The result is a collision-free trajectory for UAV and tether, assuming the UGV position is static. The optimizer takes into account constraints related to the UAV, UGV and tether positions, obstacles and temporal aspects of the motion such as limited robot velocities and accelerations, and finally the tether state, which is not required to be tense. The problem is formulated in a weighted multi-objective optimization framework. Results from simulated scenarios demonstrate that the approach is able to generate obstacle-free and smooth trajectories for the UAV and tether.

This paper presents DLL, a fast direct map-based localization technique using 3D LIDAR for its application to aerial robots. DLL implements a point cloud to map registration based on non-linear optimization of the distance of the points and the map, thus not requiring features, neither point correspondences. Given an initial pose, the method is able to track the pose of the robot by refining the predicted pose from odometry. Through benchmarks using real datasets and simulations, we show how the method performs much better than Monte-Carlo localization methods and achieves comparable precision to other optimization-based approaches but running one order of magnitude faster. The method is also robust under odometric errors. The approach has been implemented under the Robot Operating System (ROS), and it is publicly available.

In this paper, we explore the storytelling potential of a robot. We exploit the use of creative contents that maximize the embodied communication affordance of the empathic robot Haru. We identify the elements in storytelling such as narration, agency, engagement and education and synthesized these into the robot. Through effective design we investigated the possible answers that could leverage the limitations and the challenges in developing storytelling applications through a robotic medium. Our preliminary findings show that the use of an embodied agent such as a robot in storytelling only has meaning when its communicative affordance (i.e. embodiment, expressiveness, and other modalities) is tapped, adding new dimension to the experience. Otherwise, traditional storytelling delivery (e.g. tablet) without the use of embodiment will suffice. Hence, robots need to be performers rather than just mere props in storytelling.

Some relevant notions in fuzzy set theory are those of triangular-(t)- norm, t-conorm, and negation, which provide a systematic way of defining set-theoretic operations, or, from other point of view, logical connectives. For instance, the majority of fuzzy implications are directly derived from these operators, so they play a prominent role in fuzzy control theory or in approximate reasoning. This incites the search of suitable t-norms, t-conorms, and negations for solving each specific problem. In this article, we propose a procedure, that we call induction, for designing them on spaces of lattice-valued maps. Concretely, for each family of operators (t-norms, t-conorms, or negations) indexed in the domain set, we may induce an operator of the same kind, so that our method offers a great flexibility in the design task. It may be applied to well-known fuzzy objects as interval-valued or type-2 fuzzy sets. Nevertheless, the theory is formally developed for arbitrary bounded lattices.

Defining a robot's expressivity is a difficult task that requires thoughtful consideration of the potential of various robot modalities and a model of communication that humans understand. Humanoid and zoomorphic-designed robots can easily take cues from human and animals, respectively when designing their expressivity. However, a robot design that is neither human nor animal-like does not have a clear model to follow in terms of designing expressivity. Animation presents a potential model in these circumstances as animated characters in movies take various forms, sizes, shapes and styles, and are successful in defining expressivity that is widely accepted across different languages and cultures. In this paper, we discuss the development and design of the expressivity of Haru, a table top robot that is neither human nor animal-like and the application of animation expertise to the holistic treatment of the different modalities. The method maximizes animation techniques and expertise normally applied to movies to generate expressivity that is then transferred to the robot hardware. Experimental results show that the robot's expressivity generated using our method is easily understood and are preferred to the conventional approach of generating expressions.

This paper addresses the material handling problem (MHS) in warehouse automation by proposing a system that uses an automated guided vehicle (AGV) in industrial environments. The aim is to optimize the picking task with respect to manual operation in a paint factory. The work describes the whole system to perform all the automatic tasks. The process is controlled by the Manufacturing Process Management System (MPMS) and an autonomous co-worker robot execute the mission in partially known environments. The navigation system implemented is safe and robust. It considers the people detection and unknown static obstacles. Also, an ultra-wide-band localization system is implemented by offering new capabilities for situation awareness in factories. Experiments with a real holonomic platform called ARCO are performed to validate the approach.

In this work we present a bioinspired visual system sensor to estimate angular rates in unmanned aerial vehicles (UAV) using Neural Networks. We have conceived a hardware setup to emulate Drosophila's ocellar system, three simple eyes related to stabilization. This device is composed of three low resolution cameras with a similar spatial configuration as the ocelli. There have been previous approaches based on this ocellar system, most of them considering assumptions such as known light source direction or a punctual light source. In contrast, here we present a learning approach using Artificial Neural Networks in order to recover the system's angular rates indoors and outdoors without previous knowledge. A classical computer vision based method is also derived to be used as a benchmark for the learning approach. The method is validated with a large dataset of images (more than half a million samples) including synthetic and real data. The source code of the algorithms and the datasets used in this paper have been released in an open repository.

Motion is an important modality for human-robot interaction. Besides a fundamental component to carry out tasks, through motion a robot can express intentions and expressions as well. In this paper, we focus on a tabletop robot in which motion, among other modalities, is used to convey expressions. The robot incorporates a set of pre-programmed motion animations that show different expressions with various intensities. These have been created by designers with expertise in animation. The objective in the paper is to analyze if these examples can be used as demonstrations, and combined by the robot to generate additional richer expressions. Challenges are the representation space used, and the scarce number of examples. The paper compares three different learning from demonstration approaches for the task at hand. A user study is presented to evaluate the resultant new expressive motions automatically generated by combining previous demonstrations.

This work presents an approach to learn path planning for robot social navigation by demonstration. We make use of Fully Convolutional Neural Networks (FCNs) to learn from expert's path demonstrations a map that marks a feasible path to the goal as a classification problem. The use of FCNs allows us to overcome the problem of manually designing/identifying the cost-map and relevant features for the task of robot navigation. The method makes use of optimal Rapidly-exploring Random Tree planner (RRT*) to overcome eventual errors in the path prediction; the FCNs prediction is used as cost-map and also to partially bias the sampling of the configuration space, leading the planner to behave similarly to the learned expert behavior. The approach is evaluated in experiments with real trajectories and compared with Inverse Reinforcement Learning algorithms that use RRT* as underlying planner.

Many recent works have proposed algorithms for information gathering that benefit from multi-robot cooperation. However, most algorithms either employ discretization of the state and action spaces, which makes them computationally intractable for robotic systems with complex dynamics; or cannot deal with inter-robot restrictions like e.g. communication constraints. This paper presents an approach for multi-robot information gathering that tackles the two aforementioned issues. To this end we propose an algorithm that combines in an innovative manner Gaussian processes (GPs) to model the physical process of interest, RRT planners to plan paths in a continuous domain, and a distributed decision-making algorithm to achieve multi-robot cooperation. Specifically, we employ the Max-sum algorithm for distributed multi-robot cooperation by defining an information-theoretic utility function together with a path clustering approach. This function maximizes information gathering, subject to inter-robot communication constraints. We validate the proposed approach in simulations, and in a field experiment where three quadcopters explore a simulated wind field. Results demonstrate the effectiveness of the approach.

This paper presents a vision-based localization system for global pose estimation of a sewer inspection robot given prior information of the sewer network from local institutions. The system is based on a Monte-Carlo Localization system that uses RGBD odometry for the prediction stage. The update step takes into account the sewer network topology for discarding wrong hypotheses. Moreover, this step is further refined whenever a discrete element of the network (i.e. manhole) is detected. To this end, another RGBD camera pointing upwards is used for precise manhole detection. A Convolutional Neural Network has been successfully trained for classifying images with and without manholes with 96% accuracy over the tested dataset. The complete system has been validated with real data obtained from the sewers of Barcelona yielding accurate localization results. All the logs and code used in the context of this paper are publicly available.

This paper presents a methodology for mapping and localization of Unmanned Aerial Vehicles (UAVs) based on the integration of sensors from different modalities. Particularly, we integrate distance estimations to Ultra-Wideband (UWB) sensors and 3D point-clouds from RGB-D sensors. First, a novel approach for environment mapping is introduced, exploiting the synergies between UWB sensors and point-clouds to produce a multi-modal 3D map that integrates the estimated UWB sensors position. This map is further integrated into a Monte Carlo Localization method to robustly estimate the UAV pose. Finally, the full approach is tested with real indoor flights and validated with a motion tracking system.

Information gathering algorithms aim to intelligently select the robot actions required to efficiently obtain an accurate reconstruction of a physical process, such as an occupancy map, or a magnetic field. Many recent works have proposed algorithms for information gathering. However, these algorithms employ discretization of the state space, which makes them computationally intractable for robotic systems with complex dynamics. Moreover, most algorithms are not suited for online information gathering tasks. This paper presents a novel approach that tackles the two aforementioned issues. Specifically, our approach includes two intertwined steps: a Gaussian processes (GPs)-based prediction that allows a robot to identify highly unexplored locations, and an RRT*-based informative path planning that guides the robot towards those locations. The combination of the two steps allows an online realization of the algorithm, while eliminates the need of discretization. We demonstrate the effectiveness of the proposed algorithm in simulations, as well as with an experiment in which a ground-based robot explores the magnetic field intensity within an indoor environment populated with obstacles.

Exploration is a crucial problem in safety of life applications, such as search and rescue missions. Gaussian processes constitute an interesting underlying data model that leverages the spatial correlations of the process to be explored to reduce the required sampling of data. Furthermore, multi-agent approaches offer well known advantages for exploration. Previous decentralized multi-agent exploration algorithms that use Gaussian processes as underlying data model, have only been validated through simulations. However, the implementation of an exploration algorithm brings difficulties that were not tackle yet. In this work, we propose an exploration algorithm that deals with the following challenges: (i) which information to transmit to achieve multi-agent coordination; (ii) how to implement a light-weight collision avoidance; (iii) how to learn the data's model without prior information. We validate our algorithm with two experiments employing real robots. First, we explore the magnetic field intensity with a ground-based robot. Second, two quadcopters equipped with an ultrasound sensor explore a terrain profile. We show that our algorithm outperforms a meander and a random trajectory, as well as we are able to learn the data's model online while exploring.

Social and Affective Robotics is a growing multidisciplinary field encompassing computer science, engineering, psychology, education, and many other disciplines. It explores how social and affective factors influence interactions between humans and robots, and how affect and social signals can be sensed and integrated into the design, implementation, and evaluation of robots. With talks by renowned researchers in this area, Social and Affective Robotics Tutorial will help both new and experienced researchers to identify trends, concepts, methodologies and applications in this field, identified as a technological megatrend driving the fourth industrial revolution.

Target tracking with bearing-only sensors is a challenging problem when the target moves dynamically in complex scenarios. Besides the partial observability of such sensors, they have limited field of views, occlusions can occur, etc. In those cases, cooperative approaches with multiple tracking robots are interesting, but the different sources of uncertain information need to be considered appropriately in order to achieve better estimates. Even though there exist probabilistic filters that can estimate the position of a target dealing with uncertainties, bearing-only measurements bring usually additional problems with initialization and data association. In this paper, we propose a multi-robot triangulation method with a dynamic baseline that can triangulate bearing-only measurements in a probabilistic manner to produce 3D observations. This method is combined with a decentralized stochastic filter and used to tackle those initialization and data association issues. The approach is validated with simulations and field experiments where a team of aerial and ground robots with cameras track a dynamic target.

This video details the development of an intelligent outdoor Guide robot. The main objective is to deploy an innovative robotic guide which is not only able to show information, but to react to the affective states of the users, and to offer location-based services using augmented reality. The scientific challenges concern autonomous outdoor navigation and localization, robust 24/7 operation, affective interaction with visitors through outdoor human and facial feature detection as well as engaging interactive behaviors in an ongoing non-verbal dialogue with the user.

Robot navigation in human environments is an active research area that poses serious challenges. Among them, human-awareness has gain lot of attention in the last years due to its important role in human safety and robot acceptance. The proposed robot navigation system extends state of the navigation schemes with some social skills in order to naturally integrate the robot motion in crowded areas. Learning has been proposed as a more principled way of estimating the insights of human social interactions. To do this, inverse reinforcement learning is used to derive social cost functions by observing persons walking through the streets. Our objective is to incorporate such costs into the robot navigation stack in order to “emulate” these human interactions. In order to alleviate the complexity, the system is focused on learning an adequate cost function to be applied at the local navigation level, thus providing direct low-level controls to the robot. The paper presents an analysis of the results in a robot navigating in challenging real scenarios, analyzing and comparing this approach with other algorithms.

Robot navigation in human environments is an active research area that poses serious challenges. Among them, social navigation and human-awareness has gain lot of attention in the last years due to its important role in human safety and robot acceptance. Learning has been proposed as a more principled way of estimating the insights of human social interactions. In this paper, inverse reinforcement learning is analyzed as a tool to transfer the typical human navigation behavior to the robot local navigation planner. Observations of real human motion interactions found in one publicly available datasets are employed to learn a cost function, which is then used to determine a navigation controller. The paper presents an analysis of the performance of the controller behavior in two different scenarios interacting with persons, and a comparison of this approach with a Proxemics-based method.

In this paper, we introduce an approach called FSBS (Forward Search in Belief Space) for online planning in POMDPs. The approach is based on the RTBSS (Real-Time Belief Space Search) algorithm of [1]. The main departure from the algorithm is the introduction of similarity measures in the belief space. By considering statistical divergence measures, the similarity between belief points in the forward search tree can be computed. Therefore, it is possible to determine if a certain belief point (or one very similar) has been already visited. This way, it is possible to reduce the complexity of the search by not expanding similar nodes already visited in the same depth. This reduction of complexity makes possible the real-time implementation of more complex problems in robots. The paper describes the algorithm, and analyzes different divergence measures. Benchmark problems are used to show how the approach can obtain a ten-fold reduction in the computation time for similar obtained rewards when compared to the original RTBSS. The paper also presents experiments with a quadrotor in a search application.

Planning under uncertainty faces a scalability problem when considering multi-robot teams, as the information space scales exponentially with the number of robots. To address this issue, this paper proposes to decentralize multiagent Partially Observable Markov Decision Process (POMDPs) while maintaining cooperation between robots by using POMDP policy auctions. Furthermore, communication models in the multiagent POMDP literature severely mismatch with real inter-robot communication. We address this issue by applying a decentralized data fusion method in order to efficiently maintain a joint belief state among the robots. The paper focuses on a cooperative tracking application, in which several robots have to jointly track a moving target of interest. The proposed ideas are illustrated in real multi-robot experiments, showcasing the flexible and robust cooperation that our techniques can provide.

Radio signal-based localization and mapping is becoming more interesting as applications involving the collaboration between robots and static wireless devices are more common. Under certain assumptions, the problem is basically equivalent to the range-only localization and mapping problem. The paper presents a method for mapping with a mobile robot the position of a set of nodes using radio signal measurements. It uses Gaussian Mixtures for undelayed initialization of the position of the wireless nodes. The paper shows how the approach can be integrated within a Kalman Filter. This way, information can be used in the filter since the first measurement. The paper describes simulations to verify the feasibility of the approach, and presents results obtained with experimental data involving one mobile robot and a wireless sensor network.

Radio signal-based localization and mapping is becoming more interesting in robotics as applications involving the collaboration between robots and static wireless devices are more common. This paper describes a method for mapping with a mobile robot the position of a set of nodes using radio signal measurements. The method employs Gaussian Mixtures Models (GMM) for undelayed initialization of the position of the wireless nodes within a Kalman filter. Moreover, the paper extends the method to consider active sensing strategies in order to map the nodes. Entropy variation is used as a measurement of information gain, and allows to prioritize control actions of the robot. However, as there is no analytical expression for the entropy of a GMM, upper bounds of the entropy, for which close form computation is possible, are used instead. The paper describes simulations that show the feasibility of the approach.

This paper presents a decentralized data fusion approach to perform cooperative perception with data gathered from heterogeneous sensors, which can be static or carried by robots. Particularly, a Decentralized Delayed-State Extended Information Filter (DDSEIF) is described, where full state trajectories are considered to fuse the information. This permits to obtain an estimation equal to that obtained by a centralized system, and allows delays and latency in the communications. The sparseness of the information matrix maintains the communications overhead at a reasonable level. The method is applied to cooperative tracking and some results in disaster management scenarios are shown. In this kind of scenarios the target might move in both open field and indoor areas, so fusion of data provided by heterogeneous sensors is beneficial.

This paper presents a new method for the 3D localization of an outdoor wireless sensor network (WSN) by using a single flying beacon-node on-board an autonomous helicopter, which is aware of its position thanks to a GPS device. The technique is based on particle filtering and does not require any prior information about the position of the nodes to be estimated. Its structure and stochastic nature allows a distributed computation of the position of the nodes. The paper shows how the method is very suitable for outdoor applications with robotic data-mule systems. The paper includes a section with experiments.

This paper presents a probabilistic framework where uncertainties can be considered in the mosaic building process. It is shown how can be used as an environment representation for an aerial robot. The inter-image relations are modeled by homographies. The paper shows a robust method to compute them in the case of quasi-planar scenes, and also how to estimate the uncertainties in these local image relations. Moreover, the paper describes how, when a loop is present in the sequence of images, the accumulated drift can be compensated and propagated to the rest of the mosaic. In addition, the relations among images in the mosaic can be used, under certain assumptions, to localize the robot.

The paper presents a vision-based position estimation method for UAVs. It assumes a planar scene, approximation that usually holds when a vehicle is flying at a relatively high altitude. Monocular image sequences gathered by the UAV are used to estimate the vehicle motion, but accumulative errors can make diverge the estimated position. The proposed method uses an online-built mosaic to correct the drift associated to the planar motion estimation algorithm. The mosaic allows to use not only the current image but also previously recorded information for localization. Results from actual field experiments are presented

This paper presents a vision-based method to estimate the * real motion of a single camera from views of a planar patch. Projective techniques allow to estimate camera motion from pixel space apparent motion without explicit 3-D reconstruction. In addition, the paper will present the HELINSPEC project, the framework where the proposed method has been tested, and will detail some applications in external building inspection that make use of the proposed techniques.

The paper presents a framework for cooperative fire detection by means of a fleet of heterogeneous UAVs. Computer vision techniques are used to detect and localize fires from infrared and visual images and other data provided by the cameras and other sensors on-board the UAVs. The paper deals with the techniques used to decrease the uncertainty in fire detection and increase the accuracy in fire localisation by means of the cooperation of the information provided by several UAVs. The presented methods have been developed in the COMETS multi-UAV project.

This work presents real time computer vision techniques for autonomous navigation and operation of unmanned aerial vehicles. The proposed techniques are based on image feature matching and projective methods. Particularly, the paper presents the application to helicopter motion compensation and object detection. These techniques have been implemented in the framework of the COMETS multi-UAV systems. Furthermore, The work presents the application of the proposed techniques in a forest fire scenario in which the COMETS system can be demonstrated.