Quadcopter attitude control involves two tasks: smooth attitude tracking and aggressive stabilization from arbitrary states. Although both can be formulated as tracking problems, their distinct state spaces and control strategies complicate a unified reward function. We propose a multitask deep reinforcement learning framework that leverages parallel simulation with IsaacGym and a Graph Convolutional Network (GCN) policy to address both tasks effectively. Our multitask Soft Actor-Critic (SAC) approach achieves faster, more reliable learning and higher sample efficiency than single-task methods. We validate its real-world applicability by deploying the learned policy—a compact two-layer network with 24 neurons per layer—on a Pixhawk flight controller, achieving 400 Hz control without extra computational resources. We provide our code at https://github.com/ robot-perception-group/GraphMTSAC_UAV/.

Infant and youth mortality has seen a steady decline over the years. However, many issues related to sociodemographic factors still persist. In Portugal, while mortality forecasts are regularly disclosed to the general public by specialised public entities, very few studies have focused on its determinants, and none have taken advantage of the modelling capabilities of Machine Learning (ML) techniques. This work makes use of real-world data in order to identify the main determinants of infant and youth mortality in Portugal using some of these techniques. The data used for this study comprised 178 databases from various authorities, encompassing economic, demographic, environmental, health, education, and mortality variables at the municipal level. No data at the individual level was available. Two different approaches were proposed. For the first one, the problem was framed as a regression problem, with the number of deaths as the target variable and the potential determinants as the predictors. Simple regression models were used, mainly due to their interpretability. A neural network was also employed to enable a comparison between linear and nonlinear models. Feature elimination and feature selection methods were devised in order to ascertain which variables were the most relevant. These include a feature selection method specifically custom for the problem at hand which proved particularly effective, as it led to performance improvements for every model used in this work. The second approach made use of the K-means clustering technique to determine which of the previously selected variables led to better clusters with both the number of deaths and the mortality rate. To this end, the silhouette method was chosen as the evaluation metric. The best regression model achieved an R2of 0.846. The foreign population with legal status of residence in the parents’ place of residence and the average monthly earnings of employees were shown to be the features with the greatest impact on mortality.

The solar influence on space weather and terrestrial environment is substantial. Strong geomagnetic storm activity can significantly affect astronauts in orbit, communications and GPS systems and disrupt Earth’s power distribution networks, making continuous monitoring and forecasting of solar activity vital. Sunspots are magnetic disturbances in the photosphere characterized by their dark appearance in the solar disk, being directly related to phenomena that contribute to these intense storms, namely solar flares and coronal mass ejections. This article lies at the intersection between solar surveillance and computer vision by applying state-of-the-art deep learning algorithms in the automatic detection of sunspots and sunspot groups. Based on two techniques, semantic segmentation and instance segmentation, two algorithms are implemented to tackle both purposes, U-Net and Mask R-CNN respectively. The ground-truth dataset was built from the available Debrecen Heliographic Observatory (DHO) space-borne sunspot catalogues from 2010 to 2014. The best U-Net implemented model presented a 74.2% IoU, surpassing the detection results evidenced by the Automated Solar Activity Prediction System (ASAP). The instance segmentation approach, a novelty application technique for sunspot group detection and still a challenging task in computer vision, achieved 51.7 bounding box AP and 78.6% accuracy in predicting the number of sunspot groups.

User interfaces (UI) are shifting from being attention-hungry to being attentive to users’ needs upon interaction. Interfaces developed for robot teleoperation can be particularly complex, often displaying large amounts of information, which can increase the cognitive overload that prejudices the performance of the operator. This paper presents the development of a Physiologically Attentive User Interface (PAUI) prototype preliminary evaluated with six participants. A case study on Urban Search and Rescue (USAR) operations that teleoperate a robot was used although the proposed approach aims to be generic. The robot considered provides an overly complex Graphical User Interface (GUI) which does not allow access to its source code. This represents a recurring and challenging scenario when robots are still in use, but technical updates are no longer offered that usually mean their abandon. A major contribution of the approach is the possibility of recycling old systems while improving the UI made available to end users and considering as input their physiological data. The proposed PAUI analyses physiological data, facial expressions, and eye movements to classify three mental states (rest, workload, and stress). An Attentive User Interface (AUI) is then assembled by recycling a pre-existing GUI, which is dynamically modified according to the predicted mental state to improve the user's focus during mentally demanding situations. In addition to the novelty of the proposed PAUIs that take advantage of pre-existing GUIs, this work also contributes with the design of a user experiment comprising mental state induction tasks that successfully trigger high and low cognitive overload states. Results from the preliminary user evaluation revealed a tendency for improvement in the usefulness and ease of usage of the PAUI, although without statistical significance, due to the reduced number of subjects.

A well-known challenge in rover teleoperation is the operator’s lack of situational awareness (SA). This often leads to an inaccurate perception of the rover’s status and surroundings and, consequently, to faulty decision-making by the operator. We present a novel teleoperation interface to control the locomotion of a ground rover with a 7DOF force feedback device (sigma.7), while providing haptic feedback to ensure appropriate SA. In particular, the device provides proprioceptive cues to convey the rover’s attitude. This can be particularly useful for environments with insufficient visual cues to estimate attitude (e.g., a cave). In systematic experimental trials controlling a robot in an outdoor environment, we evaluated the validity of employing sigma.7 as an alternative to a standard joystick. We tested the use of attitude as an aid to situational awareness. We found no significant detriment in manoeuvrability compared to a conventional joystick, thus validating the sigma.7 as an effective control device. Regarding SA, results showed no statistical difference between the visual and haptic cues for attitude feedback, thus validating the haptic method as an effective alternative to offloading the visual channel by conveying attitude information through the haptic channel instead of visual cues. Finally, qualitative observations of the participant’s behaviour during the experiments showed that operators with haptic feedback were comprehensively aware of the rover’s status.

The First-Order Autonomous Learning Multi-Model (ALMMo) system was initially introduced as a regressor which could be easily adapted to a binary classifier. In this paper, an extension of the ALMMo algorithm is proposed, enabling it to tackle multi-class classification tasks, without escalating the computational demand substantially. Thus, this paper highlights the flexibility of the method by increasing its range of capabilities. The proposed extension is tested in 3 benchmark datasets, and the obtained results are presented as a proof of the concept. Furthermore, these results are compared to 2 benchmark methods, those being shallow neural-networks and support vector machines; as well as to the ALMMo-0 classifier.

This paper presents a comparison of linear and nonlinear Model Predictive Control (MPC) strategies for trajectory tracking Micro Aerial Vehicles (MAVs). In this comparative study, we paid particular attention to establish quantitatively fair metrics and testing conditions for both strategies. In particular, we chose the most suitable numerical algorithms to bridge the gap between linear and nonlinear MPC, leveraged the very same underlying solver and estimation algorithm with identical parameters, and allow both strategies to operate with a similar computational budget. In order to obtain a well-tuned performance from the controllers, we employed the parameter identification results determined in a previous study for the same robotic platform and added a reliable disturbance observer to compensate for model uncertainties. We carried out a thorough experimental campaign involving multiple representative trajectories. Our approach included three different stages for tuning the algorithmic parameters, evaluating the predictive control feasibility, and validating the performances of both MPC-based strategies. As a result, we were able to propose a decisional recipe for selecting a linear or nonlinear MPC scheme that considers the predictive control feasibility for a peculiar trajectory, characterized by specific speed and acceleration requirements, as a function of the available on-board resources.

Predicting if passengers in a connecting flight will lose their connection is paramount for airline profitability. We present novel machine learning-based decision support models for the different stages of connection flight management, namely for strategic, pre-tactical, tactical and post-operations. We predict missed flight connections in an airline’s hub airport using historical data on flights and passengers, and analyse the factors that contribute additively to the predicted outcome for each decision horizon. Our data is high-dimensional, heterogeneous, imbalanced and noisy, and does not inform about passenger arrival/departure transit time. We employ probabilistic encoding of categorical classes, data balancing with Gaussian Mixture Models, and boosting. For all planning horizons, our models attain an area under the curve (AUC) of the receiver operating characteristic (ROC) higher than 0.93. SHAP value explanations of our models indicate that scheduled/perceived connection times contribute the most to the prediction, followed by passenger age and whether border controls are required.

Mobile social robots should be able to engage in interaction with people effectively. However, greeting someone is a complex task since it implies an exchange of social signals. Adam Kendon modeled human greetings as a set of six phases: initiation of approach, distance salutation, head dip, approach, final approach, and close salutation. Based on Kendon’s model, we propose a system for mobile social robots that manages the greeting process through the exchange of social signals. A Hidden Markov Model keeps track of the greeting stage through the observation of the human gestures, while a behavior tree generates appropriate robot actions. We used publicly available datasets to train the Hidden Markov Model. Evaluation on test sets showed an average greeting phase estimation accuracy of 80.9%. We tested the full system (Hidden Markov Model + Behavior Tree) in simulation and in a real world pilot experiment using the Vizzy robot, and it recognized and replicated the correct phase with an accuracy of 91.8% and 53.8%, respectively.

Space free-flyers like the Astrobee robots currently operating aboard the International Space Station must operate with inherent system uncertainties. Parametric uncertainties like mass and moment of inertia are especially important to quantify in these safety-critical space systems and can change in scenarios such as on-orbit cargo movement, where unknown grappled payloads significantly change the system dynamics. Cautiously learning these uncertainties en route can potentially avoid time- and fuel-consuming pure system identification maneuvers. Recognizing this, this work proposes RATTLE, an online information-aware motion planning algorithm that explicitly weights parametric model-learning coupled with real-time replanning capability that can take advantage of improved system models. The method consists of a two-tiered (global and local) planner, a low-level model predictive controller, and an online parameter estimator that produces estimates of the robot’s inertial properties for more informed control and replanning on-the-fly; all levels of the planning and control feature online update-able models. Simulation results of RAT-TLE for the Astrobee free-flyer grappling an uncertain payload are presented alongside results of a hardware demonstration showcasing the ability to explicitly encourage model parametric learning while achieving otherwise useful motion.

The robust execution of a temporal plan in a perturbed environment is a problem that remains to be solved. Perturbed environments, such as the real world, are non-deterministic and filled with uncertainty. Hence, the execution of a temporal plan presents several challenges and the employed solution often consists of replanning when the execution fails. In this paper, we propose a novel algorithm, named Olisipo, which aims to maximise the probability of a successful execution of a temporal plan in perturbed environments. To achieve this, a probabilistic model is used in the execution of the plan, instead of in the building of the plan. This approach enables Olisipo to dynamically adapt the plan to changes in the environment. In addition to this, the execution of the plan is also adapted to the probability of successfully executing each action. Olisipo was compared to a simple dispatcher and it was shown that it consistently had a higher probability of successfully reaching a goal state in uncertain environments, performed fewer replans and also executed fewer actions. Hence, Olisipo offers a substantial improvement in performance for disturbed environments.

Efficient path planning for autonomous vehicles in cluttered environments is a challenging sequential decision-making problem under uncertainty. In this context, this paper implements a partially observable stochastic shortest path (PO-SSP) planning problem for autonomous urban navigation of Unmanned Aerial Vehicles (UAVs). To solve this planning problem, the POMCP-GO algorithm is used, which is goal oriented variant of POMCP, one of the fastest online state-of-the-art solvers for partially observable environments based on Monte Carlo Planning. This algorithm relies on the Upper Confidence Bounds (UCB1) algorithm as action selection strategy. UCB1 depends on an exploration constant typically adjusted empirically. Its best value varies significantly between planning problems, and hence, an exhaustive search to find the most suitable value is required. This exhaustive search applied to a complex path planning problem may be extremely time consuming. Moreover, considering real applications where online planning is needed, this extensive search is not suitable. Thereby this paper explores the use of an adaptive exploration coefficient for action selection during planning. Monte-Carlo value backup approximation is also applied which empirically demonstrates to accelerate the policy value convergence. Simulation results show that the use of the adaptive exploration co- \nefficient within a user-defined interval achieves better convergence and success rates when compared with most hand-tuned fixed coefficients in said interval, although never achieving the same results as the best fixed coefficient. Therefore, a compromise must be made between the desired quality of the results and the time one is willing to spend on the exhaustive search for the best coefficient value before planning.

The performance of model-based motion control for free-flying robots relies on accurate estimation of their parameters. In this work, a method of rigid body inertial parameter estimation which relies on a variational approach is presented. Instead of discretizing the continuous equations of motion, discrete dual quaternion equations based on variational mechanics are used to formulate a linear parameter estimation problem. This method depends only on the pose of the rigid body obtained from standard localization algorithms. Recursive semi-definite programming is used to estimate the inertial parameters (mass, rotational inertia and center of mass offset) online. Linear Matrix Inequality constraints based on the pseudo-inertia matrix ensure that the estimates obtained are fully physically consistent. Simulation results demonstrate that this method is robust to disturbances and the produced estimates are at least one order of magnitude more accurate when compared to discretization using finite differences.

Typically, the problem of robotic manipulation is divided among two sequential phases: a planning one and an execution one. However, since the second one is executed in open loop, the robot is unable to react in real time to changes in the task (e.g. moving object). This paper addresses the mobile manipulation problem from a real-time, closed loop perspective. In particular, we propose a generic optimization-based Cartesian controller, that given a continuous monitoring of the goal, determines the best motion commands. We target our controller to a robotic system comprising an arm and a mobile platform. However, the approach can in principle be extended to more complex mechanisms. The approach is based on shifting the problem to velocity space, where end effector velocity is a linear function of joint and base platform velocities. Our approach was quantitatively evaluated both on simulation and on a real service robot. It was also integrated into a mobile service robot architecture targeting domestic tasks and evaluated on the RoboCup@Home scientific competition. Our results show that the controller is able to reach random arm configurations with a high probability of success.

The mobile manipulation of arbitrary objects in micro-gravity by a free-flying robot is a challenging problem. One particular problem is the control of the robot after grasping an object of unknown inertial properties. This paper addresses the problem of determining, while the robot is grasping an object, the inertial properties of the resulting robot-object body. This is done by first optimizing an exciting trajectory, and then estimating the inertial parameters from the executed trajectory. These trajectories are tracked using a Nonlinear Model Predictive Control (NMPC) controller with actuation constraints. Both planned and executed trajectories are represented using Fourier series. The approach is evaluated in simulation using the Space CoBot free-flying robot.

HTC Vive has been gaining attention as a cost-effective, off-the-shelf tracking system for collecting ground truth pose data. We assess this system's pose estimation through a series of controlled experiments where we show its precision to be in the millimeter magnitude and accuracy to range from millimeter to meter. We also show that Vive gives greater weight to inertial measurements in order to produce a smooth trajectory for virtual reality applications. Hence, the Vive's off the shelf algorithm is poorly suited for robotics applications such as measuring ground truth poses, where accuracy and repeatability are key. Therefore we introduce a new open-source tracking algorithm and calibration procedure for Vive which address these problems. We also show that our approach improves the pose estimation repeatability and accuracy by up to two orders of magnitude.

Despite the recent success of state-of-the-art deep learning algorithms in object recognition, when these are deployed as-is on a mobile service robot, we observed that they failed to recognize many objects in real human environments. In this paper, we introduce a learning algorithm in which robots address this flaw by asking humans for help, also known as a symbiotic autonomy approach. In particular, we bootstrap YOLOv2, a state-of-the-art deep neural network and train a new neural network, that we call HHELP, using only data collected from human help. Using an RGB camera and an onboard tablet, the robot proactively seeks human input to assist it in labeling surrounding objects. Pepper, located at CMU, and Monarch Mbot, located at ISR-Lisbon, were the service robots that we used to validate the proposed approach. We conducted a study in a realistic domestic environment over the course of 20 days with 6 research participants. To improve object detection, we used the two neural networks, YOLOv2 + HHELP, in parallel. Following this methodology, the robot was able to detect twice the number of objects compared to the initial YOLOv2 neural network, and achieved a higher mAP (mean Average Precision) score. Using the learning algorithm the robot also collected data about where an object was located and to whom it belonged to by asking humans. This enabled us to explore a future use case where robots can search for a specific person's object. We view the contribution of this work to be relevant for service robots in general, in addition to Pepper, and Mbot.

In order to safely teleoperate an unmanned ground vehicle (UGV) through rough terrain, a human operator needs to be aware of its attitude. This awareness ensures (s)he can avoid rolling or tipping over the UGV, due to steep slopes or terrain depressions. Yet, it has been challenging to develop teleoperation systems that can provide attitude awareness, to human operators. So far, all research has been focused in implementing solutions through visual modality. We take a different approach, using haptic feedback to transmit an UGV's attitude to an human operator. Our novel attitude haptic feedback device (AHFD) provides information about the UGV's roll and pitch, and their direction of rotation, thorugh the use of upper limb proprioception. We also discuss a preliminary user study to understand the influence two different AHFD configurations (natural and ergonomic) have on attitude perception. Our results indicate there is no difference between the two AHFD configuration in judging attitude states and direction of rotations. Yet, natural configuration is perceived as causing higher physical strain and demand, while the ergonomic a higher overall mental effort. We also found participants had more difficulty in judging pitch attitude at higher angles.

Teleoperation of Unmanned Ground Vehicles (UGVs) is dependent on several factors as the human operator is physically detached from the UGV. This paper focuses on situations where a UGV designed for search and rescue loses traction, thus becoming unable to comply with the operator's commands. In such situations, the lack of Situation Awareness (SA) may lead to an incorrect and inefficient response to the current UGV state usually confusing and frustrating the human operator. The exclusive use of visual information to simultaneously perform the main task (e.g. search and rescue) and to be aware of possible impediments to UGV operation, such as loss of traction, becomes a very challenging task for a single human operator. We address the challenge of unburdening the visual channel by using other human senses to provide multimodal feedback in UGV teleoperation. To achieve this goal we present a teleoperation architecture comprising (1) a laser-based traction detector module, to discriminate between traction losses (stuck and sliding) and (2) a haptic interface to convey the detected traction state to the human operator through different types of tactile stimuli provided by three haptic devices (E-Vita, Traction Cylinder and Vibrotactile Glove). We also report the experimental results of a user study to evaluate to what extent this new feedback modality improves the user SA regarding the UGV traction state. Statistically significant results were found supporting the hypothesis that two of the haptic devices improved the comprehension of the traction state of the UGV when comparing to exclusively visual modality.

This paper addresses the problem of having a robot executing motor tasks requested by a human through spoken language. Verbal instructions do not typically have a one-to-one mapping to robot actions, due to various reasons: economy of spoken language, e.g., one short instruction might indeed correspond to a complex sequence of robot actions, and details about action execution might be omitted; grounding, e.g., some actions might need to be added or adapted due to environmental contingencies; embodiment, e.g., a robot might have different means than the human ones to obtain the goals that the instruction refers to. We propose a general cognitive architecture to deal with these issues, based on three steps: i) language-based semantic reasoning on the instruction (high-level), ii) formulation of goals in robot symbols and probabilistic planning to achieve them (mid-level), iii) action execution (low-level). The description of the mid-level is the main focus of this paper. The robot plans are adapted to the current scenario, perceived in real-time and continuously updated, taking in consideration the robot capabilities, modeled through the concept of affordances: this allows for flexibility and creativity in the task execution. We showcase the performance of the proposed architecture with real world experiments using the iCub humanoid robot, also in the presence of unexpected events and action failures.

This work proposes a novel relative localization system, based on active markers and an on-board camera, for tracking multiple quadrotors in a limited field of view. The system extracts the 3D poses of the markers including one that, by pulsating at a predefined frequency, provides an unique platform ID. We discuss how the camera field of view can be explored in presence of multiple targets, and what are the conditions on the system visibility that lead to the establishment of bidirectional sensing between robots with similar sensing capabilities. A visibility analysis is conducted to show that the developed relative localization system meets such requirements, and a closed-loop experiment is used to validate its performance under these conditions. Finally, its performance is compared with other results from the literature, and a metric is established with the intent of mapping different design solutions, facilitating design choices in presence of different requirements.

In this paper we address the 3D reconstruction of points, on a non-central catadioptric system, composed by a mirror, a projector, and a perspective camera. The goal of the paper is to propose a framework to build an omnidirectional depth camera, towards an omnidirectional RGB-D camera system. The main contributions are: an efficient technique to project 3D points from the world to an image of a general non-central catadioptric camera; the definition of the template pattern (for both the projector and camera's images); and the matching between the projection of these features to the world and its respective images. The 3D depth is directly recovered using the template matching approach. In conclusion, we apply some filtering techniques to improve the results. To evaluate the proposed framework, we test the method using synthetic data, under different levels and types of noises, proving that the framework is robust to noise and, thus, can be put into practice.

This paper presents the design of a small aerial robot for inhabited microgravity environments, such as orbiting space stations (e.g., ISS). In particular, we target a fleet of robots, called Space CoBots, for collaborative tasks with humans, such as telepresence and cooperative mobile manipulation. The design is modular, comprising an hexrotor based propulsion system, and a stack of modules including batteries, cameras for navigation, a screen for telepresence, a robotic arm, space for extension modules, and a pair of docking ports. These ports can be used for docking and for mechanically attaching two Space CoBots together. The kinematics is holonomic, and thus the translational and the rotational components can be fully decoupled. We employ a multi-criteria optimization approach to determine the best geometric configuration for maximum thrust and torque across all directions. We also tackle the problem of motion control: we use separate converging controllers for position and attitude control. Finally, we present simulation results using a realistic physics simulator. These experiments include a sensitivity evaluation to sensor noise and to unmodeled dynamics, namely a load transportation.

In this work we study the efficient search of objects in domestic environments, using probabilistic logic to represent uncertainty about object location and partially observable Markov decision processes (POMDP) for the decision-making process regarding the movements to be carried out by the robot to improve its belief about the object locations. We propose the use of a semantic map that stores information about the knowledge in the system and updates it, by an inference process, with sensor information received from the object recognition module. However, semantic maps are not capable of actively search for more information in the environment. For that reason a decision-making module, based on a POMDP framework, is integrated in the system. Several experiments were made in a realistic apartment test bed using every day objects and a mobile robot, showing that this hybrid solution makes the search process more efficient.

Multi-robot cooperative navigation in real-world environments is essential in many applications, including surveillance and search-and-rescue missions. State-of-the-art methods for cooperative navigation are often tested in ideal laboratory conditions and not ready to be deployed in real-world environments, which are often cluttered with static and dynamic obstacles. In this work, we explore a graph-based framework to achieve control of real robot formations moving in a world cluttered with a variety of obstacles by introducing a new distributed algorithm for reconfiguring the formation shape. We systematically validate the reconfiguration algorithm using three real robots in scenarios of increasing complexity.

In this work, we present a novel approach to human-aware navigation by probabilistically modelling the uncertainty of perception for a social robotic system and investigating its effect on the overall social navigation performance. The model of the social costmap around a person has been extended to consider this new uncertainty factor, which has been widely neglected despite playing an important role in situations with noisy perception. A social path planner based on the fast marching method has been augmented to account for the uncertainty in the positions of people. The effectiveness of the proposed approach has been tested in extensive experiments carried out with real robots and in simulation. Real experiments have been conducted, given noisy perception, in the presence of single/multiple, static/dynamic humans. Results show how this approach has been able to achieve trajectories that are able to keep a more appropriate social distance to the people, compared to those of the basic navigation approach, and the human-aware navigation approach which relies solely on perfect perception, when the complexity of the environment increases. Accounting for uncertainty of perception is shown to result in smoother trajectories with lower jerk that are more natural from the point of view of humans.

The following paper addresses the problem of applying existing path planning methods targeting rough terrains. Most path planning methods for mobile robots divide the environment in two areas—free and occupied—and restrict the path to lie within the free space. The presented solution addresses the problem of path planning on rough terrains, where the local shape of the environment are used to both constrain and optimize the resulting path. Finding both the feasibility and the cost of the robot crossing the terrain at a given point is cast as an optimization problem. Intuitively, this problem models dropping the robot at a given location (x,y) and determining the minimal potential energy pose (attitude angles and the distance of the centre of mass to the ground). We then applied two path planning methods for computing a feasible path to a given goal: Fast Marching Method (FMM) and Rapidly exploring Random Tree (RRT). Processing the whole mapped area, determining the cost of every cell in the map, we apply a FMM in order to obtain a potential field free of local minima. This field can then be used to either pre-compute a complete trajectory to the goal point or to control, in real time, the locomotion of the robot. Solving the previously stated problem using RRT we need not to process the entire area, but only the coordinates of the nodes generated. This last approach does not require as much computational power or time as the FMM but the resulting path might not be optimal. In the end, the results obtained from the FMM may be used in controlling the vehicle and show optimal paths. The output from the RRT method is a feasible path to the goal position. Finally, we validate the proposed approach on four example environments.

Remote maintenance operations in the experimental fusion reactor ITER may require vehicle localization, for which one of the proposed methods is based on a network of Laser Range Finder sensor measurements. This localization method requires an accurate knowledge of each sensor pose (position and orientation). A deviation in sensor pose can compromise localization accuracy thereby recalibration procedure for the sensor poses is often necessary. This paper studies several calibration algorithms based on ICP. Simulation and experimental tests were carried out for different maps and situations regarding sensor pose uncertainty. The conclusion proposes the best suited algorithms for each scenario.

Several methods have been proposed in the literature to address the problem of automatic 3D reconstruction from depth data. Most methods rely on the minimization of the matching error among individual depth frames. However, ambiguity in sensor data often leads to erroneous matching (due to local minima), hard to cope with in a purely automatic approach. This paper proposes a semiautomatic approach, denoted interactive mapping, involving a human operator in the process of detecting and correcting erroneous matches. Instead of allowing the operator complete freedom in correcting the matching in a frame by frame basis, the proposed method constrains human intervention along the degrees of freedom with most uncertainty. This paper is targeted to 3D reconstruction from RGB-D data, such as the one provided by the Kinect sensor. The user is able to translate and rotate individual RGB-D point clouds, with the help of a force field-like reaction to the movement of each point cloud. Some preliminary results are presented, illustrating the advantages of the method.

In this video we briefly illustrate the progress and contributions made with our mobile, indoor, service robots CoBots (Collaborative Robots), since their creation in 2009. Many researchers, present authors included, aim for autonomous mobile robots that robustly perform service tasks for humans in our indoor environments. The efforts towards this goal have been numerous and successful, and we build upon them. However, there are clearly many research challenges remaining until we can experience intelligent mobile robots that are fully functional and capable in our human environments.

This paper proposes an alternative approach to common teleoperation methods found in search and rescue (SAR) robots. Using a head mounted display (HMD) the operator is capable of perceiving rectified images of the robot world in 3-D, as transmitted by a pair of stereo cameras onboard the robot. The HMD is also equipped with an integrated head-tracker, which permits controlling the robot motion in such a way that the cameras follow the operator's head movements, thus providing an immersive sensation to him. We claim that this approach is a more intuitive and less error prone teleoperation of the robot. The proposed system was evaluated by a group of subjects, and the results suggest that it may yield significant benefits to the effectiveness of the SAR mission. In particular, the user's depth perception and situational awareness improved significantly when using the HMD, and their performance during a simulated SAR operation was also enhanced, both in terms of operation time and on successful identification of objects of interest.

This work adds the concept of object to an existent low-level attention system of the humanoid robot iCub. The objects are defined as clusters of SIFT visual features. When the robot first encounters an unknown object, found to be within a certain (small) distance from its eyes, it stores a cluster of the features present within an interval about that distance, using depth perception. Whenever a previously stored object crosses the robot's field of view again, it is recognized, mapped into an egocentrical frame of reference, and gazed at. This mapping is persistent, in the sense that its identification and position are kept even if not visible by the robot. Features are stored and recognized in a bottom-up way. Experimental results on the humanoid robot iCub validate this approach. This work creates the foundation for a way of linking the bottom-up attention system with top-down, object-oriented information provided by humans.

This work describes a semi-autonomous robot for rescue operations, nicknamed RAPOSA (FOX in English). The robot was designed and built to operate in outdoor environments hostile to the human presence, such as debris resulting from the collapse of built structures, and is targeted to the tele-operated detection of potential survivors using a set of specific sensors whose information is transmitted to a remote human operator. RAPOSA's mechanical structure is composed of a main body and a front body, whose locomotion is supported on tracked wheels, allowing motion even when the robot is upside down. The front body has variable tilting capabilities, providing means to overcome edges higher than the robot main body (e.g., when climbing a stair) and is also useful to grab the lower ground when only the main body has ground contact. This front body has one thermal camera and two web cameras installed. Additional sensors include gas, temperature and humidity sensors, Web cams, light diodes, microphone and loudspeaker. The robot uses wireless communications, with an option for tethered operation. The tether carries both power and communications, with an access point on its end, and can also be used to suspend the robot inside a deep hole. Docking and undocking the robot to the tether is accomplished remotely by the operator with the help of a camera located inside the robot, and represents the most innovative feature of RAPOSA

The SocRob project was born as a challenge for multidisciplinary research on broad and generic approaches for the design of a cooperating society of robots, involving control, robotics and artificial intelligence researchers. In this paper we introduce some of the hardware options already taken by the group in the design of a robotic soccer team, chosen as our first case study. Each robot of the population is endowed with several sensors. The most important of them is vision. The others are linked to the main processing unit (a Pentium motherboard) by an i2c bus. Conceptual issues regarding the functional architecture of the team are also discussed. We propose a 3-level architecture, consisting of a set of context-switchable behaviors, each of them resulting of the composition of low-level task primitives.