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Mobile robotic systems have evolved to include sensors capable of truthfully describing robot status and operating environment as accurately and reliably as never before. This possibility is challenged by effective sensor data exploitation, because of the cognitive load an operator is exposed to, due to the large amount of data and time-dependency constraints. This paper addresses this challenge in remote-vehicle teleoperation by proposing an intuitive way to present sensor data to users by means of using mixed reality and visual aids within the user interface. We propose a method for organizing information presentation and a set of visual aids to facilitate visual communication of data in teleoperation control panels. The resulting sensor-information presentation appears coherent and intuitive, making it easier for an operator to catch and comprehend information meaning. This increases situational awareness and speeds up decision-making. Our method is implemented on a real mobile robotic system operating outdoor equipped with on-board internal and external sensors, GPS, and a reconstructed 3D graphical model provided by an assistant drone. Experimentation verified feasibility while intuitive and comprehensive visual communication was confirmed through an assessment, which encourages further developments.

The problem of autonomous navigation of a ground vehicle in unstructured environments is both challenging and crucial for the deployment of this type of vehicle in real-world applications. Several well-established communities in robotics research deal with these scenarios such as search and rescue robotics, planetary exploration, and agricultural robotics. Perception plays a crucial role in this context, since it provides the necessary information to make the vehicle aware of its own status and its surrounding environment. We present a review on the recent contributions in the robotics literature adopting learning-based methods to solve the problem of environment perception and interpretation with the final aim of the autonomous context-aware navigation of ground vehicles in unstructured environments. To the best of our knowledge, this is the first work providing such a review in this context.

In this paper an Integrated Platform of Assistive Technology supporting elderly and people with impairments in their own premises is presented. The platform consists of a network of interconnected and sensor-based intelligent objects, with the purpose of monitoring parameters of end-users and their living environment. Both wearable (smartwatch, RFID reader, ECG) and environmental (environmental monitoring, passive RFID tags, smart switches, smart Reed relays, power monitors, RGB-D cameras, seat-posture monitoring) sensors and devices have been installed in the Living Labs. Data collected by the sensors are conveyed to a remote server, hosting also smart paradigms to extract suitable information related to the status and habits of end-users. Such information is then made available to caregivers. The architecture presented through this paper represents an example of a customizable and flexible assistive system, aimed to improve the life quality of elderly through the implementation of the “Smart Home concept”, which is strategic to promote an Active and Healthy Ageing (AHA) society.

In this paper an assistive device for the mobility support of the elderly and people with impairments is presented. The device is a Smart Walker developed for the Assistive Technology context, within the framework of the NATIFLife project. The hardware architecture of the system, the algorithm implemented for both static and dynamic obstacles avoidance and the experimental results are reported.

In recent years, drones have become widely used in many fields. Their vertical flight capability makes these systems suitable for carrying out a variety of tasks. In this paper, the delivery service they provide is analysed. The delivery of goods quickly and to remote areas is a relevant application scenario; however, the systems proposed in the literature use electromagnets, which affect the duration of the flight. In addition, these devices are heavy and suffer from high energy consumption, which reduces the maximum transportable payload. This study proposes a new lightweight magnetic plate composed of permanent magnets, capable of collecting and positioning any object as long as it has a ferromagnetic surface on the top. This plate was developed for the Mohamed Bin Zayed International Robotics Challenge 2020, an international robotics competition for multi-robot systems. Challenge two of this competition required a drone capable of picking up different types of bricks and assembling them to build a wall according to an assigned pattern. The bricks were of different colours and sizes, with weights ranging from 1 to 2 kg. In light of this, it was concluded that weight was the most relevant specification to consider in drone design.

In the last decades a considerable interest has been paid in investigating the pros and cons of adopting tethered mobile robotic platforms. Tethers have been proposed for both data communication and power supply, especially in applications where a radio communication link cannot be used or when a longer autonomy is needed. Typically, tethered single robots have been presented in the literature, however, recently more interesting solutions appeared, proposing multiple heterogeneous platforms connected through a tether [1], [2]. In a system of an Unmanned Ground Vehicle (UGV) cooperating with an Unmanned Aerial Vehicle (UAV) another relevant aspect to take into consideration is the control approach. A centralized control scheme for a target-chaser formation, involving a UAV and a UGV has been presented in [3], and several solutions have been proposed to address the problem of tracking a ground vehicle from an aerial platform, even for autonomous departure and landing [4], [5]., https://youtu.be/trxEU4xwn0Y

This work describes a lightweight magnetic plate for the pick and place task of objects with a ferromagnetic surface. The plate can be installed on any type of platform, namely both on ground and aerial vehicles. Its structure makes it fully customizable in size or attraction power. The main contributions of the proposed solution are a) the lightness of the device and b) the reduced power consumption required, compared to other typical solutions used in pick and place operations. This is even more relevant in long time operations where suction grippers or electromagnets would require an intensive power consumption. In this paper the design and implementation of the system along with the experimental results are presented.

Being able to estimate the traversability of the area surrounding a mobile robot is a fundamental task in the design of a navigation algorithm. However, the task is often complex, since it requires evaluating distances from obstacles, type and slope of terrain, and dealing with non-obvious discontinuities in detected distances due to perspective. In this paper, we present an approach based on deep learning to estimate and anticipate the traversing score of different routes in the field of view of an on-board RGB camera. The backbone of the proposed model is based on a state-of-the-art deep segmentation model, which is fine-tuned on the task of predicting route traversability. We then enhance the model's capabilities by a) addressing domain shifts through gradient-reversal unsupervised adaptation, and b) accounting for the specific safety requirements of a mobile robot, by encouraging the model to err on the safe side, i.e., penalizing errors that would cause collisions with obstacles more than those that would cause the robot to stop in advance. Experimental results show that our approach is able to satisfactorily identify traversable areas and to generalize to unseen locations., Accepted at IROS 2020

Surveying with unmanned aerial vehicles flying close to the terrain is crucial for the collection of details that are not visible when flying at higher altitudes. This type of missions can be applied in several scenarios such as search and rescue, precision agriculture, and environmental monitoring, to name a few. We present a strategy for the generation of low-altitude trajectories for terrain following. The trajectory is generated taking into account the morphology of the area of interest, represented as a georeferenced Digital Surface Model (DSM), while ensuring a safe separation from any obstacle. The surface model of the scenario is created by using a UAV-based photogrammetry software, which processes the images acquired during a preliminary mission at high altitude. The solution was developed, tested, and verified both in simulation and in real scenarios with a multirotor equipped with low-cost sensing. The experimental results proved the validity of the generation of trajectories at altitudes lower than most of the works available in the literature. The images acquired during the low-altitude mission were processed to obtain a high-resolution reconstruction of the area as a representative application result.

Robotics solutions may provide a valuable support to combat the spread of the current corona virus pandemic. This can be mainly achieved by avoiding the direct contact between humans, for instance during typical body temperature checks or throughout a process of swabbing. In this paper we present a strategy for the temperature checking of people in public areas leveraging a multi-robot system. The system is composed of an aerial vehicle tethered to a ground mobile platform. The physical link between the two vehicles introduces further constraints in the autonomous navigation of the system compared to common individually cooperating vehicles. On the other hand, a long flight autonomy can be achieved. The paper reports some crucial aspects to consider both in the localization and the control strategy that have been addressed during the system development.

This paper presents a system for Complete Coverage Path Planning to survey an outdoor area with a flock of Unmanned Aerial Vehicles (UAVs). The proposed system is able to manage the presence of no-fly zones, by decomposing the area into smaller subregions via a Morse-based decomposition approach. Furthermore, negotiation for UAV-subregion assignment is managed, taking into account environment geometry and both starting and desired final positions for the UAVs, once the coverage mission is completed. The back-and-forth motion is chosen as coverage pattern inside each subregion. All these features have been implemented with an integrated software framework, capable of working with georeferenced maps and data structures, thus obtaining a software tool compatible with commonly used geographic information systems.

In rough terrains, such as landslides or volcanic eruptions, it is extremely complex to plan safe trajectories for an Unmanned Ground Vehicle (UGV), since both robot stability and path execution feasibility must be guaranteed. In this paper, we present a complete solution for the autonomous navigation of ground vehicles in the mentioned scenarios. The proposed solution integrates three different aspects. The first is the coverage path planning for the definition of UAV trajectories for aerial imagery acquisition. The collected images are used for the photogrammetric reconstruction of the considered area. The second aspect is the adoption of a flock of UAVs to implement the coverage in a parallel way. In fact, when non-coverable zones are present, decomposition of the whole area to survey is performed. A solution to assign the different regions among the flying vehicles composing the team is presented. The last aspect is the path planning of the ground vehicle by means of a traversability analysis performed on the terrain 3D model. The computed paths are optimal in terms of the difficulty of moving across the rough terrain. The results of each step within the overall approach are shown.

Smart Walkers are a category of robotic assistive devices particularly useful for the elderly. These systems help the users in locomotion and with respect to the classical walkers, reduce the risk of fall. This work presents a new system that has been developed using low-cost time of flight laser sensors in place of the more expensive laser scanners. Control of direction is performed by guiding the user by mean of brakes on the rear wheels. Other sensors include an inertial measurement unit with magnetometer and an interface with an external absolute localization system. Preliminary trials have been performed on a graphical and physical simulator implemented in the V-Rep framework.

In this paper, a strategy for the mission planning of a UAV swarm in 3-D environments is presented. The operating space is represented as a 3-D grid and the A* algorithm is used; paths are planned for each UAV towards any possible target and the optimal combination, in terms of reducing the path length for the whole swarm, is considered. Further features and algorithm enhancements have been developed for reducing computational time, improving planned trajectories, and for the automatic redistribution of targets. The intended application is the deployment of UAVs for landslide monitoring and search of people involved in catastrophic events.

This paper describes the approach employed to implement the autonomous landing of an Unmanned Aerial Vehicle (UAV) upon a moving ground vehicle. We consider an application scenario in which a target, made of a visual pattern, is mounted on the top of a ground vehicle which roams in an arena using a certain path and velocity; the UAV is asked to find the ground vehicle, by detecting the visual pattern, and then to track it in order to perform the approach and finalize the landing. To this aim, Computer Vision is adopted to perform both detection and tracking of the visual target; the algorithm used is based on the TLD (Tracking-Learning-Detection) approach, suitably integrated with an Hough Transform able to improve the precision of the identification of the 3D coordinates of the pattern. The output of the Computer Vision algorithm is then exploited by a Kalman filter which performs the estimation of the trajectory of the ground vehicle in order to let the UAV track, follow and approach it. The paper describes the software and hardware architecture of the overall application running on the UAV. The application described has been practically used with success in the context of the “Mohamed Bin Zayed” International Robotic Challenge (MBZIRC) which took place in March 2017 in Abu Dhabi.