Using UAVs has recently emerged as a cost-efficient solution to assist first responders in search and rescue missions. Victims are usually equipped with wireless devices, which makes RF sensing an efficient solution for their localization in disaster situations. Nevertheless, the success of existing methods is highly diminished by the noisy nature of RF measurements. While leveraging recent advancements in lightweight gas sensing, we present in this ongoing work paper a novel localization approach that efficiently combines RF measurements with victim odor information while accounting for the dynamic nature of measurements' quality. We discuss the approach design, early simulation results, and ongoing experimental evaluation.

In this paper, we tackle the problem of localizing the source of a three-dimensional signal field with a team of mobile robots able to collect noisy measurements of its strength and share information with each other. The adopted strategy is to cooperatively compute a closed-form estimation of the gradient of the signal field that is then employed to steer the multi-robot system toward the source location. In order to guarantee an accurate and robust gradient estimation, the robots are placed on the surface of a sphere of fixed radius. More specifically, their positions correspond to the generators of a constrained Centroidal Voronoi partition on the spherical surface. We show that, by keeping these specific formations, both crucial geometric properties and a high level of field coverage are simultaneously achieved and that they allow estimating the gradient via simple analytic expressions. We finally provide simulation results to evaluate the performance of the proposed approach, considering both noise-free and noisy measurements. In particular, a comparative analysis shows how its higher robustness against faulty measurements outperforms an alternative state-of-the-art solution.

Simulation, a common practice to evaluate au-tonomous vehicles, requires to specify realistic scenarios, in par-ticular critical ones, occurring rarely and potentially dangerous to reproduce on the road. Such scenarios may be either generated randomly, or specified manually. Randomly generating scenarios is easy, but their relevance might be difficult to assess. Manually specified scenarios can focus on a given feature, but their design might be difficult and time-consuming, especially to achieve satisfactory coverage. In this work, we propose an automatic approach to generate a large number of relevant critical scenarios for autonomous driving simulators. The approach is based on the generation of behavioral conformance tests from a formal model (specifying the ground truth configuration with the range of vehicle behaviors) and a test purpose (specifying the critical feature to focus on). The obtained abstract test cases cover, by construction, all possible executions exercising a given feature, and can be automatically translated into the inputs of autonomous driving simulators. We illustrate our approach by generating thousands of behavior trees for the CARLA simulator for several realistic configurations.

Covering a given area with a team of mobile robots in a minimum time is a well-studied problem with many real-world applications. A rarely studied subject, however, is the case of a weighted plane: due to the necessity of taking time-consuming measurements or having to traverse different kinds of terrains, the coverage time may vary over the environment and the path planning needs to be adapted accordingly. In this paper, we present an adapted version of a state-of-the-art mCPP (multi-robot coverage path planning) approach, the DARP algorithm, to make it suitable to deal with weighted environments. In particular, we propose several modifications to DARP that allow overcoming some of its limitations and, as a result, obtain an increased convergence rate and decreased convergence time with respect to the original version. Furthermore, as proved by extensive simulations, these improvements are also noticed in the unweighted version of the problem.

The ability to detect objects, under image corruptions and different weather conditions is vital for deep learning models especially when applied to real-world applications such as autonomous driving. Traditional RGB-based detection fails under these conditions and it is thus important to design a sensor suite that is redundant to failures of the primary frame-based detection. Event-based cameras can complement frame-based cameras in low-light conditions and high dynamic range scenarios that an autonomous vehicle can encounter during navigation. Accordingly, we propose a redundant sensor fusion model of event-based and frame-based cameras that is robust to common image corruptions. The method utilizes a voxel grid representation for events as input and proposes a two-parallel feature extractor network for frames and events. Our sensor fusion approach is more robust to corruptions by over 30% compared to only frame-based detections and outperforms the only event-based detection. The model is trained and evaluated on the publicly released DSEC dataset.

This paper addresses a path planning problem for a fleet of Unmanned Aerial Vehicles (UAVs) that uses both prior information and online gathered data to efficiently inspect large surfaces such as ship hulls and water tanks. UAVs can detect corrosion patches and other defects on the surface from low-resolution images. If defects are detected, they get closer to the surface for a high-resolution inspection. The prior information provides expected defects locations and is affected by both false positives and false negatives. The mission objective is to prioritize the close-up inspection of defected areas while keeping a reasonable time for the coverage of the entire surface. We propose two solutions to this problem: a coverage algorithm that divides the problem into a set of Traveling Salesman Problems (Part-TSP) and a cooperative frontier approach that introduces frontier utilities to incorporate the prior information (Coop-Frontier). We finally provide extensive simulation results to analyze the performance of these approaches and compare them with alternative solutions. These results suggest that both Part-Tspand Coop-Frontier perform better than the baseline solution. Part-Tsphas the best performance in most cases. However, coop-Frontier is preferable in extreme cases because more robust to inhomogeneous corrosion distribution and imperfect information.

Testing and validating advanced automotive software is of paramount importance to guarantee safety and quality. While real-world testing is highly demanding and simulation testing is not reliable, we propose a new augmented reality framework that takes advantage of both environments. This new testing methodology is intended to be a bridge between Vehicle-in-the-Loop and real-world testing. It enables to easily and safely place the whole vehicle and all its software, from perception to control, in realistic test conditions. This framework provides a flexible way to introduce any virtual element in the outputs of the sensors of the vehicle under test. For each modality of sensing, the framework requires a real time augmentation function that preserves real sensor data and enhances them with virtual data. The LiDAR data augmentation function is presented together with its implementation details. Relying on both qualitative and quantitative analysis of experimental results, the representability of tests scenes generated by the augmented reality framework is finally proven.

Reliably predicting future occupancy of highly dynamic urban environments is an important precursor for safe autonomous navigation. Common challenges in the prediction include forecasting the relative position of other vehicles, modelling the dynamics of vehicles subjected to different traffic conditions, and vanishing surrounding objects. To tackle these challenges, we propose a spatio-temporal prediction network pipeline that takes the past information from the environment and semantic labels separately for generating future occupancy predictions. Compared to the current SOTA, our approach predicts occupancy for a longer horizon of 3 seconds and in a relatively complex environment from the nuScenes dataset. Our experimental results demonstrate the ability of spatiotemporal networks to understand scene dynamics without the need for HD-Maps and explicit modeling dynamic objects. We publicly release our occupancy grid dataset based on nuScenes to support further research.

A crucial aspect that automotive systems need to face before being used in everyday life is the validation of their components. To this end, standard exhaustive methods are inappropriate to validate the probabilistic algorithms widely used in this field and new solutions need to be adopted. In this paper, we present an approach based on Statistical Model Checking (SMC) to validate the collision risk assessment generated by a probabilistic perception system. SMC represents an intermediate between test and exhaustive verification by relying on statistics and evaluates the probability of meeting appropriate Key Performance Indicators (KPIs) based on a large number of simulations. As a case study, a state-of-the-art algorithm is adopted to obtain the collision risk estimations. This algorithm provides an environment representation through Bayesian probabilistic occupancy grids and estimates positions in the near future of every static and dynamic part of the grid. Based on these estimations, time-to-collision probabilities are then associated with the corresponding cells. Using CARLA simulator, a large number of execution traces are then generated, considering both collisions and almost-collisions in realistic urban scenarios. Real experiments complete the analysis and show the reliability of the simulation results.

This paper addresses the problem of exploring unknown terrains with a fleet of cooperating aerial vehicles. We present a novel decentralized approach which alternates gradient-free stochastic optimization and a frontier-based approach. Our method allows each robot to generate its trajectory based on the collected data and the local map built integrating the information shared by its teammates. Whenever a local optimum is reached, which corresponds to a location surrounded by already explored areas, the algorithm identifies the closest frontier to get over it and restarts the local optimization. Its low computational cost, the capability to deal with constraints and the decentralized decision-making make it particularly suitable for multi-robot applications in complex 3D environments. Simulation results show that our approach generates feasible trajectories which drive multiple robots to completely explore realistic environments. Furthermore, in terms of exploration time, our algorithm significantly outperforms a standard solution based on closest frontier points while providing similar performances compared to a computationally more expensive centralized greedy solution.

Automotive systems must undergo a strict process of validation before their release on commercial vehicles. With the increased use of probabilistic approaches in autonomous systems, standard validation methods are not applicable to this end. Furthermore, real life validation, when even possible, implies costs which can be obstructive. New methods for validation and testing are thus necessary. In this paper, we propose a generic method to evaluate complex probabilistic frameworks for autonomous driving. The method is based on Statistical Model Checking (SMC), using specifically defined Key Performance Indicators (KPIs), as temporal properties depending on a set of identified metrics. By studying the behavior of these metrics during a large number of simulations via our statistical model checker, we finally evaluate the probability for the system to meet the KPIs. We show how this method can be applied to two different subsystems of an autonomous vehicle: a perception system and a decision-making approach. An overview of these two systems is given to understand related validation challenges. Extensive validation results are then provided for the decision-making case.

This paper deals with the problem of estimating in a collaborative way the gradient and the Hessian matrix of an unknown signal via noisy measurements collected by a group of robots. We propose symmetric formations with a reduced number of robots for both the two-dimensional (2-D) and the three-dimensional (3-D) cases, such that the gradient and Hessian of the signal are estimated at the center of the formation via simple computation on local quantities independently of the orientation of the formation. If only gradient information is required, the proposed formations are suitable for mobile robots that need to move in circular motion. We also provide explicit bounds for the approximation error and for the noise perturbation that can be used to optimally scale the formation radius. Numerical simulations illustrate the performance of the proposed strategy for source seeking against alternative solutions available in the literature and show how Hessian estimation can provide faster convergence even in the presence of noisy measurements.

We study the problem of monitoring the evolution of atmospheric variables within low-altitude cumulus clouds with a fleet of Unmanned Aerial Vehicles (UAVs). To tackle this challenge, two main problems can be identified: i) creating on-line maps of the relevant variables, based on sparse local measurements; ii) designing a planning algorithm which exploits the obtained map to generate trajectories that optimize the adaptive data sampling process, minimizing the uncertainty in the map, while steering the vehicles within the air flows to generate energetic-efficient flights. Our approach is based on Gaussian Processes (GP) for the mapping, combined with a stochastic optimization scheme for the trajectories generation. The system is tested in simulations carried out using a realistic three-dimensional current field. Results for a single UAV as well as for a fleet of multiple UAVs, sharing information to cooperatively achieve the mission, are provided.

This paper addresses a fundamental search problem in which a searcher subject to time and energy constraints tries to find a mobile target. The target's motion is modeled as a random walk on a discrete set of points on a line segment. At each time step, the target chooses one of the adjacent nodes at random and moves there. We study two detection models. In the no-crossing model, the searcher detects the target if it is on the same node or if it takes the same edge at the same time. In the crossing model, detection happens only if the target lands on the same node at the same time. For the no-crossing model, where move and stay actions may have different costs, we present an optimal search strategy under energy and time constraints. For the crossing model, we formulate the problem of designing an optimal strategy as a partially observable Markov decision process (POMDP) and solve it using methods that reduce the state-space representation of the belief. The POMDP solution reveals structural properties of the optimal solution. We use this structure to design an efficient strategy and analytically study its performance. Finally, we present preliminary experimental results to demonstrate the applicability of our model to our tracking system, which is used for finding radio-tagged invasive fish.

This paper studies the problem of searching for an unknown moving target in a bounded two-dimensional convex area with a mobile robot. A key component of designing a search strategy is the target motion model, which is often unknown in practical scenarios. When designing search strategies, researchers either (1) ignore the target motion and treat the target as a stationary object with unknown location, (2) treat the target as an adversary and model the search task as a game, or (3) use a stochastic model such as a random walk. For each of these models we analyze possible search paths with the objective of minimizing the expected capture time. Our intent is to investigate how the choice of the model influences the choice of the strategy and consequently how the capture time will depend on this choice. In addition to a theoretical analysis, we compare the strategies in simulation.

We present deterministic strategies for capturing a target performing a discrete random walk on a discretized line segment. The searcher has a limited time budget. Its goal is to maximize the probability of capturing the target within the budget. A challenging aspect of our model is that the target can cross the searcher without being captured when they take the same edge at the same time in opposite directions. We present a Partially Observable Markov Decision Process (POMDP) approach for finding the optimal search strategy. We also present an efficient approximate solution to the POMDP. The strategies found by this approach reveal structural properties of the efficient search strategies which we exploit to solve the problem efficiently without running the POMDP.

In this paper we study the problem of designing search strategies to find a target whose motion is described by a random walk along a one-dimensional bounded environment. The sensing model and the characteristic of the environment require the searcher and the target to be on the same site at the same time to guarantee capture. The objective is to optimize the searcher's motion, given by a sequence of actions (move right, left or remain stationary), so that the probability of capturing the target is maximized. Each action is associated with an energy cost. The searcher strategy is constrained by a total energy budget. We propose a class of randomized strategies for which we provide an analytical expression for the capture probability as a function of a single parameter. We then use this expression to find the best strategy within this class. In addition to theoretical results, the algorithms are analyzed in simulation and compared with other intuitive solutions.

The objective of this paper is to present a strategy to safely move a robot in an unknown and complex environment where people are moving and interacting. The robot, by using only its sensor data, must navigate respecting humans' comfort. To obtain good results in such a dynamic environment, a prediction on humans' movement is also crucial. To solve all the aforementioned problems we introduce a suitable cost function. Its optimization is obtained by using a new stochastic and adaptive optimization algorithm (CAO). This method is very useful in particular when the analytical expression of the optimization function is unknown but numerical values are available for any state configuration. Additionally, the proposed method can easily incorporate any dynamical and environmental constraints. To validate the performance of the proposed solution, several simulation results are provided.

The SFly project is an EU-funded project, with the goal to create a swarm of autonomous vision controlled micro aerial vehicles. The mission in mind is that a swarm of MAV's autonomously maps out an unknown environment, computes optimal surveillance positions and places the MAV's there and then locates radio beacons in this environment. The scope of the work includes contributions on multiple different levels ranging from theoretical foundations to hardware design and embedded programming. One of the contributions is the development of a new MAV, a hexacopter, equipped with enough processing power for onboard computer vision. A major contribution is the development of monocular visual SLAM that runs in real-time onboard of the MAV. The visual SLAM results are fused with IMU measurements and are used to stabilize and control the MAV. This enables autonomous flight of the MAV, without the need of a data link to a ground station. Within this scope novel analytical solutions for fusing IMU and vision measurements have been derived. In addition to the realtime local SLAM, an offline dense mapping process has been developed. For this the MAV's are equipped with a payload of a stereo camera system. The dense environment map is used to compute optimal surveillance positions for a swarm of MAV's. For this an optimiziation technique based on cognitive adaptive optimization has been developed. Finally, the MAV's have been equipped with radio transceivers and a method has been developed to locate radio beacons in the observed environment.

This paper deals with the problem of deploying a team of flying robots to perform surveillance coverage missions over a terrain of arbitrary morphology. In such missions, a key factor for the successful completion is the knowledge of the terrain's morphology. In this paper, we introduce a two-step centralized procedure to align optimally a swarm of flying vehicles for the aforementioned task. Initially, a single robot constructs a map of the area of interest using a novel monocular-vision-based approach. A state-of-the-art visual-SLAM algorithm tracks the pose of the camera while, simultaneously, building an incremental map of the surrounding environment. The map generated is processed and serves as an input in an optimization procedure using the cognitive adaptive methodology initially introduced in [1], [2]. The output of this procedure is the optimal arrangement of the robot team, which maximizes the monitored area. The efficiency of our approach is demonstrated using real data collected from aerial robots in different outdoor areas.

This paper investigates the problem of determining the speed and the attitude of a vehicle equipped with a monocular camera and inertial sensors. The vehicle moves in a 3D unknown environment. It is shown that, by collecting the visual and inertial measurements during a very short time interval, it is possible to determine the following physical quantities: the vehicle speed and attitude, the absolute distance of the point features observed by the camera during the considered time interval and the bias affecting the inertial measurements. In particular, this determination, is based on a closed form solution which analytically expresses the previous physical quantities in terms of the sensor measurements. This closed form determination allows performing the overall estimation in a very short time interval and without the need of any initialization or prior knowledge. This is a key advantage since allows eliminating the drift on the absolute scale and on the vehicle orientation. In addition, the paper provides the minimum number of distinct camera images which are needed to perform this determination. Specifically, if the magnitude of the gravity is unknown, at least four camera images are necessary while if it is a priori known, three camera images are necessary. The performance of the proposed approach is evaluated by using real data.

In this paper, the problem of positioning a team of mobile robots for a surveillance task in a non-convex environment with obstacles is considered. The robots are equipped with global positioning capabilities (for instance they are equipped with GPS) and visual sensors able to monitor the surrounding environment. Furthermore, they are able to communicate one with each other. The goal is to maximize the area monitored by the team, by identifying the best configuration of the team members. Due to the non-convex nature of the problem, an analytical solution can not be obtained. The proposed method is based on a new cognitive-based, adaptive optimization algorithm (CAO). This method allows getting coordinated and scalable controls to accomplish the task, even when the obstacles are unknown and the team is heterogeneous, i.e. each robot is equipped with a different type of visual sensor. Extensive simulations are presented to show the efficiency of the proposed approach.