This paper describes a trajectory planning algorithm for mobile robot navigation in crowded environments; the aim is to solve the problem of planning a valid path through moving people. The proposed solution relies on an algorithm based on the Informed Optimal Rapidly-exploring Random Tree (InformedRRT*), where the planner continuously computes a valid path to navigate in crowded environments. While the robot executes the trajectory of the current path, this re-planning method always allows a feasible and optimal solution to be obtained. Compared to other state-of-the-art algorithms, this solution does not compute the entire path each time an obstacle is detected, instead it evaluating the current solution validity, i.e., the presence of moving obstacles on the current path; in this case the algorithm tries to repair the current solution. Only if the current path is completely unacceptable is a new path computed from scratch. Thanks to its reactivity, our solution always guarantees a valid path that brings the robot to the desired goal position. This dynamic approach is validated in a real case scenario where a mobile robot moves through a human crowd in a safe and reliable way.

This work a novel solutions that relies on autonomous robots with the aim of improving data centers power efficiency by providing an easy to use tool to perform environmental monitoring. Data center environmental monitoring has been highly explored in the last few years. In fact, due to the high power density managed by these buildings, precise monitoring is necessary to improve power efficiency without increasing the risk of hardware failure. This work proposes a robot system that is in charge of autonomously monitor temperature and humidity in the whole data center room environment. After that the robot have built a map of the data center room, it is able to localize in the map and performs localized measurements at different locations. Those measurements are stored and can be visualized by the user thanks to a web Graphical User Interface (GUI). The solution emulates an Environmental Sensor Network, a very popular solution for precise monitoring in these environments. The robot trajectories and the localized measurements can be easily reconfigured from the same GUI. For this reason, we name it a Virtual Sensor Network (VSN). In this work, we discuss the implementation choices and present some results collected during a use case experiment in a real environment.

Cloud robotics is a new approach to robotics that exploits the internet as a resource for parallel computing and data sharing. Robots are no more considered as isolated devices but now they can add new functionalities, communicate with the environment and share knowledge base. Exploiting this new technology a robot can also take advantages by off-loading heavy computations to the cloud, thus reducing hardware costs, power consumption. The Joint Open Lab on Connected Robotic Applications laB (JOL CRAB) is a research laboratory created by Telecom Italia in collaboration with Politecnico di Torino. The aim is to investigate technologies and develop concepts where the focus is mainly placed on the relationship between robots and the cloud computing, addressing issues that arise from the use of robotic services in public/enterprise environments not only of technological, legal, economical, sociological or psychological kind, but also related to ergonomics, cognitive perception, and relational experience. The collaboration between university and the industry led to different field trials where issues and feasibility of new services were evaluated and a community of stakeholders was created in the territory. We present these field trials, the different issues that arise and how that can be solved.

Data center monitoring has been a critical subject of research in recent years. In this paper we present a robotic system, based on the Robot Operating System (ROS), in which a mobile robot equipped with a laser range sensor and an Inertial Motion Unit (IMU) is able to autonomously navigate in a data center room for accurate monitoring of critical measurements, such as servers' external temperature, humidity and other physical quantities. The robot is able to autonomously create a map of a previously unknown room, localize therein and execute a list of measurements at different locations, which are provided by the user via a web Graphical User Interface (GUI). The application is based on a cloud robotics infrastructure which encloses the ROS nodes and exposes REST APIs to the user. We discuss our implementation choices with regards to the particular requirements of the scenario, both in terms of robot navigation and software infrastructure, and present some preliminary results in a real scenario.

Robotics education at M. Sc. courses is quite challenging, especially in the organization of intriguing laboratory activities about industrial manipulators, to accompany and complete traditional material and lessons on kinematics, dynamics, motion planning and control issues. Time and space constraints, the number of students attending the labs, and the available software and equipments (and their cost) can seriously limit the lab practices that can be actually offered in a Robotics course. On the basis of the experience matured at Politecnico di Torino, this paper describes how lab practices about manipulators can be organized by using low-cost structures, built up from the standard LEGO Mindstorms Kit, so to let the students become familiar with manipulators in different ways. Such practices are then well completed by activities with an industrial robot, available at Politecnico.

In this work we investigate the problem of Simultaneous Localization And Mapping (SLAM) for the case in which the information acquired by the robot is modeled as a network of constraints in a graphical model. Analyzing the resulting formulation we propose a closed-form approach to tackle the problem, which is proved to retrieve a first-order approximation of the actual nonlinear solution, under mild assumptions on the structure of the involved covariance matrices. The outcome of the analysis reveals several desirable properties of the proposed approach: no initial guess for optimization is needed and the technique is able to correctly estimate robot posterior also in presence of arbitrarily long loops. The approach is further validated by means of extensive simulations and real tests, and the consistency of the estimation process is also evaluated. We remark that this work is not intended to extend the already crowded literature on SLAM but is aimed at providing a consistent analytical insight, useful for efficiently attacking several open research issues, like active SLAM and exploration, for which the computational cost of simulating SLAM posterior still constitutes a troublesome bottleneck.

Robots and automated systems can be employed in the logistic field to efficiently perform common tasks like building and updating maps of indoor and outdoor logistic spaces, locating specific goods on the map, tracing the product flow in the area, while assuring the surveillance of the environment. This paper reports and discusses the already achieved results of the on-going research project MACP4Log (Mobile Autonomous and Cooperating robotic Platforms for supervision and monitoring of large LOGistic surfaces), aimed at the study and development of a prototype of a mobile robotic platform, with on-board vision systems and sensors, integrating a flexible wireless communication solution, able to move autonomously in large logistic spaces, and to communicate with a supervisor and other similar platforms in order to achieve a coordinated action to carry out specific tasks.

Multi robot systems are envisioned to play an important role in many robotic applications. A main prerequisite for a team deployed in a wide unknown area is the capability of autonomously navigate, exploiting the information acquired through the on-line estimation of both robot poses and surrounding environment model, according to Simultaneous Localization And Mapping (SLAM) framework. As team coordination is improved, distributed techniques for filtering are required in order to enhance autonomous exploration and large scale SLAM increasing both efficiency and robustness of operation. Although Rao-Blackwellized Particle Filters (RBPF) have been demonstrated to be an effective solution to the problem of single robot SLAM, few extensions to teams of robots exist, and these approaches are characterized by strict assumptions on both communication bandwidth and prior knowledge on relative poses of the teammates. In the present paper we address the problem of multi robot SLAM in the case of limited communication and unknown relative initial poses. Starting from the well established single robot RBPF-SLAM, we propose a simple technique which jointly estimates SLAM posterior of the robots by fusing the prioceptive and the eteroceptive information acquired by each teammate. The approach intrinsically reduces the amount of data to be exchanged among the robots, while taking into account the uncertainty in relative pose measurements. Moreover it can be naturally extended to different communication technologies (bluetooth, RFId, wifi, etc.) regardless their sensing range. The proposed approach is validated through experimental test.

Autonomous exploration under uncertain robot position requires the robot to plan a suitable motion policy in order to visit unknown areas while minimizing the uncertainty on its pose. The corresponding problem, namely active SLAM (Simultaneous Localization and Mapping) and exploration has received a large attention from the robotic community for its relevance in mobile robotics applications. In this work we tackle the problem of active SLAM and exploration with Rao-Blackwellized Particle Filters. We propose an application of Kullback-Leibler divergence for the purpose of evaluating the particle-based SLAM posterior approximation. This metric is then applied in the definition of the expected gain from a policy, which allows the robot to autonomously decide between exploration and place revisiting actions (i.e., loop closing). The technique is shown to enhance robot awareness in detecting loop closing occasions, which are often missed when using other state-of-the-art approaches. Results of extensive tests are reported to support our claims.

This paper presents the identification process aimed at estimating the model parameters of a COMAU Smart S2 industrial robot for controller design purposes. The principal challenges are given by the presence of a controller in-the-loop, and the absence of sensors to measure joint accelerations and velocities. A method valid in this context has been applied, and suitable trajectories were generated in order to avoid to excite the unmodelled plant dynamic while allowing to compute analytically the joint velocities and accelerations. This method was then applied to a real 6 degrees of freedom (DoF) industrial robot, estimating and using its parameters to design new improved model-based controllers.

Industrial robots use closed proprietary controller architecture that prevents the prototyping of advanced control algorithms when new sensor, such as force/torque or vision sensors, are used. The main bottleneck, apart from the closed architecture, consists in the real-time nature of the control task, that set tight temporal constraints usually unattainable by the limited sampling rate of force and vision sensors. The aim of this paper is to present and test a low-cost simple architecture able to perform both force/torque and vision-in-the-loop control, using an industrial COMAU Smart S2 6-dof articulated manipulator. Successful experiments were performed with various force control tasks and vision-based tracking of planar moving optical marks.

Different solutions to the robot control problem by using only position measurements have been studied in literature. The performances of some linear and nonlinear observers previously proposed are discussed in this paper, on the basis of the experimental results obtained in the control of a SCARA two-link manipulator with two revolute joints. The comparison is carried out by using the observed velocities to implement a linear state feedback algorithm for the robot control. The obtained results show that different factors (e.g. the required task, the available knowledge of the robot dynamic model) may influence the choice of a particular observer.

This paper addresses the problem of finding whether a given set of three-dimensional (3D) vectors (the object) can be brought to match a second set of vectors (the template) by means of an affine motion, minimizing a measure of the mismatch error and satisfying an assigned set of geometrical constraints. This problem is encountered in many applications of computer vision, robotics, and manufacturing processes, and has been tackled by several authors in the unconstrained case. Spherical, ellipsoidal and polyhedral constraints are here introduced in the problem, and a solution scheme based on an efficient convex optimization algorithm is proposed. An example of application of the proposed methodology to a manufacturing tolerancing problem is also provided.

This paper presents a new formulation, in terms of an integrated finite element model, of a mechanical beam with surface bonded piezoelectric sensors and actuators. Lagrange equation considering both mechanical and piezoelectric energies is used to derive the element dynamic equation which, assembled with the state space formulation of the electronic circuit, yields the overall state space equation. Modal reduction, including static deflections modes, is introduced to lower the model order. An experimental verification has been carried out on a simple beam with a bonded polyvinylidene fluoride layer acting as a sensor. The results have shown a very good accordance between the model and the real system.>

The authors present an algorithm for hybrid force-position control of a robot manipulator using the operational space formulation approach. The proposed scheme modifies and integrates a previous algorithm due to O. Khatib (1987) and to Khatib and J. Burdick (1986), to achieve an exact decoupling of the force and position control along the task subspaces. The theoretical results presented demonstrate the viability of this approach, which was confirmed by simulation results. These provided evidence that the decoupling between force and position was exact, with no noticeable increase in the required computational burden.>

The model of a manipulator with four degrees of freedom and a long flexible arm that includes the coupling effects between rigid and flexible links is used to design a decentralized control scheme. An additional control scheme is included to suppress the elastic variation of the flexible arm. The control scheme does not rely on the dynamic model and payload parameters since disturbance estimation and rejection techniques are used. The simulation results show that the model is accurate enough to design the controller and to perform further analysis on the system. The proposed controller closely tracks the desired input under variations of configuration, payload, and input reference signal.>

Some experiments on the integration of algorithmic techniques with knowledge-based ones are discussed. Two case studies are presented: an FMS cell and a press shop. It was found that the algorithmic procedures developed for production scheduling resulted in limiting the ability to cope with the complexity of the real manufacturing world. The scheduling problem, seen as a constraint satisfaction problem, can be approached with rule-based techniques. Nevertheless, algorithmic techniques are found to be valuable for their efficiency and ability to deal with aggregated data. This ability is fundamental for an efficient implementation of hierarchical control systems in general and in the manufacturing context in particular. This suggests that the integration of rule-based techniques with algorithmic ones can increase the efficiency of searching in the space of possible solutions. The ability to deal with aggregated data can have little value when detailed real-time operation scheduling is needed. In this case, simple dispatching rules are often used, and sophisticated operations research methods are not used. In such a dynamic situation, a purely-rule based approach may be more suitable.>

This paper considers the problem of stopping rules, in the context of sequential Bayesian classification. In particular a new criterion, based on the probability of reversal of the obtained classification, is introduced and compared to more commonly used strategies. The results show good behavior of the proposed technique, with both simulated and real data drawn from biomedical application. In fact it appears that this stopping rule reduces the misallocation error rate with the same mean number of used features, or conversely, with an equal level of misallocation error rate, it reduces the mean number of features necessary to attain it.

The municipality of an Italian town is trying to apply a new procedure for the assignment of financial resources to decentralized urban districts, with the aim of improving the effectiveness of intervention in social services, and reducing the differences between zones. The main features of this project are a) to change from a procedure based on sectorial budget allocation to a procedure based on comprehensive "zonal needs;" b) to give urban districts the power to make the decision of suballocation of the assigned amount of resources, so experiencing a "participative" process of urban management; and c) to create a two-level informative structure, integrated with the planning process. A model is presented which evaluates comparatively the amount of resources to be assigned in every district for local plans, comparing "social demand" of public services with the "offer" of corresponding facilities in each district. Both demand and offer are reported to the socioeconomic conditions of district population and are influenced by other general context evaluations. The model has been constructed within this "participative" process; its application has produced results quoted as "satisfactory" by the actors in the decision process.