Mario Gianni

Experience in System Design for Human-Robot Teaming in Urban Search and Rescue

The paper describes experience with applying a user-centric design methodology in developing systems for human-robot teaming in Urban Search & Rescue. A human-robot team consists of several robots (rovers/UGVs, microcopter/UAVs), several humans at an off-site command post (mission commander, UGV operators) and one on-site human (UAV operator). This system has been developed in close cooperation with several rescue organizations, and has been deployed in a real-life tunnel accident use case. The human-robot team jointly explores an accident site, communicating using a multi-modal team interface, and spoken dialogue. The paper describes the development of this complex socio-technical system per se, as well as recent experience in evaluating the performance of this system.

Rescue robots at earthquake-hit Mirandola, Italy: A field report

In May 2012, two major earthquakes occurred in the Emilia-Romagna region, Northern Italy, followed by further aftershocks and earthquakes in June 2012. This sequence of earthquakes and shocks caused multiple casualties, and widespread damage to numerous historical buildings in the region. The Italian National Fire Corps deployed disaster response and recovery of people and buildings. In June 2012, they requested the aid of the EU-funded project NIFTi, to assess damage to historical buildings, and cultural artifacts located therein. To this end, NIFTi deployed a team of humans and robots (UGV, UAV) in the red-area of Mirandola, Emilia-Romagna, from Tuesday July 24 until Friday July 27, 2012. The team worked closely together with the members of the Italian National Fire Corps involved in the red area. This paper describes the deployment, and experience.

Designing, developing, and deploying systems to support human–robot teams in disaster response

This paper describes our experience in designing, developing and deploying systems for supporting human–robot teams during disaster response. It is based on R&D performed in the EU-funded project NIFTi. NIFTi aimed at building intelligent, collaborative robots that could work together with humans in exploring a disaster site, to make a situational assessment. To achieve this aim, NIFTi addressed key scientific design aspects in building up situation awareness in a human–robot team, developing systems using a user-centric methodology involving end users throughout the entire R&D cycle, and regularly deploying implemented systems under real-life circumstances for experimentation and testing. This has yielded substantial scientific advances in the state-of-the-art in robot mapping, robot autonomy for operating in harsh terrain, collaborative planning, and human–robot interaction. NIFTi deployed its system in actual disaster response activities in Northern Italy, in July 2012, aiding in structure damage assessment. Graphical Abstract

Real-time Autonomous 3D Navigation for Tracked Vehicles in Rescue Environments

The paper presents a novel framework for 3D autonomous navigation for tracked vehicles. The framework takes care of clustering and segmentation of point clouds, traversability analysis, autonomous 3D path planning, motion planning and flippers control. Results illustrated in an experiment section show that the framework is promising to face harsh terrains. Robot performance is proved in three main experiments taken in a training rescue area, on fire escape stairs and in a non-planar testing environment, built ad-hoc to prove 3D path planning functionalities. Performance tests are also presented.

TRADR Project: Long-Term Human-Robot Teaming for Robot Assisted Disaster Response

This paper describes the project TRADR: Long-Term Human-Robot Teaming for Robot Assisted Disaster Response. Experience shows that any incident serious enough to require robot involvement will most likely involve a sequence of sorties over several hours, days and even months. TRADR focuses on the challenges that thus arise for the persistence of environment models, multi-robot action models, and human-robot teaming, in order to allow incremental capability improvement over the duration of a mission. TRADR applies a user centric design approach to disaster response robotics, with use cases involving the response to a medium to large scale industrial accident by teams consisting of human rescuers and several robots (both ground and airborne). This paper describes the fundamentals of the project: the motivation, objectives and approach in contrast to related work.

Dynamic obstacles detection and 3D map updating

We present a real time method for updating a 3D map with dynamic obstacles detection. Moving obstacles are detected through ray-casting on spherical voxelization of point clouds. We evaluate the accuracy of this method on a point cloud dataset, suitably constructed for testing ray-surface intersection under relative motion conditions. Moreover, we show the benefits of the map updating in a real robot equipped with a rotating LIDAR system, navigating in real world scenarios, populated by moving people.

An Augmented Reality approach for trajectory planning and control of tracked vehicles in rescue environments

In this paper we propose a framework for trajectory planning and control of tracked vehicles for rescue environments, based on Augmented Reality (AR). The framework provides the human operator with an AR-based interface that facilitates both 3D path planning and obstacle negotiation. The interface converts the 3D movements of a marker pen, handheld by the operator, into trajectories feasible for the tracked vehicle. The framework implements a trajectory tracking controller to allow the tracked vehicle to autonomously follow the trajectories, decided by the operator. This controller relies on a localization system which provides, at real-time, position feedback. The localization system exploits the performance of a Dead Reckoning System together with the accuracy of an ICP-based SLAM in pose estimation, to determine the pose of the tracked vehicle within the 3D map. We demonstrate the application of the planning framework in autonomous robot navigation for evaluating the robot capabilities in rescue environments. Our experiments show the effectiveness of the trajectory tracking control method.

Adaptive Robust Three-dimensional Trajectory Tracking for Actively Articulated Tracked Vehicles*

A new approach is proposed for an adaptive robust three-dimensional 3D trajectory-tracking controller design. The controller is modeled for actively articulated tracked vehicles AATVs. These vehicles have active sub-tracks, called flippers, linked to the ends of the main tracks, to extend the locomotion capabilities in hazardous environments, such as rescue scenarios. The proposed controller adapts the flippers configuration and simultaneously generates the track velocities, to allow the vehicle to autonomously follow a given feasible 3D path. The approach develops both a direct and differential kinematic model of the AATV for traversal task execution correlating the robot body motion to the flippers motion. The benefit of this approach is to allow the controller to flexibly manage all the degrees of freedom of the AATV as well as the steering. The differential kinematic model integrates a differential drive robot model, compensating the slippage between the vehicle tracks and the traversed terrain. The underlying feedback control law dynamically accounts for the kinematic singularities of the mechanical vehicle structure. The designed controller integrates a strategy selector too, which has the role of locally modifying the rail path of the flipper end points. This serves to reduce both the effort of the flipper servo motors and the traction force on the robot body, recognizing when the robot is moving on a horizontal plane surface. Several experiments have been performed, in both virtual and real scenarios, to validate the designed trajectory-tracking controller, while the AATV negotiates rubble, stairs, and complex terrain surfaces. Results are compared with both the performance of an alternative control strategy and the ability of skilled human operators, manually controlling the actively articulated components of the robot.

Deployment of ground and aerial robots in earthquake-struck Amatrice in Italy (brief report)

We provide key facts about the TRADR project deployment of ground and aerial robots in Amatrice, Italy, after the major earthquake in August 2016. The robots were used to collect data for 3D textured models of the interior and exterior of two badly damaged churches of high national heritage value.

A Stimulus-Response Framework for Robot Control

We propose in this article a new approach to robot cognitive control based on a stimulus-response framework that models both a robot’s stimuli and the robot’s decision to switch tasks in response to or inhibit the stimuli. In an autonomous system, we expect a robot to be able to deal with the whole system of stimuli and to use them to regulate its behavior in real-world applications. The proposed framework contributes to the state of the art of robot planning and high-level control in that it provides a novel perspective on the interaction between robot and environment. Our approach is inspired by Gibson’s constructive view of the concept of a stimulus and by the cognitive control paradigm of task switching. We model the robot’s response to a stimulus in three stages. We start by defining the stimuli as perceptual functions yielded by the active robot processes and learned via an informed logistic regression. Then we model the stimulus-response relationship by estimating a score matrix that leads to the selection of a single response task for each stimulus, basing the estimation on low-rank matrix factorization. The decision about switching takes into account both an interference cost and a reconfiguration cost. The interference cost weighs the effort of discontinuing the current robot mental state to switch to a new state, whereas the reconfiguration cost weighs the effort of activating the response task. A choice is finally made based on the payoff of switching. Because processes play such a crucial role both in the stimulus model and in the stimulus-response model, and because processes are activated by actions, we address also the process model, which is built on a theory of action. The framework is validated by several experiments that exploit a full implementation on an advanced robotic platform and is compared with two known approaches to replanning. Results demonstrate the practical value of the system in terms of robot autonomy, flexibility, and usability.