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.

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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. © 2013 IEEE

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 subtracks, 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. C 2015 Wiley Periodicals, Inc.

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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

Modeling cognitive control is a major issue in robot control, and it is about deciding when a task cannot succeed and a new task need to be initiated. These decisions are induced by incoming stimuli alerting of events taking place while the robot is executing its duties. To learn cognitive control we address the human inspired mechanisms that govern cognitive control and that have been widely studied in neuroscience, namely, shifting and inhibition. Shifting and inhibition are, in fact, executive cognitive functions responding selectively to stimuli, so as to switch from one activity to a more compelling one or to inhibit inappropriate urges and preserve focus on the current task. In an autonomous system these cognitive skills are crucial to assess a well-regulated reactive behavior, which is of particular relevance in critical circumstances. In this paper we illustrate a new method developed for learning shifting and inhibition, based on Gaussian Processes, and using examples provided by skilled operators. We finally show that the learning method is promising and can be seen as a new view for modeling robot reactive and proactive behaviors.