Jonathan Cacace

Impedance control of VToL UAVs with a momentum-based external generalized forces estimator

An estimator of external generalized forces (force plus moments) acting on aerial platforms, and based on the momentum of the mechanical system, is proposed for the control of VToL UAVs together with a hierarchical architecture separating the translational and rotational dynamics of the vehicle. The closed-loop system equations are shaped as mechanical impedances, programmable through the controller gains, and forced by the residuals given by the estimation error. This arrangement allows the VToL UAVs to perform hovering and tracking tasks without a precise knowledge of the vehicle dynamics and in presence of external disturbances and unmodeled aerodynamic effects. Experiments are presented to evaluate the performance of the proposed control design.

Hybrid visual servoing with hierarchical task composition for aerial manipulation

In this letter, a hybrid visual servoing with a hierarchical task-composition control framework is described for aerial manipulation, i.e., for the control of an aerial vehicle endowed with a robot arm. The proposed approach suitably combines into a unique hybrid-control framework the main benefits of both image-based and position-based control schemes. Moreover, the underactuation of the aerial vehicle has been explicitly taken into account in a general formulation, together with a dynamic smooth activation mechanism. Both simulation case studies and experiments are presented to demonstrate the performance of the proposed technique.

A mixed-initiative control system for an Aerial Service Vehicle supported by force feedback

We present an approach to mixed initiative control for unmanned aerial vehicles (UAVs) where sliding autonomy is supported by mixed-initiative planning and haptic feedback. In the proposed framework, we assume that an autonomous system can plan and execute robotic tasks while a human operator can provide interventions when necessary receiving a force feedback. The haptic feedback is associated with the sensation about how the system is diverging from the planned operations. We tested the system at work in virtual and real environments considering simple navigation tasks. We compared the performance of human operators with or without the assistance of the force feedback. The collected results support the hypothesis that the proposed approach enables effective and intuitive mixed-initiative control.

Aerial service vehicles for industrial inspection: task decomposition and plan execution

This work proposes a high-level control system designed for an Aerial Service Vehicle capable of performing complex tasks in close and physical interaction with the environment in an autonomous manner. We designed a hybrid control architecture which integrates task, path, motion planning/replanning, and execution monitoring. The high-level system relies on a continuous monitoring and planning cycle to suitably react to events, user interventions, and failures, communicating with the low level control layers. The system has been assessed on real-world and simulated scenarios representing an industrial environment.

A control architecture for multiple drones operated via multimodal interaction in search & rescue mission

An architecture suitable for the control of multiple unmanned aerial vehicles deployed in Search & Rescue missions is presented in this paper. In the proposed system, a single colocated human operator is able to coordinate the actions of a set of robots in order to retrieve relevant information of the environment. This work is framed in the context of the SHERPA project whose goal is to develop a mixed ground and aerial robotic platform to support search and rescue activities in alpine scenario. Differently from typical human-drone interaction settings, here the operator is not fully dedicated to the drones, but involved in search and rescue tasks, hence only able to provide sparse and incomplete instructions to the robots. In this work, the domain, the interaction framework and the executive system for the autonomous action execution are discussed. The overall system has been tested in a real world mission with two drones equipped with on-board cameras.

Velocity estimation of an UAV using visual and IMU data in a GPS-denied environment

This paper proposes two methods for UAV translational velocity estimation based on onboard sensing only. Spherical image measurements provided by a single onboard camera along with IMU data consist the main information feeding the estimators. The first algorithm consists of a nonlinear observer, designed using Lyapunov synthesis, while the second is based on the Unscented Kalman filtering technique. Differently with respect to existing approaches, the velocity is directly estimated from the onboard image without the need to fully estimate the vehicle 3D pose. The low computational requirement makes the proposed techniques suitable for applications where the execution time is of prominent importance even if no powerful hardware is available, as it is the case with UAV systems. Experimental results validate the algorithms, and this with the use of only four image features.

Attentional multimodal interface for multidrone search in the Alps

We present a multimodal attentional interface suitable for a human operator that monitors and controls the activities of a team of drones during search and rescue missions. We consider a scenario where the operator is a component of the rescue team, hence not fully dedicated to the robots, but only able to interact with them with sparse and incomplete commands. In this context, an adaptive interface is needed to support the user situation awareness and to enable an effective interaction with the drones. In this work, we propose a multimodal attention-based interface designed for this domain. This framework is to filter the information flow towards the operator selecting and adapting the communication mode according to the context and the human state. We illustrate the features of the adaptive system along with an initial assessment in a simulated scenario.

Implicit robot selection for human multi-robot interaction in Search and Rescue missions

We present a system suitable for human multi-robot interaction that supports the operator in the robot selection process. The proposed framework allows a human to issue commands to a robotic team without an explicit robot selection in so enabling a fluent interaction. This work is framed in the operative context of the SHERPA project [1], which proposes the deployment of a robotic platform for Search & Rescue in an alpine scenario and assumes the presence of a human rescuer that can orchestrate the robots operations with multimodal commands. In this context, implicit robot selection is mainly motivated by fast communication and the difficulties to distinguish different robots of similar shape in a hazardous environment and in adverse weather conditions. In the proposed approach, each robot of the team can evaluate the probability to be referred in an incomplete command, considering its actual capabilities along with geometrical and contextual information. We describe the overall system architecture focusing on the human intention recognition process. The proposed framework is trained and evaluated in a simulated case study.

Hybrid visual servoing for aerial grasping with hierarchical task-priority control

In this paper a hybrid visual servoing with a dynamic and hierarchical task-priority control framework is proposed for the control of an aerial vehicle endowed with a robot arm. The proposed task composition algorithm retains the main benefits of classical image-based and position-based control scheme, that can be suitably combined in a common hybridcontrol framework.Moreover, the under-actuation of the vehicle base has been systematically taken into account within a general formulation, while a dynamic smooth activation/deactivation mechanism is proposed to avoid discontinuity in the control action. Simulations have been proposed to demonstrate the effectiveness of the proposed approach.

Attentional supervision of human-robot collaborative plans

A human-robot interaction system should be capable of adapting the execution of cooperative plans with respect to complex human activities and interventions. In this paper, we present an integrated framework that exploits attentional supervision and contention scheduling to combine human-aware planning, plan execution, and natural human-robot interaction. Specifically, in the proposed approach, hierarchical cooperative plans are exploited as top-down attentional guidance for the robotic executive system, which can flexibly orchestrate the task activities while reacting to environmental stimuli and human behaviors. We describe the overall framework discussing some case studies in human-robot collaborative scenarios.