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We present a decentralized connectivity-maintenance control framework for a heterogeneous human–robot team. The algorithm is able to manage a team composed of an arbitrary number of mobile robots (drones and ground robots in our case) and humans, for collaboratively achieving exploration and patrolling tasks. Differently from other works on the subject, here the human user physically becomes part of the team, moving in the same environment of the robots and receiving information about the team connectivity through wearable haptics or audio feedback. Although human explores the environment, robots move so as to keep the team connected via a connectivity-maintenance algorithm; at the same time, each robot can also be assigned with a specific target to visit. We carried out three human subject experiments, both in virtual and real environments. Results show that the proposed approach is effective in a wide range of scenarios. Moreover, providing either haptic or audio feedback for conveying information about the team connectivity significantly improves the performance of the considered tasks, although users significantly preferred receiving haptic stimuli w.r.t. the audio ones. Note to Practitioners—Exploration, patrolling, and search-and-rescue are highly dynamic and unstructured scenarios. When considering the operative conditions of such environments, the benefits of multirobot systems are evident. Most tasks can be carried out faster and more robustly by a team of robots with respect to a single unit. There are also situations explicitly requiring the presence of a multirobot team, e.g., using one drone for surveillance of the ground team and one ground mobile robot for carrying supplies. Of course, if the operator(s) in charge of the operation could share the same environment of the robots (i.e., be together with the robots in the field), they would be provided with a level of situational awareness that no teleoperation technology can match as of today. This work presents a framework for controlling heterogeneous teams composed of one human operator and an arbitrary number of aerial and ground mobile robots. The operator moves together with the robotic team and, at the same time, he or she receives meaningful information about the status of the formation. The algorithm only uses the relative position of the drones and humans with respect to each other, and all computations are designed in a decentralized fashion. Decentralization avoids relying on any absolute positioning system (e.g., GPS) or centralized command centers. These features make the proposed framework ready for deployment in different high-impact applications, such as in surveillance, search-and-rescue, and disaster response scenarios. [ABSTRACT FROM AUTHOR]

Research on robotic manipulation of fragile, compliant objects, such as food items, is gaining traction due to its game-changing potential within the food production and retailing sectors, currently characterized by manually intensive and highly repetitive tasks. Food products exhibit high levels of frailness, biological variation, and complex 3-D shapes and textures. For these reasons, introducing greater levels of robotic automation in the food and agricultural sectors remains an important challenge. This article addresses this challenge by developing a human-centered, haptic-based, learning from demonstration (LfD) policy that enables pretrained autonomous grasping of food items using an anthropomorphic robotic system. The policy combines data from teleoperation and direct human manipulation of objects, embodying human intent and interaction areas of significance. We evaluated the proposed solution against a recent state-of-the-art LfD policy as well as against two standard impedance controller techniques. Results show that the proposed policy performs significantly better than the other considered techniques, leading to high grasping success rates while guaranteeing the integrity of the food at hand. [ABSTRACT FROM AUTHOR]

This article proposes a shared-control teleoperation architecture for robot manipulators transporting an object on a tray. Differently from many existing studies about remotely operated robots with firm grasping capabilities, we consider the case in which, in principle, the object can break its contact with the robot end-effector. The proposed shared-control approach automatically regulates the remote robot motion commanded by the user and the end-effector orientation to prevent the object from sliding over the tray. Furthermore, the human operator is provided with haptic cues informing about the discrepancy between the commanded and executed robot motion, which assist the operator throughout the task execution. We carried out trajectory tracking experiments employing an autonomous 7-degree-of-freedom (DoF) manipulator and compared the results obtained using the proposed approach with two different control schemes (i.e., constant tray orientation and no motion adjustment). We also carried out a human-subjects study involving 18 participants in which a 3-DoF haptic device was used to teleoperate the robot linear motion and display haptic cues to the operator. In all experiments, the results clearly show that our control approach outperforms the other solutions in terms of sliding prevention, robustness, commands tracking, and user’s preference. [ABSTRACT FROM AUTHOR]

This article presents the design of a decentralized connectivity-maintenance algorithm for the teleoperation of a team of multiple UAVs, together with an extensive human subject evaluation in virtual and real environments. The proposed connectivity-maintenance algorithm enhances earlier works by improving their applicability, safety, effectiveness, and ease of use, by including: 1) an airflow-avoidance behavior that avoids stack downwash phenomena in rotor-based aerial robots; 2) a consensus-based action for enabling fast displacements with minimal topology changes by having all follower robots moving at the leader’s velocity; 3) an automatic decrease of the minimum degree of connectivity, enabling an intuitive and dynamic expansion/compression of the formation; and 4) an automatic detection and resolution of deadlock configurations, i.e., when the robot leader cannot move due to counterbalancing connectivity- and external-related inputs. We also devised and evaluated different interfaces for teleoperating the team as well as different ways of receiving information about the connectivity force acting on the leader. The results of two human subject experiments show that the proposed algorithm is effective in various situations. Moreover, using haptic feedback to provide information about the team connectivity outperforms providing both no feedback at all and sensory substitution via visual feedback. Note to Practitioners—The control of one drone is usually performed with a remote controller (similar to a joypad) that uses radio-wave signals. When controlling more than one drone, even a simple task, such as moving the whole team around, becomes very challenging. Developing an easy, yet efficient way to impart commands to a formation of drones is necessary to achieve any complex task. This article proposes a framework to control a fleet of drones (quadrotors) in an intuitive way while receiving meaningful and effective information on the state of the formation. The proposed technique does not rely on any absolute positioning system (e.g., GPS) or centralized command center. Instead, it only uses the relative position of the drones with respect to each other, and all computations are designed in a decentralized fashion. These features make the proposed framework ready for deployment in different high-impact applications, such as in surveillance, search-and-rescue, and disaster response scenarios. [ABSTRACT FROM AUTHOR]

Although robotic telemanipulation has always been a key technology for the nuclear industry, little advancement has been seen over the last decades. Despite complex remote handling requirements, simple mechanically linked master–slave manipulators still dominate the field. Nonetheless, there is a pressing need for more effective robotic solutions able to significantly speed up the decommissioning of legacy radioactive waste. This paper describes a novel haptic shared-control approach for assisting a human operator in the sort and segregation of different objects in a cluttered and unknown environment. A three-dimensional scan of the scene is used to generate a set of potential grasp candidates on the objects at hand. These grasp candidates are then used to generate guiding haptic cues, which assist the operator in approaching and grasping the objects. The haptic feedback is designed to be smooth and continuous as the user switches from a grasp candidate to the next one, or from one object to another one, avoiding any discontinuity or abrupt changes. To validate our approach, we carried out two human–subject studies, enrolling 15 participants. We registered an average improvement of 20.8%, 20.1%, and 32.5% in terms of completion time, linear trajectory, and perceived effectiveness, respectively, between the proposed approach and standard teleoperation. [ABSTRACT FROM AUTHOR]

Haptic shared control enables a human operator and an autonomous controller to share the control of a robotic system using haptic active constraints. It has been used in robotic teleoperation for different purposes, such as navigating along paths minimizing the torques requested to the manipulator or avoiding possibly dangerous areas of the workspace. However, few works have focused on using these ideas to account for the user's comfort. In this article, we present an innovative haptic-enabled shared control approach aimed at minimizing the user's workload during a teleoperated manipulation task. Using an inverse kinematic model of the human arm and the rapid upper limb assessment (RULA) metric, the proposed approach estimates the current user's comfort online. From this measure and an a priori knowledge of the task, we then generate dynamic active constraints guiding the users towards a successful completion of the task, along directions that improve their posture and increase their comfort. Studies with human subjects show the effectiveness of the proposed approach, yielding a 30% perceived reduction of the workload with respect to using standard guided human-in-the-loop teleoperation. [ABSTRACT FROM AUTHOR]

The manipulation of large objects by robotic systems is a challenge for applications in the construction industry, industrial decommissioning, and urban search and rescue (USAR). These are associated with dangerous environments and thus motivate devising robotic solutions to replace human presence. Furthermore, they often require manipulation of long objects, such as pipes, bars, beams, and metal frameworks, with limited access to their center of mass (CoM). [ABSTRACT FROM AUTHOR]

This paper presents an adaptive trajectory tracking control strategy for quadrotor micro aerial vehicles (MAVs). The proposed approach, while maintaining the common assumption of an orientation dynamics faster than the translational one, removes the assumption of absence of external disturbances and of geometric center coincident with the Center of Mass (CoM). In particular, the trajectory tracking control law is made adaptive with respect to the presence of external forces and moments (e.g., due to wind) and to the uncertainty of parameters of the dynamic model, such as the position of the CoM. A stability analysis is presented to analytically support the proposed controller, while numerical simulations are provided in order to validate its performance. [ABSTRACT FROM AUTHOR]