In this paper, the problem of impedance control of dual-arm cooperative manipulators is studied. A general impedance control scheme is adopted, which encompasses a centralized impedance control strategy, aimed at conferring a compliant behavior at the object level, and a decentralized impedance control, enforced at the end-effector level, aimed at avoiding large internal loading of the object. Remarkably, the mechanical impedance behavior is defined in terms of geometrically consistent stiffness. The overall control scheme is based on a twoloop arrangement, where a simple proportional integral derivative inner motion loop is adopted for each manipulator, while an outer loop, using force and moment measurements at the robots wrists, is aimed at imposing the desired impedance behaviors. The developed control scheme is experimentally tested on a dual-arm setup composed of two 6-DOF industrial manipulators carrying a common object. The experimental investigation concerns the four different controller configurations that can be achieved by activating/deactivating the single impedance controllers.

In this paper, a distributed controller–observer schema for tracking control of the centroid and of the relative formation of a multi-robot system with first-order dynamics is presented. Each robot of the team uses a distributed observer to estimate the overall system state and a motion control strategy for tracking control of time-varying centroid and formation. Proof of the overall convergence of the controller–observer schema for different kinds of connection topologies, as well as for the cases of unsaturated and saturated control inputs is presented. In particular, the solution is proven to work in the case of strongly connected non-switching topologies and in the case of balanced strongly connected switching topologies. In order to complete the work, the approach is validated by experimental tests with a team of five wheeled mobile robots

Haptic feedback is essential for humans to successfully perform complex and delicate manipulation tasks. A recent rise in tactile sensors has enabled robots to leverage the sense of touch and expand their capability drastically. However, many tasks still need human intervention/guidance. For this reason, we present a teleoperation framework designed to provide haptic feedback to human operators based on the data from camera-based tactile sensors mounted on the robot gripper. Partial autonomy is introduced to prevent slippage of grasped objects during task execution. Notably, we rely exclusively on low-cost off-the-shelf hardware to realize an affordable solution. We demonstrate the versatility of the framework on nine different objects ranging from rigid to soft and fragile ones, using three different operators on real hardware.

This paper proposes a multiple robot control algorithm\ud to approach the problem of patrolling an open or closed\ud line. The algorithm is fully decentralized, i.e., no communication\ud occurs between robots or with a central station. Robots behave\ud according only to their sensing and computing capabilities to\ud ensure high scalability and robustness towards robots’ fault.\ud The patrolling algorithm is designed in the framework of\ud behavioral control and it is based on the concept of Action: an\ud higher level of abstraction with respect to the behaviors. Each\ud Action is obtained by combining more elementary behaviors in\ud the Null-Space-Behavioral framework. A Finite-State-Automata\ud is designed as supervisor in charge of selecting the appropriate\ud action. The approach has been validated in simulation as well\ud as experimentally with a patrol of 3 Pioneer robots available\ud at the Distributed Intelligence Laboratory of the University of\ud Tennesse

In this paper, we present a distributed fault {detection} and isolation (FDI) strategy for a team of networked robots that builds on a distributed controller-observer schema. Remarkably different from other works in literature, the proposed FDI approach makes each robot of the team able to detect and isolate faults occurring on other robots, even if they are not direct neighbors. By means of a local observer, each robot can estimate the overall state of the team and it can use such an estimate to compute its local control input to achieve global tasks. The same information used by the local observers is also used to compute residual vectors, whose aim is to allow the detection and the isolation of actuator faults occurring on any robot of the team. Adaptive thresholds are derived based on the dynamics of the residual vectors by considering the presence of nonzero initial observer estimation errors, and noise terms affecting state measurement and model dynamics. The approach is validated via both numerical simulations and experiments involving four Khepera III mobile robots

This paper deals with the networked control of loosely or tightly connected cooperative manipulators in charge of achieving a cooperative task that is specified by means of proper task-oriented variables depending on the full state of the system. Since the full state is not known to robots, a two-layer architecture is designed. At the first level, each arm controller runs a distributed observer that estimates the system state. At the second level, this estimation is adopted to compute the local control input as in the case that a central unit is available. In addition, since the dynamic parameters of the arms might not be perfectly known, the local control law is made adaptive in order to counteract this uncertainty. The designed solution is suitable not only for pure motion coordination tasks but can also be exploited in those cases where a closed kinematic chain is generated by multirobot object manipulation. In this situation, the objective is to both move the object and limit the internal stresses on it that, however, cannot be locally computed. To overcome this issue, the wrench exerted by each robot on the object is decomposed in an external component (contributing to the motion of the object) and in an internal component that is locally estimated and, then, regulated. The approach was validated by simulation with 6-DOF serial chain manipulators mounted on a mobile platform and performing cooperative tasks

Cloth manipulation is a challenging task that, despite its importance, has received relatively little attention compared to rigid object manipulation. In this paper, we provide three benchmarks for evaluation and comparison of different approaches towards three basic tasks in cloth manipulation: spreading a tablecloth over a table, folding a towel, and dressing. The tasks can be executed on any bimanual robotic platform and the objects involved in the tasks are standardized and easy to acquire. We provide several complexity levels for each task, and describe the quality measures to evaluate task execution. Furthermore, we provide baseline solutions for all the tasks and evaluate them according to the proposed metrics.

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