This paper addresses the Bearing-Only Tracking problem of an underwater acoustic source using multiple Autonomous Underwater Vehicles. The vehicles, equipped with Passive Acoustic Monitoring sensors, must communicate to exchange their local target Direction of Arrival estimates through acoustic communication systems, which are typically characterized by low bandwidth and high latencies. The different availability of local measurements compared to those coming from the other vehicles in the team can create situations where one vehicle is forced to generate an estimate based on measurements that are heavily unbalanced toward one source. This negatively impacts the numerical balance of the regressor matrix used for the estimation, potentially leading to biased results. This paper proposes a weighting method to mitigate this effect, validating it through numerical simulations.

This paper introduces a control architecture that enables a robotic system to ensure the safety of human operators entering its workspace. The proposed method utilizes an appropriate metric to measure safety levels and adjusts the robot’s motion to maintain this metric above a minimum threshold. To guarantee safety, the robot scales down and deviates from its intended path. For redundant robots, internal motion is exploited to enhance safety levels further. The approach is incorporated into a Hierarchical Quadratic Programming control framework, allowing the robot to address other control objectives simultaneously, such as handling joint limits. Experimental results with a dual-arm mobile robot developed as part of the EU-funded CANOPIES project demonstrate the effectiveness of the proposed method.Note to Practitioners—This paper was motivated by the problem of ensuring human safety in unstructured environments shared with human operators. We propose a control architecture that allows complex dual-arm robotic systems to operate effectively in such scenarios. The devised architecture gives the robot the capability to slow down a trajectory to follow as well as to deviate from a nominal path to keep a human operator safe. We tested the devised approach in a precision farming setting; however, it can be adopted in any human-robot interaction scenario.

Bearing Only Tracking of an underwater moving acoustic source employing multiple moving sensors is addressed in this paper. The vehicles need to communicate to exchange their local measurements coming from Passive Acoustic Monitoring sensors with an inevitable communication latency and packet loss, which has to be to be properly addressed in the algorithm. Additionally, The limited velocity of the sound in water causes the sensors to receive asynchronous data caused by the sound propagation delay. A distributed, iterative, optimization algorithm taking into account all these aspects is proposed and numerically verified on realistic simulations to validate the proposed approach.

This paper introduces a shared control framework designed specifically for agricultural mobile manipulators engaged in harvesting operations. The shared control strategy allows for achieving such operations by dynamically exchanging the control between the robotic system and a human operator depending on the uncertainty in the environment perception. For this purpose, the robot’s behavior is dynamically adapted to switch between two control modes with a different level of autonomy of the robot. The level of autonomy is encoded in two different admittance behaviors which are included in a first-order Hierarchical Quadratic Programming (HQP) control framework, that allows the robot to simultaneously address other control objectives at the same time. Experimental results with a dual-arm mobile robot, developed as part of the EU-funded CANOPIES project, demonstrate the effectiveness of the proposed method in real conditions.

Redundancy in robot structures allows the implementation of control algorithms in which it is possible to add secondary control objectives. Those are typically functions to be minimized/maximized and projected onto the null-space of the primary control objectives. As an example, typical metrics to maximize are the robot manipulability or the distance from its mechanical joint limits. Usually, designer's heuristics is used to decide which function eventually to optimize. This paper shows that heuristics may lead to counter-intuitive results such as, for example, reducing the dexterous workspace with respect to, e.g., avoiding optimization at all. A learning algorithm is proposed to allow the robot to dynamically select the function to optimize in a way to increase the overall dexterous workspace with respect to the static, heuristic choice. As a result, the robot will be able to increase its dexterous workspace by selecting the proper lower-priority task via the use of a neural network trained during a proper supervised learning process. A 3-link planar manipulator is used as numerical case study.

The requirements of modern production systems together with more advanced robotic technologies have fostered the integration of teams comprising humans and autonomous robots. While this integration has the potential to provide various benefits, it also raises questions about how to effectively manage these teams, taking into account the different characteristics of the agents involved. This paper presents a framework for task allocation in a human multi-robot collaborative scenario. The proposed solution combines an optimal offline allocation with an online reallocation strategy which accounts for inaccuracies of the offline plan and/or unforeseen events, human subjective preferences and cost of task switching. Experiments with two manipulators cooperating with a human operator in a box filling task are presented.

Task-priority inverse kinematics is a popular motion control algorithm which efficiently handles redundancy in robot manipulators. It has been recently extended in order to handle also set-based control objectives or inequality constraints. As any local motion planner it is prone to the occurrence of local minima. This work further extends set-based inverse kinematics by adding a motion planner in order to avoid such occurrence. Motion planners are usually computationally heavy especially in their eventual implementation with a task-priority architecture. To reduce this issue, the planner is implemented as a sampling-based algorithm which works in the reduced-dimensionality of the robot workspace applying Cartesian constraints only. The output trajectory is then checked against the inverse kinematics algorithm exploiting the redundancy and verifying the fulfillment of the joint-based task constraints. During the motion, inverse kinematics is then used also in real-time to ensure a reactive behavior to address, e.g., mismatch between the a-priori information and real-time perception acquisition. Also, the motion planner runs in background to adapt to changes in the environment or to accommodate incremental mapping. Comparison with alternative approaches are investigated and discussed. The most promising method is validated first in hundreds of numerical simulations to provide a solid statistical analysis and then experimentally with a Kinova Jaco2 7 DOFs manipulator equipped with an RGB-D sensor. Note to Practitioners—In this work we propose a motion planning algorithm that allows to effectively deploy robot manipulators in partially unstructured environments. It is structured in two layers: a Cartesian space global planner guarantees the feasibility of the trajectory with respect to potential obstacles in the environment, while a reactive local planner checks the feasibility at joint level. This architecture allows to define all the safety constraints both at Cartesian and joint space needed to operate in unstructured environments, while keeping the computation time lower with respect to standard approaches that define all the constraints in an offline global planner. The reduced computation time allows to effectively perform real-time replanning operations when needed, making the robotic system capable of adapting to a dynamic environment and suitable for sharing its workspace with human operators.

The paper presents a shared control architecture for non-holonomic mobile robots commanded through a motor imagery based Brain-Computer Interface (BCI). The overall system is aimed at assisting people to teleoperate a mobile robot in a simulated house-like scenario by resorting to two motor imagery commands. The developed architecture is structured in such a way that the user can drive the mobile robot while safety tasks, e.g. obstacle avoidance, are autonomously achieved, leaving complete autonomy to the mobile robot to let the latter adjust its configuration, e.g. aligning itself with a narrow passage. The overall architecture has been realized by developing control modules with the ROS environment, while the OpenVibe framework has been adopted to process the EEG signals. The effectiveness of the proposed architecture has been validated through experiments where a healthy user, wearing a Unicorn g.tec BCI, performs an assisted teleoperation task through motor imagery sessions with a Turtlebot robot.

The paper presents a shared control architecture for robotic systems commanded through a motor imagery based Brain-Computer Interface (BCI). The overall system is aimed at assisting people to perform teleoperated manipulation tasks, and it is structured so as to leave different levels of autonomy to the user depending on the actual stage of the task execution. The low-level part of the shared control architecture is also in charge of taking into account safety and operational tasks, such as to avoid collisions or to manage robot joint limits. The overall architecture has been realized by integrating control and perception software modules developed within the ROS environment, with the OpenVibe framework used to operate the BCI device. The effectiveness of the proposed architecture has been validated through experiments where a healthy user, wearing a Unicorn g.tec BCI, performs an assisted task through motor imagery sessions, with a 7 Degrees of Freedoms Kinova Jaco2 robotic arm.

In this paper an experimental study of set-based task-priority kinematic control for a dual-arm mobile robot is developed. The control strategy for the coordination of the two manipulators and the mobile base relies on the definition of a set of elementary tasks to be properly handled depending on their functional role. In particular, the tasks have been grouped into three categories: safety, operational and optimization tasks. The effectiveness of the resulting task hierarchy has been validated through experiments on a Kinova Movo robot, in a domestic use case scenario.

The paper presents a multiple task-priority inverse kinematics algorithm for a dual-arm aerial manipulator. Both tasks defined as equality constraints and inequality constraints are handled by means of a singularity robust method based on the Null-Space based Behavioral control. The proposed schema is constituted by the inverse kinematics control, that receives the desired behavior of the system and outputs the reference values for the motion variables, i.e. the UAV pose and the arm joints position, and a motion control, that computes the vehicle thrusts and the joint torques. The method has been experimentally validated on a system composed by an underactuated aerial hexarotor vehicle equipped with two lightweight 4-DOF manipulators, involved in operations requiring the coordination of the two arms and the vehicle.

Set-Based Multi-Task Priority is a recent framework to handle inverse kinematics for redundant structures. Both equality tasks, i.e., control objectives to be driven to a desired value, and set-bases tasks, i.e., control objectives to be satisfied with a set/range of values can be addressed in a rigorous manner within a priority framework. In addition, optimization tasks, driven by the gradient of a proper function, may be considered as well, usually as lower priority tasks. In this paper the proper design of the tasks, their priority and the use of a Set-Based Multi-Task Priority framework is proposed in order to handle several constraints simultaneously in real-time. It is shown that safety related tasks such as, e.g., joint limits or kinematic singularity, may be properly handled by consider them both at an higher priority as set-based task and at a lower within a proper optimization functional. Experimental results on a 7DOF Jaco2arm with and without the proposed approach show the effectiveness of the proposed method.

Within the European project DexROV the topic of underwater intervention is addressed. In particular, a remote control room is connected through a satellite communication link to surface vessel, which is in turn connected to an UVMS (Underwater Vehicle-Manipulator System) with an umbilical cable. The operator may interact with the system using a joystick or exoskeleton. Since a direct teleoperation is not feasible, a cognitive engine is in charge of handling communication latency or interruptions caused by the satellite link, and the UVMS should have sufficient autonomy in dealing with low level constraints or secondary objectives. To this purpose, a task-priority-based inverse kinematics algorithm has been developed in order to allow the operator to control only the end effector, while the algorithm is in charge of handling both operative and joint-space constraints. This paper describes some preliminary experimental results achieved during the DexROV campaign of July 2017 in Marseilles (France), where most of the components have been successfully integrated and the inverse kinematics nicely run.

In this paper we present a framework that allows the motion control of a robotic arm automatically handling different kinds of safety-related tasks. The developed controller is based on a Task-Priority Inverse Kinematics algorithm that allows the manipulator's motion while respecting constraints defined either in the joint or in the operational space in the form of equality-based or set-based tasks. This gives the possibility to define, among the others, tasks as joint-limits, obstacle avoidance or limiting the workspace in the operational space. Additionally, an algorithm for the real-time computation of the minimum distance between the manipulator and other objects in the environment using depth measurements has been implemented, effectively allowing obstacle avoidance tasks. Experiments with a Jaco2manipulator, operating in an environment where an RGB-D sensor is used for the obstacles detection, show the effectiveness of the developed system.

In this paper we present an architecture for the operation of an assistive robot finally aimed at allowing users with severe motion disabilities to perform manipulation tasks that may help in daily-life operations. The robotic system, based on a lightweight robot manipulator, receives high level commands from the user through a Brain-Computer Interface based on P300 paradigm. The motion of the manipulator is controlled relying on a closed loop inverse kinematic algorithm that simultaneously manages multiple set-based and equality-based tasks. The software architecture is developed relying on widely used frameworks to operate BCIs and robots (namely, BCI2000 for the operation of the BCI and ROS for the control of the manipulator) integrating control, perception and communication modules developed for the application at hand. Preliminary experiments have been conducted to show the potentialities of the developed architecture.