Hierarchical frameworks-a special class of directed frameworks with a layer-by-layer architecture-can be an effective mechanism to coordinate robot swarms. Their effectiveness was recently demonstrated by the mergeable nervous systems paradigm (Mathews et al., 2017), in which a robot swarm can switch dynamically between distributed and centralized control depending on the task, using self-organized hierarchical frameworks. New theoretical foundations are required to use this paradigm for formation control of large swarms. In particular, the systematic and mathematically analyzable organization and reorganization of hierarchical frameworks in a robot swarm is still an open problem. Although methods for framework construction and formation maintenance via rigidity theory exist in the literature, they do not address cases of hierarchy in a robot swarm. In this article, we extend bearing rigidity to directed topologies and extend the Henneberg constructions to generate self-organized hierarchical frameworks with bearing rigidity. We investigate three-key self-reconfiguration problems: 1) framework merging; 2) robot departure; and 3) framework splitting. We also derive the mathematical conditions of these problems and then develop algorithms that preserve rigidity and hierarchy using only local information. Our approach can be used for formation control generally, as in principle it can be coupled with any control law that makes use of bearing rigidity. To demonstrate and validate our proposed hierarchical frameworks and methods, we apply them to four scenarios of reactive formation control using an example control law.

As with all actuated mechanical systems, the development of a control scheme for robotic systems must take actuator saturation into account. Furthermore, in order to properly control a robotic arm, other types of mechanical limitations must also be considered (e.g. limited operating range of the joint, speed limitations). In this paper, we propose a saturation-aware trajectory-based Explicit Reference Governor, a lightweight constrained control scheme with no online optimization. Additionally, we tested the effectiveness of the proposed control architecture on a 7 degree-of-freedom KUKA LBR IIWA14 R820. Both the performance and the computational time of our proposed solution were tested against other constrained control solutions available in the literature.

This article presents a novel information-based mission planner for a drone tasked to monitor a spatially distributed dynamical phenomenon. For the sake of simplicity, the area to be monitored is discretized. The insight behind the proposed approach is that, due to the spatiotemporal dependencies of the observed phenomenon, one does not need to collect data on the entire area, which is one of the main limiting factors in unmanned aerial vehicle (UAV) applications due to their limited autonomy. In fact, unmeasured states can be estimated using an estimator, such as a Kalman filter. In this context, the planning problem becomes the one of generating a flight plan that maximizes the quality of the state estimation while satisfying the flight constraints (e.g., flight time). The first result of this article is the formulation of this problem as a special orienteering problem where the cost function is a measure of the quality of the estimation. This results in a mixed-integer semidefinite formulation, which can be optimally solved for small instances of the problem. For larger instances, a heuristic is proposed, which provides suboptimal results. Simulations numerically demonstrate the capabilities and efficiency of the proposed path-planning strategy. We believe that this approach has the potential to increase dramatically the area that a drone can monitor, thus increasing the number of applications where monitoring with drones can become economically convenient.Note to Practitioners—This article was motivated by the problem of performing large-scale field monitoring activities using unmanned aerial vehicle (UAV), which at the moment is very time-consuming and limits the definitive adoption of UAVs for this kind of activities. This problem is caused by the limited autonomy of commercial UAVs and the lack of systematic ways to plan missions so as to maximize the amount of information collected. This work starts from the observation that, in many applications, the phenomena that one wants to observe have dynamics and statistical properties. Accordingly, data that are not directly measured can be estimated with a characterizable observation error. In this article, we develop the theoretical foundations for an information-based path planning and define the problem of designing the optimal mission as an optimization problem based on the knowledge of the monitored phenomenon. The presented results have the potential to dramatically improve the effectiveness of drones for monitoring applications.

Due to the lack of systematic empirical analyses and comparisons of ideas and methods, a clearly established state of the art is still missing in the optimization-based design of robot swarms. In this article, we propose an experimental protocol for the comparison of fully automatic design methods. This protocol is characterized by two notable elements: 1) a way to define benchmarks for the evaluation and comparison of design methods and 2) a sampling strategy that minimizes the variance when estimating their expected performance. To define generally applicable benchmarks, we introduce the notion of mission generator: a tool to generate missions that mimic those a design method will eventually have to solve. To minimize the variance of the performance estimation, we show that, under some common assumptions, one should adopt the sampling strategy that maximizes the number of missions considered—a formal proof is provided as the supplementary material. We illustrate the experimental protocol by comparing the performance of two offline fully automatic design methods that were presented in previous publications.

In this work, we propose a global planning strategy specifically designed for precision agriculture settings, where field activities may have different requirements ranging from a full orchard inspection to sparse targeted per-plant interventions. This global planning strategy is formulated as a novel Multi-Platform Steiner Traveling Salesman Problem (MP-STSP) where, in order to guarantee the exploitation of multiple moving platforms and the minimization of the overall operational time, the proposed formulation explicitly takes into account the time required to perform each task. By doing so, the computed itineraries attempt to balance the workload among the deployed platforms. Comparative simulations, inspired by the needs of the EU H2020 Project PANTHEON1, are provided to numerically demonstrate the effectiveness of the proposed planning strategy for an orchard precision agriculture setting.

Abstract In this work, we face the problem of estimating the relative position and orientation of a camera and an object, when they are both equipped with inertial measurement units (IMUs), and the object exhibits a set of n landmark points with known coordinates (the so-called Pose estimation or P n P Problem). We present two algorithms that, fusing the information provided by the camera and the IMUs, solve the P n P problem with good accuracy. These algorithms only use the measurements given by IMUs’ inclinometers, as the magnetometers usually give inaccurate estimates of the Earth magnetic vector. The effectiveness of the proposed methods is assessed by numerical simulations and experimental tests. The results of the tests are compared with the most recent methods proposed in the literature.

This paper describes recent developments of a robotic hummingbird project, aimed at achieving precision stationary hovering. To this end, the early version of our flapping mechanism is modified which, besides being more efficient, reduces significantly the asymmetry of the wing trajectory of the previous version. A cascade control strategy is used to compensate for the residual parasitic torques and the misalignment of the lift vector and the autopilot.

In this work we consider a swarm of agents shaped as bars with a certain orientation in the state space. Members of the swarm have to reach an aggregate state, while guaranteeing the collision avoidance and possibly achieving an angular consensus. By relying on a segment-to-segment distance definition, we propose a control law, which guides the agents towards this goal. A theoretical analysis of the proposed control scheme along with simulations and experimental results is provided. The proposed framework can be used to model several application scenarios ranging from collaborative transportation to precision farming, where each agent may represent either a large robot or a group of robots intent to carry bar-like shaped loads. Representative examples include: a fleet of robot-teams performing a collaborative object transportation task in an automated logistic setting, or a fleet of autonomous tractors each carrying a large atomizer to spray chemical products for pest and disease control in a precision farming setting.

Robotic manipulators that are intended to interact with humans in their operating region are systems that need formal safety guarantees. Current solutions cannot handle both input and state constraints, have difficulties handling nonconvex constraints, or are computationally too expensive. To tackle these drawbacks, we analyzed a constrained control strategy, the Explicit Reference Governor (ERG), which can address both input and state constraints, and does not require any online optimization, thus making it computationally inexpensive. This paper presents the theory of the ERG for a general robotic manipulator and shows simulations for a specific 2DOF planar robotic manipulator. The proposed control scheme is able to steer the robot arm to the desired end-effector position, or an admissible approximation, in the presence of limited joint ranges, actuator saturations, and static obstacles. As a result, the ERG is a promising tool for the control of robotic manipulators subject to constraints.

In this paper the problem of estimating the relative orientation and position between a camera and an object is investigated. It is assumed that both the camera and the object are provided with an Inertial Measurement Unit (IMU) capable of measuring their attitude with respect to the gravity and the earth magnetic vectors. Furthermore, the object is assumed to contain a feature of n points, the position of which is known in the object coordinate frame. An algorithm is proposed, which uses the image provided by the camera and the information provided by the IMUs, to solve the PnP problem, i.e., to estimate the relative pose of the object in the camera reference frame. Two special cases will be studied. The first is the case where all the attitude information given by the IMU is used. In the second case only the measurements provided by inclinometers are used, neglecting those coming from the magnetometers, because they are usually quite noisy. The effectiveness of the proposed algorithms is tested either by numerical simulations and by experimental tests with cameras.