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Ensuring safe and continuous autonomous navigation in long-term mobile robot applications is still challenging. To ensure a reliable representation of the current environment without the need for periodic re-mapping, updating the map is recommended. However, in case of incorrect estimation of robot pose, updating the map can lead to errors that prevent the robot localisation and jeopardize map accuracy. In this paper, we propose a safe LIDAR-based occupancy grid map updating algorithm for dynamic environments taking into account the uncertainties in the estimation of the robot’s pose. The proposed approach allows robust long-term operations as it can recover the robot’s pose, even when it gets lost, to continue the map update process providing a coherent map. Moreover, the approach is robust also to temporary changes in the map due to the presence of dynamic obstacles such as humans and other robots. Results highlighting map quality, localisation performance, and pose recovery, both in simulation and experiments, are reported.

The rise of collaborative robotics has offered new opportunities for integrating automation into the factories, allowing robots and humans to work side-by-side. However, this close physical coexistence inevitably brings new constraints for ensuring safe human-robot cooperation. The current paramount challenge is integrating human safety constraints without compromising the robotic performance goals, which require minimization of the task execution time alongside ensuring its accomplishment. This letter proposes a novel robot trajectory planning algorithm to produce minimum-time yet safe motion plans along specified paths in shared workspaces with humans. To this end, a safety module was used to evaluate the safety of a time-optimal trajectory iteratively. A safe replanning module was developed to optimally adapt the generated trajectory online whenever the optimal plan violates dynamically provided safety limits. In order to preserve performance, a recovery trajectory planning algorithm was included such that the robot is allowed to restore higher speed motions as soon as the safety concern has been resolved. The proposed solution's effectiveness was evaluated both in simulations and real experiments with two robotic manipulators.

In recent years, robotics has been largely applied to improve the efficiency of logistic processes. Pallets cover a crucial role in the logistic flow, since they represent the main way to store and ship items. When put onto pallets, the items are wrapped with plastic films to protect them and prevent them from falling. Despite being the first and necessary operation for handling the stacked goods, unwrappingthe task of removing the plastic films wrapped around the goodshas not yet been satisfactorily automated. We propose the first robotic solution for autonomous unwrapping of generally shaped pallets, including both homogeneous and heterogeneous pallets. Force and torque measurements are exploited to retrieve information on the collisions between the end-effector and the wrapped items or the plastic film. Based on the contact information, we design a novel reactive planning strategy that makes the unwrapping task effective and robust on pallets with uncertain position or shape. We present the results of an extensive experimental campaign to validate the proposed method. Note to Practitioners—This work is motivated by the fact that unwrapping machines are not yet common on the market. The few commercial examples are usually bulky machines that lack the flexibility to adapt to different and irregularly shaped pallets. Thus, the crucial operation of removing the plastic film around palletized goods is still mainly performed by hand. Blade handling, ladders, and electrostatic shocks are sources of potential injury. We propose a flexible, autonomous unwrapping robot suitable for both cuboid and irregularly shaped pallets. The robot is composed of a robotic arm, a custom cutting end-effector, a vision module, and a suitable planning and control unit. The reduced dimensions allow it to be mounted on a mobile base. The robot has been successfully tested on different pallet configurations. However, extensive testing in real-world scenarios should be carried out to assess both reliability and time efficiency in more realistic working conditions. Moreover, real pallets can reach considerable heights. Thus, a prismatic joint should be integrated to address such cases. Finally, unwrapping in the presence of typical plastic straps and different types of film, e.g., the shrink one, is to be evaluated.

Legged robots are meant to autonomously navigate unstructured environments for applications like search and rescue, inspection, or maintenance. In autonomous navigation, a close relationship between locomotion and perception is crucial; the robot has to perceive the environment and detect any change in order to autonomously make decisions based on what it perceived. One main challenge in autonomous navigation for legged robots is locomotion over unstructured terrains. In particular, when the ground is slippery, common control techniques and state estimation algorithms may not be effective, because the ground is commonly assumed to be non-slippery. This paper addresses the problem of slip detection, a first fundamental step to implement appropriate control strategies and perform dynamic whole-body locomotion. We propose a slip detection approach, which is independent of the gait type and the estimation of the position and velocity of the robot in an inertial frame, that is usually prone to drift problems. To the best of our knowledge, this is the first approach of a quadruped robot slip detector that can detect more than one foot slippage at the same time, relying on the estimation of measurements expressed in a non-inertial frame. We validate the approach on the 90 kg Hydraulically actuated Quadruped robot (HyQ) from the Istituto Italiano di Tecnologia (IIT), and we compare it against a state-of-the-art slip detection algorithm.

Historically, robots first found application in factories and plants. Until recently, the most noticeable examples of robot systems directly sold to the consumer were limited to edutainment systems (e.g., NAO [1]), automated chore robots [26], and social telepresence platforms [27]. Initially, telepresence robots consisted of a mobile base with an interactive screen. Today, following a trend of anthropomorphization of technology, human-like upper bodies have begun to replace those simple screens (e.g., Pepper [2] and R1 [3]) and share the same social communication modalities of humans, e.g., body posture, gestures, gaze direction, and facial expressions. Unfortunately, social robots are mostly designed to speak and make gestures and have limited capabilities when it comes to physically interacting with people and their surrounding environments.

This letter presents a new approach to solve the time-optimal path tracking under limited joint range and bounds on velocity, acceleration, and jerk. To obtain smooth and continuous accelerations, with beneficial effects for the load and wear on the actuators but a limited impact on performance, we state the minimum-time path tracking problem with the jerk as the control input. The main contribution of this letter is a formulation that includes the jerk constraints in the optimization problem and that, even if the resulting non-linear programming problem is non-convex, allows to perform an efficient and reliable convex relaxation using McCormick envelopes. Simulations and experimental tests on two 7-DoF manipulators have been carried out to show the benefits of the proposed approach and to compare it to state-of-the-art techniques.

Recently, the development of soft robots is imposing a change of prospective in several aspects of design and control, moving the robotic field closer to the natural world. Soft robots, like many animals, are often built of continuously deformable elements, and are consequently characterized by a highly under-actuated input space. In this letter we prove that given a generic nonlinear task to be accomplished by a soft robot - as e.g., the positioning of its end effector in space - a linear actuation space with the size of the task itself is already sufficient to achieve the goal. We then introduce the dynamically consistent projector into synergistic space, which can be used to convert controllers designed in operational space, to work in the under-actuated case. This enables a direct translation of control strategies from classic to soft robotics. Leveraging on this result, we present the first dynamic feedback controller for trunk-like soft robots taking in account nonconstant deformations of the soft body. We present simulations showing that using this controller it is possible to track a prescribed dynamic evolution of the robot's tip with zero error at steady state, both in planar and 3-D case with gravity.

Recently, in the attempt to increase blind people autonomy and improve their quality of life, a lot of effort has been devoted to develop technological travel aids. These systems can surrogate spatial information about the environment and deliver it to end-users through sensory substitution (auditory, haptic). However, despite the promising research outcomes, these solutions have met scarce acceptance in real-world. Often, this is also due to the limited involvement of real end users in the conceptual and design phases. In this manuscript, we propose a novel indoor navigation system based on wearable haptic technologies. All the developmental phases were driven by continuous feedback from visually impaired persons. The proposed travel aid system consists of a RGB-D camera, a processing unit to compute visual information for obstacle avoidance, and a wearable device, which can provide normal and tangential force cues for guidance in an unknown indoor environment. Experiments with blindfolded subjects and visually impaired participants show that our system could be an effective support during indoor navigation, and a viable tool for training blind people to the usage of travel aids.

The diffusion of e-commerce has produced larger and larger volumes of varying items to be handled in warehouses, with the effect that the need for picking automation is increasing. Conventionally, automation has been achieved through a custom plant designed for large-scale production of items having well-established characteristics that are expected to change slowly and to only a small degree over time. However, today the challenge is to realize a solution that is flexible enough to handle goods with different shapes, sizes, and physical properties and that require different grasping modes. To solve this problem, we first analyzed how humans perform picking and then synthesized their behavior according to four main tactics. These were then used as guidelines for the design, planning, and control of WRAPP-up, a dual-arm robot composed of two anthropomorphic manipulators: a Pisa/IIT SoftHand and a velvet tray (Figure 1). The system has been validated and evaluated through extensive experimental tests.

This paper presents a robotics laboratory originated by the collaboration between the university and high school within the Italian school-work transition program. The educational objective of the proposed lab is twofold: 1) ease the transfer of robotic researchers’ expertise into useful means for the students’ learning; 2) teaching by practice the multidisciplinarity of robotics. We exploited the RoboCup Junior Race as a useful scenario to cover topics from 3D printing for fast prototyping to low-level and high-level controller design. An ad-hoc end-of-term student survey confirms the effectiveness of the approach. Finally, the paper includes some considerations on how general problems in the robotic and scientific community, such as gender issues and COVID-19 restrictions, can impact the educational robotics activities.

A standing challenge in current intralogistics is to reliably, effectively yet safely coordinate large-scale, heterogeneous multi-robot fleets without posing constraints on the infrastructure or unrealistic assumptions on robots. A centralized approach, proposed by some of the authors in prior work, allows to overcome these limitations with medium-scale fleets (i.e., tens of robots). With the aim of scaling to hundreds of robots, in this paper we explore a de-centralized variant of the same approach. The proposed framework maintains the key features of the original approach, namely, ensuring safety despite uncertainties on robot motions, and generality with respect to robot platforms, motion planners and controllers. We include considerations on liveness and solutions to prevent or recover from deadlocks in specific situations are reported and discussed. We validate the approach empirically with simulated, large, heterogeneous multi-robot fleets (up to 100 robots tested) operating both in benchmark and realistic environments., Funding Agencies:Ministry of Education, Universities and Research (MIUR)Semantic Robots KKS research, H2020-EU.2.1.1. - INDUSTRIAL LEADERSHIP - Leadership in enabling and industrial technologies - Information and Communication Technologies (ICT), Semantic Robots KSS, AutoHauler (Vinnova), CrossLab (Department of Excellence)

This paper proposes an online optimal perception-aware strategy meant to maximize the information collected along the trajectory via the available measurements while simultaneously minimizing the negative effects of actuation/process noise. Indeed, in several robotic applications, the actuation/process noise is far from negligible and its negative effects are particularly relevant especially with intermittent measurements (e.g. collected by a vision system with limited Field-Of-View). New metrics are proposed as combinations of the Constructability Gramian, for measuring the amount of information collected via the available sensors, and the Reachability Gramian, for measuring the degrading effects of actuation/process noise. Control inputs that optimize those cost functions are provided. To show the effectiveness of our method, we consider one case study involving a unicycle-like vehicle, subject to Gaussian measurement noise and Gaussian or Brownian actuation noise, that estimates its state using intermittent distances from known environmental markers.

Multirobot fleets play an important role in industrial logistics, surveillance, and exploration applications. A wide literature exists on the topic, both resorting to reactive (i.e. collision avoidance) and to deliberative (i.e. motion planning) techniques. In this work, null space-based inverse kinematics (NSB-IK) methods are applied to the problem of fleet management. Several NSB-IK approaches existing in the literature are reviewed, and compared with a reverse priority approach, which originated in manipulator control, and is here applied for the first time to the considered problem. All NSB-IK approaches are here described in a unified formalism, which allows (i) to encode the property of each controller into a set of seven main key features, (ii) to study possible new control laws with an opportune choice of these parameters. Furthermore, motivated by the envisioned application scenario, we tackle the problem of task-switching activation. Leveraging on the iCAT TPC technique Simetti and Casalino, 2016, in this article, we propose a method to obtain continuity in the control in face of activation or deactivation of tasks, and subtasks by defining suitable damped projection operators. The proposed approaches are evaluated formally, and via simulations. Performances with respect to standard methods are compared considering a specific case study for multivehicles management.

Despite the strong demand for solutions and the intense research effort, fully autonomous object manipulation is not yet solved. One of the main challenges is the large variety of conditions in which the objects should be handled. There is a need for solutions that integrate both advanced perception systems for object recognition and pose estimation and robust planning algorithms. In this work, we present a fully autonomous dual-arm picking platform for intralogistics. The system is the result of the integration of a dual-arm manipulation system with a perception system for object registration, 3D instance-aware mapping, and 6D pose estimation of the objects. Eventually, we present the results of experiments on objects and conditions relevant to the intralogistics domain to assess the performance of the system., https://youtu.be/aN2qjvAbw18 https://www.youtube.com/watch?v=uy8uh_NEmbw

This work takes inspiration from how human infants gradually acquire knowledge about how their body works by performing suitable exploratory actions. With the aim of replicating this important feature also in robotic systems, in this paper we propose a method that combines adaptive robot control, meant to determine both the kinematic parameters and the type of joint (prismatic or revolute), and trajectory optimization, meant to determine the persistent excitation motions required to acquire the maximum amount of knowledge about the robot itself. The ultimate aim is to enable robots to learn and become aware of their physical-mechanical structure. This would favour the application of powerful and predictable model-based control techniques even when only a minimal knowledge of the robot is available (i.e. the number of joints in our case). A preliminary validation is carried out through simulations on simple 1 DoF and 2 DoF robots, showing promising results and potential improvements to be addressed in the future., https://youtu.be/S-vGstFVvAI

Aerial robotic manipulation is an emergent trend that poses several challenges. To overcome some of these, the DLR cable-Suspended Aerial Manipulator (SAM) has been envisioned. SAM is composed of a fully actuated multi-rotor anchored to a main carrier through a cable and a KUKA LWR attached below the multi-rotor. This work presents a control method to allow SAM, which is a holonomically constrained system, to perform such interaction tasks using a hierarchical control framework. Within this framework, compliance control of the manipulator end-effector is considered to have the highest priority. The second priority is the control of the oscillations induced by, for example, the motion of the arm or physical contact with the environment. A third priority task is related to the internal motion of the manipulator. The proposed approach is validated through simulations and experiments.

Stochastic Model Predictive Control has proved to be an efficient method to plan trajectories in uncertain environments, e.g., for autonomous vehicles. Chance constraints ensure that the probability of collision is bounded by a predefined risk parameter. However, considering chance constraints in an optimization problem can be challenging and computationally demanding. In this paper, we present a grid-based Stochastic Model Predictive Control approach. This approach allows to determine a simple deterministic reformulation of the chance constraints and reduces the computational effort, while considering the stochastic nature of the environment. Within the proposed method, we first divide the environment into a grid and, for each predicted step, assign each cell a probability value, which represents the probability that this cell will be occupied by surrounding vehicles. Then, the probabilistic grid is transformed into a binary grid of admissible and inadmissible cells by applying a threshold, representing a risk parameter. Only cells with an occupancy probability lower than the threshold are admissible for the controlled vehicle. Given the admissible cells, a convex hull is generated, which can then be used for trajectory planning. Simulations of an autonomous driving highway scenario show the benefits of the proposed grid-based Stochastic Model Predictive Control method., Accepted to the 2020 IEEE International Conference on Intelligent Transportation Systems. The published version is available at https://doi.org/10.1109/ITSC45102.2020.9294388

Self driving vehicles promise to bring one of the greatest technological and social revolutions of the next decade for itstheir potential to drastically change human mobility and goods transportation, in particular regarding efficiency and safety. Autonomous racing provides very similar technological issues while allowing for more extreme conditions in a safe human environment. While the software stack driving the racing car consists of several modulessuch as sensor data processing, perception, mapping, localization, planning and control, in this paper we focus on the localization problem, which provides as output the estimated pose of the vehicle needed by the planning and control modules. When driving near the friction limits, localization accuracy is critical as small errors can induce large errors in control due to the nonlinearities of the vehicle&rsquo, s dynamic model. Moreover, fast and accurate pose estimation is a necessary tool to tackle corners and negotiate overtakings along the path that minimizes lap time. In this paper, we present a localization architecture for a racing car that does not rely on Global Navigation Satellite Systems (GNSS). It consists consistingof two multi-rate Extended Kalman Filters and an extension of the classicala state-of-the-art laser-based Monte Carlo localization approach that exploits some a priori knowledge of the environment and context. We first compare the proposed method with a solution based on a widely employed state-of-the-art implementation, outlining its strengths and limitations within our experimental scenario. The architecture is then tested both in simulation and experimentally on a full-scale autonomous electric racing car during an event of Roborace Season Alpha. The results show its robustness in avoiding the robot kidnapping problem typical of particle filters localization methods, while providing a smooth and high rate pose estimate. The pose error distribution depends on the car velocity, and spans on average from 0.1 m (at 60 km/h) to 1.48 m (at 200 km/h) laterally and from 1.9 m (at 100 km/h) to 4.92 m (at 200 km/h) longitudinally.

International audience; Cooperative manipulation is a basic skill in groups of humans, animals , and in many robotic applications. Besides being an interesting challenge, communication-less approaches have been applied to groups of robots in order to achieve higher scalability and simpler hardware and software design. We present a generic model and control law for robots cooperatively manipulating an object, for both ground and floating systems. The control method exploits a leader-follower scheme and is based only on implicit communication (i.e., the sensing of contact forces). The control objective mainly consists of steering the object manipulated by the swarm of robots to a desired position and orientation in a cooperative way. For a system with just one leader, we present analytical results on the equilibrium configurations and their stability that are then validated by numerical simulations. The role of object internal forces (induced by the robots through contact forces) is discussed in terms of convergence of the object position and orientation to the desired values. We also present a discussion on additional properties of the controlled system that were investigated using thorough numerical analysis, namely, the robustness of the system when the object is subject to external disturbances in non-ideal conditions , and how the number of leaders in the swarm can affect the aforementioned convergence and robustness.

Intruder tracking and herding problems are crucial in several applications. In this letter, a game theoretic coordination protocol for multimobile robot systems is proposed to tackle both problems simultaneously. Defender robots move according to computed Nash equilibria to herd the intruder into a safe area while preventing its access to one or more protected areas. The concept of a virtual barrier is presented to induce defenders to automatically and uniformly deploy along the barrier in order to drive intruder away from the protected areas and toward the safe one. Simulation results are reported to validate the proposed approach.

International audience; This paper considers the study of cooperative manipulation of a cable-suspended load with two generic aerial robots and without explicit communication. The role of the internal force for the asymptotic stability of the beam-position/beam-attitude equilibria is analyzed in depth. Using a nonlinear Lyapunov-based approach, we prove that if a non-zero internal force is chosen then asymptotic stabilization of any desired beam-pose configuration can be achieved with a decentralized and communication-less master-slave admittance controller. If, conversely, a zero internal force is chosen, as done in the majority of the state-of-the-art algorithms, the attitude of the beam is not controllable without communication. Non-zero internal force can be interpreted then as a fundamental factor that enables the use of cables as implicit communication means between the two vehicles in replacement of the explicit ones. Furthermore, the formal proof of the system output-strictly passivity with respect to an energy-like storage function and a certain input-output pair is given. This proves the stability and the robustness of the method even during motion. The theoretical findings are validated through numerical simulations.

In this work, we present WALK-MAN, a humanoid platform that has been developed to operate in realistic unstructured environment, and demonstrate new skills including powerful manipulation, robust balanced locomotion, high-strength capabilities, and physical sturdiness. To enable these capabilities, WALK-MAN design and actuation are based on the most recent advancements of series elastic actuator drives with unique performance features that differentiate the robot from previous state-of-the-art compliant actuated robots. Physical interaction performance is benefited by both active and passive adaptation, thanks to WALK-MAN actuation that combines customized high-performance modules with tuned torque/velocity curves and transmission elasticity for high-speed adaptation response and motion reactions to disturbances. WALK-MAN design also includes innovative design optimization features that consider the selection of kinematic structure and the placement of the actuators with the body structure to maximize the robot performance. Physical robustness is ensured with the integration of elastic transmission, proprioceptive sensing, and control. The WALK-MAN hardware was designed and built in 11 months, and the prototype of the robot was ready four months before DARPA Robotics Challenge (DRC) Finals. The motion generation of WALK-MAN is based on the unified motion-generation framework of whole-body locomotion and manipulation (termed loco-manipulation). WALK-MAN is able to execute simple loco-manipulation behaviors synthesized by combining different primitives defining the behavior of the center of gravity, the motion of the hands, legs, and head, the body attitude and posture, and the constrained body parts such as joint limits and contacts. The motion-generation framework including the specific motion modules and software architecture is discussed in detail. A rich perception system allows the robot to perceive and generate 3D representations of the environment as well as detect contacts and sense physical interaction force and moments. The operator station that pilots use to control the robot provides a rich pilot interface with different control modes and a number of teleoperated or semiautonomous command features. The capability of the robot and the performance of the individual motion control and perception modules were validated during the DRC in which the robot was able to demonstrate exceptional physical resilience and execute some of the tasks during the competition.

Interconnection of a large number of systems has become a reality from a technological point of view although intelligent connection among smart systems still presents a challenge in several application scenarios. More specifically, when many smart devices must accomplish an overall goal based on information exchanges with other devices, several factors render this task a real challenge, such as, knowing what specific information to exchange, with whom, how to go about it, and when this exchange will occur. Moreover, interconnected devices may require access to or the use of common resources, making the management of the overall system even more complex. The management of shared resources usually sets the goal of optimizing usage while guaranteeing achievement of the overall goals. Algorithms or protocols that grant the device a correct, safe and optimized access to resources play a fundamental role. In this chapter we will provide those algorithms that are fundamental in several different application scenarios that allow for the development of more complex algorithms in order to manage interconnected systems in the management of shared resources.

In this letter, a new approach is proposed to optimally plan the motion along a parametrized path for flexible joint robots, i.e., robots whose structure is purposefully provided with compliant elements. State-of-the-art methods efficiently solve the problem in case of torque-controlled rigid robots via a translation of the optimal control problem into a convex optimization problem. Recently, we showed that, for jerk-controlled rigid robots, the problem could be recast into a non-convex optimization problem. The non-convexity is given by bilinear constraints that can be efficiently handled through McCormick relaxations and spatial Branch-and-Bound techniques. In this letter, we show that, even in case of robots with flexible joints, the time-optimal trajectory planning problem can be recast into a non-convex problem in which the non-convexity is still given by bilinear constraints. We performed experimental tests on a planar 2R elastic manipulator to validate the benefits of the proposed approach. The scalability of the method for robots with multiple degrees of freedom is also discussed.

We report on the development of a planning and control framework for an electric full scale autonomous racing car. We describe the whole control system architecture including the mapping and localization methods employed. We also report on novel experimental results obtained within the Roborace Localization challenge, whose first edition took place in Zala Zone1 track in August 2019, related to an informed version of Adaptive Monte Carlo Localization developed for the racing scenario.

Autonomous vehicles are undergoing a rapid development thanks to advances in perception, planning and control methods and technologies achieved in the last two decades. Moreover, the lowering costs of sensors and computing platforms are attracting industrial entities, empowering the integration and development of innovative solutions for civilian use. Still, the development of autonomous racing cars has been confined mainly to laboratory studies and small to middle scale vehicles. This paper tackles the development of a planning and control framework for an electric full scale autonomous racing car, which is an absolute novelty in the literature, upon which we report our preliminary experiments and perspectives on future work. Our system leverages real time Nonlinear Model Predictive Control to track a pre-planned racing line. We describe the whole control system architecture including the mapping and localization methods employed.

In this paper we consider the controllability problem for a system consisting of a pair of Dubins vehicles moving in a 3D space (i.e.źpair of 3D-Dubins vehicles) while maintaining constant distance. Necessary and sufficient conditions for the existence of a limited control effort to steer the system between any two configurations are provided. The proposed controllability analysis and the developed motion planning algorithm are a step toward the solution of planning problems for example in case the robots are physically constrained to a payload to be deployed. Moreover, results obtained in this paper are relevant in order to solve formation control problems for multiple robots as aerial or underwater vehicles, which move in 3D spaces. Simulation results highlight the sufficiency of the obtained conditions showing that even from critical configurations an admissible control can be determined. The controllability analysis of two 3D Dubins vehicles constrained to maintain constant distance is proposed.Necessary and sufficient conditions for the existence of a limited control to steer the system between any two configurations are provided.The control laws that verify distance constraints are also furnished.The results are relevant for aerial or underwater vehicles that are e.g. physically constrained to a payload to be deployed.

Control of two-wheeled humanoid robots poses several challenges due to the unstable dynamics of their mobile base and the coupling between the upper and lower body dynamics. The problem is often faced in the literature with methods based on the linearized or simplified models that fail in exploiting the whole body dynamic capabilities of the platform in use. In this letter, we tackle the problem of whole-body dynamic control for a mobile wheeled unstable humanoid robot. Compared to the existing approaches based on online optimization to guarantee respect of the constraints, we propose a control method that takes into account the nominal constrained dynamics of the robot in the quasi-velocities through an internal model, thus reducing the computational burden. A computed-torque control law in the quasi-velocities is used to stabilize the robot around the upper position. We report on preliminary experimental results and on the method effectiveness in rejecting unknown external disturbances.

Coordination is a core problem in multi-robot systems, since it is a key to ensure safety and efficiency. Both centralized and decentralized solutions have been proposed, however, most assume perfect communication. This letter proposes a centralized method that removes this assumption, and is suitable for fleets of robots driven by generic second-order dynamics. We formally prove that: first, safety is guaranteed if communication errors are limited to delays; and second, the probability of unsafety is bounded by a function of the channel model in networks with packet loss. The approach exploits knowledge of the network's non-idealities to ensure the best possible performance of the fleet. The method is validated via several experiments with simulated robots.

In this letter, we investigate how to resolve conflicting motions for mixed robot–robot and human–robot multiagent systems. This letter is motivated by atypical driving conditions, such as parking lots, where driving rules are not as strictly enforced as on standard roads. As a result, navigation algorithms should take into account the human drivers’ behaviors, especially if they prove to be in conflict with the common rules of the road. In this letter, we make use of safety barrier certificates with a direction bias to deconflict agents’ behavior in a near-to-collision scenario, in compliance with local traffic rules. We also propose a tool to identify the driving direction bias—both for human and autonomous agents.

Warehouse logistics is a rapidly growing market for robots. However, one key procedure that has not received much attention is the unwrapping of pallets to prepare them for objects picking. In fact, to prevent the goods from falling and to protect them, pallets are normally wrapped in plastic when they enter the warehouse. Currently, unwrapping is mainly performed by human operators, due to the complexity of its planning and control phases. Autonomous solutions exist, but usually they are designed for specific situations, require a large footprint and are characterized by low flexibility. In this work, we propose a novel integrated robotic solution for autonomous plastic film removal relying on an impedance-controlled robot. The main contribution is twofold: on one side, a strategy to plan Cartesian impedance and trajectory to execute the cut without damaging the goods is discussed; on the other side, we present a cutting device that we designed for this purpose. The proposed solution presents the characteristics of high versatility and the need for a reduced footprint, due to the adopted technologies and the integration with a mobile base. Experimental results are shown to validate the proposed approach., Funding Agency:Ministero dell' Istruzione, dell' Universita e della Ricerca (MIUR)

Accepted for ANTS 2018 Eleventh International Conference on Swarm Intelligence October 29-31, 2018. Rome, Italy; International audience; This work investigates collaborative aerial transportation by swarms of agents based only on implicit information, enabled by the physical interaction among the agents and the environment. Such a coordinating mechanism in collaborative transportation is a basic skill in groups of social animals. We consider cable-suspended objects transported by a swarm of flying robots and we formulate several hypothesis on the behavior of the overall system which are validated thorough numerical study. In particular, we show that a nonzero internal force reduces to one the number of asymptotically stable equilibria and that the internal force intensity is directly connected to the convergence rate. As such, the internal force represents the cornerstone of a communication-less cooperative manipulation paradigm in swarms of flying robots. We also show how a swarm can achieve a stable transportation despite the imprecise knowledge of the system parameters.

Autonomous robots will soon enter our everyday life as self-driving cars. These vehicles are designed to behave according to certain sets of cooperative rules, such as traffic ones, and to respond to events that might be unpredictable in their occurrence but predictable in their nature, such as a pedestrian suddenly crossing a street, or another car losing control. As civilian autonomous cars will cross the road, racing autonomous cars are under development, which will require superior Artificial Intelligence Drivers to perform in structured but uncertain conditions. We describe some preliminary results obtained during the development of a planning and control system as key elements of an Artificial Intelligence driver for the competition scenario.

In this paper, we study the minimum length paths covered by the center of a unicycle equipped with a limited field-of-view (FOV) camera, which must keep a given landmark in sight. Previous works on this subject have provided the optimal synthesis for the cases in which the FOV is only limited in the horizontal directions (i.e., left and right bounds) or in the vertical directions (i.e., upper and lower bounds). In this paper, we show how to merge previous results and hence obtain, for a realistic image plane modeled as a rectangle, a finite alphabet of extremal arcs and the overall synthesis. As shown, this objective cannot be straightforwardly achieved from previous results but needs further analysis and developments. Moreover, there are initial configurations such that there exists no optimal path. Nonetheless, we are always able to provide an $\varepsilon$ -optimal path whose length approximates arbitrarily well any other shorter path. As final results, we provide a partition of the motion plane in regions such that the optimal or $\varepsilon$ -optimal path from each point in that region is univocally determined.

This paper presents a contribution to the problem of obtaining an optimal synthesis for shortest paths for a unicycle guided by an on-board limited Field-Of-View (FOV) sensor, which must keep a given landmark in sight. Previous works on this subject have provided an optimal synthesis for the case in which the FOV is limited in the horizontal directions (H-FOV, i.e., left and right boundaries). In this paper we study the complementary case in which the FOV is limited only in the vertical direction (V-FOV, i.e., upper and lower boundaries). With respect to the H-FOV case, the vertical limitation is all but a simple extension. Indeed, not only the geometry of extremal arcs is different, but also a more complex structure of the synthesis is revealed by analysis. We will indeed show that there exist initial configurations for which the optimal path does not exist. In such cases, we provide an $\varepsilon$ -optimal path whose length approximates arbitrarily well any other shorter path. Finally, we provide a partition of the motion plane in regions such that the optimal or $\varepsilon$ -optimal path from each point in that region is univocally determined.

In this work is presented a novel platform able to navigate autonomously in unknown indoor environments. The system has been designed as a framework with different interconnected modules in order to be able to apply the same navigation system to different robotic platforms. The proposed framework is a real–time system developed on board of the robot, without the need of external computing units. The entire system has been tested and evaluated on multi–rotors vehicles with different hardware configurations, evaluating the performance and the quality of the reconstructed map. Experimental results show a promising robust and highly accurate indoor navigation module.

In this article a coordinated approach to 3D exploration of large uncluttered areas with a team of flying robots with constrained payload is proposed. Coordination is used as trump card for an effective exploration in feasible time and to overcome problems related to the limited computational power and autonomy of the robot platform. In particular, a 3D exploration strategy based on a combination of local and global information and a 4D motion planning are used for achieving 3D coverage of completely unknown environments.

Today, human intervention is the only effective course of action after a natural or artificial disaster. This is true both for relief operations, where search and rescue of survivors is the priority, and for subsequent activities, such as those devoted to building assessment. In these contexts, the use of robotic systems would be beneficial to drastically reduce operators? risk exposure. However, the readiness level of robots still prevents their effective exploitation in relief operations, which are highly critical and characterized by severe time constraints. On the contrary, current robotic technologies can be profitably applied in procedures like building assessment after an earthquake. To date, these operations are carried out by engineers and architects who inspect numerous buildings over a large territory, with a high cost in terms of time and resources, and with a high risk due to aftershocks. The main idea is to have the robot acting as an alter ego of the human operator, who, thanks to a virtual-reality device and a body-tracking system based on inertial sensors, teleoperates the robot.

The paper presents the first simulative results and algorithmic developments of the task-priority based control applied to a distributed sampling network in an area coverage or adaptive sampling mission scenario. The proposed approach allowing the fulfilment of a chain of tasks with decreasing priority each of which directly related to both operability and safety aspects of the entire mission. The task-priority control is presented both in the centralized and decentralized implementations showing a comparison of performance. Finally simulations of the area coverage mission scenario are provided showing the effectiveness of the proposed approach.

This paper presents a study of analysis of minimum-time trajectories for a differential drive robot equipped with a fixed and limited field-of-view camera, which must keep a given landmark in view during maneuvers. Previous works have considered the same physical problem and provided a complete analysis/synthesis for the problem of determining the shortest paths. The main difference in the two cost functions (length vs. time) lays on the rotation on the spot. Indeed, this maneuver has zero cost in terms of length and hence leads to a 2D shortest path synthesis. On the other hand, in case of minimum time, the synthesis depends also on the orientations of the vehicle. In other words, the not zero cost of the rotation on the spot maneuvers leads to a 3D minimum-time synthesis. Moreover, the shortest paths have been obtained by exploiting the geometric properties of the extremal arcs, i.e., straight lines, rotations on the spot, logarithmic spirals and involute of circles. Conversely, in terms of time, even if the extremal arcs of the minimum-time control problem are exactly the same, the geometric properties of these arcs change, leading to a completely different analysis and characterization of optimal paths. In this paper, after proving the existence of optimal trajectories and showing the extremal arcs of the problem at hand, we provide the control laws that steer the vehicle along these arcs and the time-cost along each of them. Moreover, this being a crucial step toward numerical implementation, optimal trajectories are proved to be characterized by a finite number of switching points between different extremal arcs, i.e., the concatenations of extremal arcs with infinitely many junction times are shown to violate the optimality conditions.

In this paper we present a novel geometric approach to motion planning for constrained robot systems. This problem is notoriously hard, as classical sampling-based methods do not easily apply when motion is constrained in a zero-measure submanifold of the configuration space. Based on results on the functional controllability theory of dynamical systems, we obtain a description of the complementary spaces where rigid body motions can occur, and where interaction forces can be generated, respectively. Once this geometric setting is established, the motion planning problem can be greatly simplified. Indeed, we can relax the geometric constraint, i.e., replace the lower-dimensional constraint manifold with a full-dimensional boundary layer. This in turn allows us to plan motion using state-of-the-art methods, such as RRT∗, on points within the boundary layer, which can be efficiently sampled. On the other hand, the same geometric approach enables the design of a completely decoupled control scheme for interaction forces, so that they can be regulated to zero (or any other desired value) without interacting with the motion plan execution. A distinguishing feature of our method is that it does not use projection of sampled points on the constraint manifold, thus largely saving in computational time, and guaranteeing accurate execution of the motion plan. An explanatory example is presented, along with an experimental implementation of the method on a bimanual manipulation workstation.

Several advanced control laws are available for complex robotic systems such as humanoid robots and mobile manipulators. Controls are usually developed for locomotion or for manipulation purposes. Resulting motions are usually executed sequentially and the potentiality of the robotic platform is not fully exploited. In this work we consider the problem of loco-manipulation planning for a robot with given parametrized control laws known as primitives. Such primitives, may have not been designed to be executed simultaneously and by composing them instability may easily arise. With the proposed approach, primitives combination that guarantee stability of the system are obtained resulting in complex whole-body behavior. A formal definition of motion primitives is provided and a random sampling approach on a manifold with limited dimension is investigated. Probabilistic completeness and asymptotic optimality are also proved. The proposed approach is tested both on a mobile manipulator and on the humanoid robot Walk-Man, performing loco-manipulation tasks.

Compliance in robot design and control is often introduced to improve the robot performance in tasks where interaction with environment or human is required. However a rigorous method to choose the correct level of compliance is still not available. In this work we use robust optimization as a tool to select the optimal compliance value in a robotenvironment interaction scenario under uncertainties. We propose an approach that can be profitably applied on a variety of tasks, e.g. manipulation tasks or locomotion tasks. The aim is to minimize the forces of interaction considering model constraints and uncertainties. Numerical results show that: i) in case of perfect knowledge of the environment stiff robots behave better in terms of force minimization, ii) in case of uncertainties the optimal stiffness of the robot is lower than the previous case and optimal solutions provide a faster task accomplishment, iii) the optimal stiffness decreases as a function of the uncertainty measure. Experiments are carried out in a realistic set-up in case of bi-manual object handover.

The purpose of this work is to move a step toward the automation of industrial plants through full exploitation of autonomous robots. A planning algorithm is proposed to move different objects in desired configurations with heterogeneous robots such as manipulators, mobile robots and conveyor belts. The proposed approach allows different objects to be handled by different robots simultaneously in an efficient way and avoiding collisions with the environment and self-collisions between robots. In particular, the integrated system will be capable of planning paths for a set of objects from various starting points in the environment (e.g. shelves) to their respective final destinations. The proposed approach unifies the active (e.g., grasping by a hand) and passive (e.g., holding by a table) steps involved in moving the objects in the environment by treating them as end-effectors with constraints and capabilities. Time varying graphs will be introduced to model the problem for simultaneous handling of objects by different end-effectors. Optimal exploration of such graphs will be used to determine paths for each object with time constraints. Results will be validated through simulations.

A software and control architecture for a humanoid robot is a complex and large project, which involves a team of developers/researchers to be coordinated and requires many hard design choices. If such project has to be done in a very limited time, i.e., less than 1 year, more constraints are added and concepts, such as modular design, code reusability, and API definition, need to be used as much as possible. In this work, we describe the software architecture developed for Walk-Man, a robot participant at the Darpa Robotics Challenge. The challenge required the robot to execute many different tasks, such as walking, driving a car, and manipulating objects. These tasks need to be solved by robotics specialists in their corresponding research field, such as humanoid walking, motion planning, or object manipulation. The proposed architecture was developed in 10 months, provided boilerplate code for most of the functionalities required to control a humanoid robot and allowed robotics researchers to produce their control modules for DRC tasks in a short time. Additional capabilities of the architecture include firmware and hardware management, mixing of different middlewares, unreliable network management, and operator control station GUI. All the source code related to the architecture and some control modules have been released as open source projects.

Sensor-guided robotic locomotion in different media often takes inspiration from natural examples, e.g. from fishes, birds, and humans for underwater, aerial, or walking robots, respectively. Interestingly, several naturalistic observations show that even very different species exhibit some similarities in their locomotion patterns: a notable one perhaps being the spiraling nature of paths that in some cases can be observed in sensory-guided tasks. As often conjectured in naturalistic studies, a common optimality principle may underpin such motion behaviors. We show in a robotics framework that spiraling motions appear in the solution of the problem of minimum path length. In particular, we study optimal paths for a simple model of finned, winged, and legged robotic vehicles, under different constraints on the field-of-view of the available sensory inputs. After showing that logarithmic spirals are indeed extremals of a sensory-constrained shortest-path problem, we provide a complete synthesis of the optimal control for different robotic vehicles. Application of these results to robotics can reduce the length of paths to be followed by underwater, aerial, or legged robots to reach targets in their environment. The work also provides some interesting, although preliminary, insights into how sensory field-of-view limitations may influence motion patterns in natural systems.

In this article, we consider how a very large numbers of robots, differing in their bodies, sensing, and intelligence, may be made to coexist, communicate, and compete fairly toward achieving their individual goals, i.e., to build a society of I robots. We discuss some characteristics that the rules defining acceptable social behaviors should possess. We consider threats that may be posed to such a society by the misbehaviors of some of its members, either due to faults or malice, and the possibility to detect and isolate them through cooperation of peers. The article presents examples of motion control protocols, for arbitrarily large groups of heterogeneous robots. We discuss intrusion detection algorithms, which allow detection of deviance from such rules, and algorithms to build a consensus view on the environment and on the integrity of peers, so as to improve the overall security of the society of robots.

A scalable platform for decentralized traffic management of a multi-agent system has been proposed. Safety of the platform is achieved with a cooperative conflict avoidance policy. Security of communications among vehicles with respect to potential external adversaries is obtained through use of cryptographic keys and rekeying policies. A prototypical implementation of the architecture has been described, and some experimental results have been reported.