Abstract In automotive engineering, vehicle platooning is a proposed method for improving convoy movements by coordinating them to increase the safety and efficiency of transportation systems. The complexity and stochastic nature of platooning systems provide difficulties for traditional model-checking techniques. However, Statistical Model Checking (SMC) offers a solution by using statistical inference to probabilistically evaluate system features. This paper shows that Uppaal SMC offers a robust framework for assessing platooning systems across various operational scenarios, combining statistical analysis and formal verification methodologies. The behaviour of the platoon is modelled stochastically to cover a wide range of driving scenarios and road surfaces. Using SMC, it has been possible to gauge the safety and functionality of the platoon by estimating the probability of several properties.

This paper addresses the lateral wind gust estimation and compensation problem for racecar models. A wind-sensorless solution, i.e. a solution not using direct wind measures, is proposed. More precisely, by modeling the wind disturbance as a fully unknown input signal, an input-state observer is derived using only information about the vehicle’s longitudinal speed and lateral pose relative to the road. The observer is characterized by a simple structure, explicit closed-form, direct implementability on a micro-controller, and dead-beat property, i.e. it ensures the convergence of the estimation error in a finite time. Moreover, leveraging on the reconstructed wind data, a backstepping wind-compensation controller is also proposed, allowing asymptotic tracking of a path with desired curvature and providing the end-user with a free control parameter specifying the desired tracking speed. Formal proofs of the estimation error and tracking error convergence are given. Performance evaluation of the proposed solution is obtained in simulation by closing in the loop the full nonlinear model of a real racecar, the Robocar system, with the proposed estimation and control method. Both the estimator and the controller are shown to outperform existing solutions, even in the presence of noisy measurements.

Abstract Unmanned aerial vehicle (UAV) co-operative systems are complex cyber-physical systems that integrate a high-level control algorithm with pre-existing closed implementations of lower-level vehicle kinematics. In model-driven development, simulation is one of the techniques that are usually applied, together with testing, in the analysis of system behaviours. This work proposes a method and tools to validate the design of UAV co-operative systems based on co-simulation and formal verification. The method uses the Prototype Verification System, an interactive theorem prover based on a higher-order logic language, and the Functional Mock-up Interface, a widely accepted standard for co-simulation. In this paper, results on the co-simulation and proofs of safety requirements of a representative co-ordination algorithm are shown and discussed in a scenario where quadcopters are deployed and perform space-coverage operations.

This paper presents an approach to create high-fidelity models suited for digital twin application of distributed multi-agent cyber–physical systems (CPSs) exploiting the combination of simulation units through co-simulation. This approach allows for managing the complexity of cyber–physical systems by decomposing them into multiple intertwined components tailored to specific domains. The native modular design simplifies the building, testing, prototyping, and extending CPSs compared to monolithic simulator approaches. A system of platoon of vehicles is used as a case study to show the advantages achieved with the proposed approach. Multiple components model the physical dynamics, the communication network and protocol, as well as different control software and external environmental situations. The model of the platooning system is used to compare the performance of Vehicle-to-Vehicle communication against a centralized multi-access edge computing paradigm. Moreover, exploiting the detailed model of vehicle dynamics, different road surface conditions are considered to evaluate the performance of the platooning system. Finally, taking advantage of the co-simulation approach, a solution to drive a platoon in critical road conditions has been proposed. The paper shows how co-simulation and design space exploration can be used for parameter calibration and the design of countermeasures to unsafe situations.

This paper presents an approach to create high fidelity Digital-Twin models for distributed multi-agent cyber-physical systems based on the combination of simulating components, generated from different modeling languages, each tailored for the specific domain of the subsystem. The approach specifically addresses the wireless communication domain, exploiting a Python module as a simulating component to evaluate the impact of network delay among the distributed elements of the system under analysis. A case study with a platoon of four vehicles following a leading car, all modeled in Simulink, is used to show the applicability of the approach, allowing the comparison between a Vehicle-to-Vehicle communication against a centralized multi-access edge computing paradigm.

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 propose a distributed coordination algorithm for safe and efficient traffic management of heterogeneous robotic agents, moving within dynamic large scale industrial environments. The algorithm consists of a distributed resource-sharing protocol involving a re-planning strategy. Once every agent is assigned with a desired motion path, the algorithm ensures ordered traffic flows of agents, that avoid inter-robot collision and system deadlock (stalls). The algorithm allows multi-level representation of the environment, i.e. large or complex rooms may be seen as a unique resource with given capacity at convenience, which makes the approach appealing for complex industrial environments. Under a suitable condition on the maximum number of agents with respect to the capacity of the environment, we prove that the algorithm correctly allows mutual access to shared resources while avoiding deadlocks. The proposed solution requires no centralized mechanism, no shared memory or ground infrastructure support. Only a local inter-robot communication is required, i.e. every agent must communicate with a limited number of other spatially adjacent robots. We finally show the effectiveness of the proposed approach by simulations, with application to an industrial scenario.

This paper addresses the classification problem for a set of autonomous robots that interact with each other. The objective is to classify agents that "behave" in "different way", due to their own physical dynamics or to the interaction protocol they are obeying to, as belonging to different "species". This paper describes a technique that allows a decentralized classification system to be built in a systematic way, once the hybrid models describing the behavior of the different species are given. This technique is based on a decentralized identification mechanism, by which every agent classifies its neighbors using only local information. By endowing every agent with such a local classifier, the overall system is enhanced with the ability to run behaviors involving individuals of the same species as well as of different ones. The mechanism can also be used to measure the level of cooperativeness of neighbors and to discover possible intruders among them. General applicability of the proposed solution is shown through examples of multiagent systems from Biology and from Robotics.

This paper focuses on casting manipulation and presents an innovative mechanism that allows objects placed in a three-dimensional space to be reached with a relatively small robot. Casting manipulation is a technique by which a robotic end-effector is thrown and its ballistic flight is controlled through a tether cable so as to reach a target object. Previous work presented a solution that is viable only when the position of the target object is perfectly known or aligned with the throwing plane. Our work extends the technique by use of a novel mechanical design of the arm, and a suitable control scheme for the flight of the end-effector, which makes it applicable for objects placed at generic positions in a three-dimensional environment. Effectiveness of the casting robot was shown during the first ESA Lunar Robotics Challenge, where our team came in the second place.

In this paper, we address decision making problems, depending on a set of input events, with networks of dynamic agents that have partial visibility of such events. Previous work by the authors proposed so-called logical consensus approach, by which a network of agents, that can exchange binary values representing their local estimates of the events, is able to reach a unique and consistent decision. The approach therein proposed is based on the construction of an iterative map, whose computation is centralized and guaranteed under suitable conditions on the input visibility and graph connectivity. Under the same conditions, we extend the approach in this work by allowing the construction of a logical linear consensus system that is globally stable in a fully distributed way. The effectiveness of the proposed method is showed through the real implementation of a wireless sensor network as a framework for the surveillance of an urban area.

A general technique to build a dynamic and distributed intrusion detector for a class of multi-agent systems is proposed in this paper, by which misbehavior in the motion of one or more agents can be discovered. Previous work from the authors has focused on how to distinguish the behavior of a misbehaving agent in a completely distributed way, by developing a solution where agents act as local monitors of their neighbors and use locally sensed information as well as data received from other monitors at a particular time. In this work, we improve the system detection capability by allowing monitors to use information collected at different instants and thus realizing a dynamic state observer that is valid for any system in the considered class. Finally, we show through simulations the effectiveness of the proposed solution for a case study.

This paper describes the design and implementation of DAVID, a lunar vehicle developed for the European Space Agency (ESA) Lunar Robotics Challenge, presenting severe terrain negotiation and sample acquisition challenges. We discuss in some detail two of the main innovative aspects of our entry to the challenge, i.e. the locomotion system and the sample acquisition system. Motivated by the challenge specifications, a range of different locomotion systems were considered, among which we chose a simple, rugged and effective wheeled system. We provide an account of the choice of five different types of wheels, which were designed, analyzed and experimentally tested in conditions similar to the challenge. The system eventually turned out to be very effective in negotiating 89% slopes of volcanic terrain on the challenge site, Mount Teide in Tenerife. To reduce the distance to be traveled on the difficult terrain and avoid risks in reaching the lowest parts of a crater, the vehicle was endowed with an innovative sample acquisition system, i.e. a casting manipulator. Casting manipulation is a technique in which the end-effector is thrown, the sample material is acquired, and the end-effector is retrieved using a light tether that acts as a "fishing line". The casting manipulator developed for DAVID uses an innovative sling-like technique, capable to obtain longer and more precise casts than previous oscillating versions. The analysis and experimental verification of DAVID's robot sling are reported, demonstrating its effectiveness. Finally, we give a brief account of the outcomes of the ESA Lunar Robotics Challenge, where our team came in second over other 8 teams that passed the final qualification phase.

This paper addresses a security problem in robotic multi-agent systems, where agents are supposed to cooperate according to a shared protocol. A distributed Intrusion Detection System (IDS) is proposed here, that detects possible non-cooperative agents. Previous work by the authors showed how single monitors embedded on-board the agents can detect non- cooperative behavior, using only locally available information. In this paper, we allow such monitors to share the collected information in order to overcome their sensing limitation. In this perspective, we show how an agreement on the type of behavior of a target-robot may be reached by the monitors, through execution of a suitable consensus algorithm. After formulating a consensus problem over non-scalar quantities, and with a generic update function, we provide conditions for the consensus convergence and an upper bound to its transient duration. Effectiveness of the proposed solution is finally shown through simulation of a case study.

In this paper we address the problem of generating input plans to steer complex dynamical systems in an obstacle-free environment. Plans considered admit a finite description length and are constructed by words on an alphabet of input symbols, which could be e.g. transmitted through a limited capacity channel to a remote system, where they can be decoded in suitable control actions. We show that, by suitable choice of the control encoding, finite plans can be efficiently built for a wide class of dynamical systems, computing arbitrarily close approximations of a desired equilibrium in polynomial time. Moreover, we illustrate by simulations the power of the proposed method, solving the steering problem for an example in the class of underactuated systems, which have attracted wide attention in the recent literature

In this paper, we present a method to control casting manipulation by means of real-time visual feedback. Casting manipulation is a technique to deploy a robotic end-effector at large distances from the robot’s base, by throwing the end-effector and controlling its ballistic flight using forces transmitted through a light tether connected to the end-effector itself. The tether cable can also be used to retrieve the end-effector and exert forces on the robot’s environment. Previous work has shown that casting manipulation is able to catch objects at a large distance, proving it viable for applications such as sample acquisition and return, rescue, etc. In previous experiments, the position of the target object was known exactly. In this paper, we present a first attempt at closing a real-time control loop on casting manipulation using visual feedback of moving targets. As accurate planning methods developed for off-line open-loop planning cannot be used in real-time, we develop a simplified model and control algorithm, whose effectiveness is demonstrated through experiments.