An open problem in industrial automation is to reliably perform tasks requiring in-contact movements with complex workpieces, as current solutions lack the ability to seamlessly adapt to the workpiece geometry. In this paper, we propose a Learning from Demonstration approach that allows a robot manipulator to learn and generalise motions across complex surfaces by leveraging differential mathematical operators on discrete manifolds to embed information on the geometry of the workpiece extracted from triangular meshes, and extend the Dynamic Movement Primitives (DMPs) framework to generate motions on the mesh surfaces. We also propose an effective strategy to adapt the motion to different surfaces, by introducing an isometric transformation of the learned forcing term. The resulting approach, namely MeshDMP, is evaluated both in simulation and real experiments, showing promising results in typical industrial automation tasks like car surface polishing.

A clear understanding of where humans move in a scenario, their usual paths and speeds, and where they stop, is very important for different applications, such as mobility studies in urban areas or robot navigation tasks within human-populated environments. We propose in this article, a neural architecture based on Vision Transformers (ViTs) to provide this information. This solution can arguably capture spatial correlations more effectively than Convolutional Neural Networks (CNNs). In the paper, we describe the methodology and proposed neural architecture and show the experiments' results with a standard dataset. We show that the proposed ViT architecture improves the metrics compared to a method based on a CNN.

In the context of telehealth, robotic approaches have proven a valuable solution to in-person visits in remote areas, with decreased costs for patients and infection risks. In particular, in ultrasonography, robots have the potential to reproduce the skills required to acquire high-quality images while reducing the sonographer’s physical efforts. In this paper, we address the control of the interaction of the probe with the patient’s body, a critical aspect of ensuring safe and effective ultrasonography. We introduce a novel approach based on variable impedance control, allowing the real-time optimisation of compliant controller parameters during ultrasound procedures. This optimisation is formulated as a quadratic programming problem and incorporates physical constraints derived from viscoelastic parameter estimations. Safety and passivity constraints, including an energy tank, are also integrated to minimise potential risks during human-robot interaction. The proposed method’s efficacy is demonstrated through experiments on a patient’s dummy torso, highlighting its potential for achieving safe behaviour and accurate force control during ultrasound procedures, even in cases of contact loss.

In this paper, we propose a robot oriented knowledge representation system based on the use of the Prolog language. Our framework hinges on a special organisation of Knowledge Base (KB) that enables: 1) its efficient population from natural language texts using semi-automated procedures based on Large Language Models (LLMs); 2) the seamless generation of temporal parallel plans for multi-robot systems through a sequence of transformations; 3) the automated translation of the plan into an executable formalism. The framework is supported by a set of open source tools and its functionality is shown with a realistic application.

This paper introduces a new method for online estimating the penetration of the end-effector and the viscoelastic properties of a soft body, through palpation exams using a collaborative robotic arm. The estimator is based on the dimensionality reduction method that simplifies the nonlinear Hunt-Crossley model. In addition, in our algorithm, the model parameters can be found without a force sensor, leveraging only the robotic arm controller data. An extended Kalman filter is employed to achieve online estimation, which embeds the dynamic contact model. The algorithm is tested with various types of silicone, a material that resembles biological tissues, including samples with hard intrusions to simulate cancerous cells within a softer tissue. The results indicate that this technique can accurately determine the model parameters and estimate the penetration of the end-effector into the soft body. These promising preliminary results demonstrate robots’ potential to be an effective tool for early-stage cancer diagnostics.

Autonomous vehicle racing is facing a growing interest both in industrial and academic settings spanning multiple disciplines. In this paper, we will explore how to create a robust, efficient, and reliable decision-making mechanism to decide, at every point in time, the trajectories that a vehicle should take to overtake its opponents and win the race. The proposed framework combines a graph-based path planner with a game-theoretic model to generate powerful racing strategies. We implemented the framework, and we carried out an experimental evaluation to show its effectiveness and evaluate the impact of the different parameters.

Rescue missions in mountain environments are hardly achievable by standard legged robots—because of the high slopes—or by flying robots—because of limited payload capacity. We present a concept for a rope-aided climbing robot which can negotiate up-to-vertical slopes and carry heavy payloads. The robot is attached to the mountain through a rope, and it is equipped with a leg to push against the mountain and initiate jumping maneuvers. Between jumps, a hoist is used to wind/unwind the rope to move vertically and affect the lateral motion. This simple (yet effective) two-fold actuation allows the system to achieve high safety and energy efficiency. Indeed, the rope prevents the robot from falling while compensating for most of its weight, drastically reducing the effort required by the leg actuator. We also present an optimal control strategy to generate point-to-point trajectories overcoming an obstacle. We achieve fast computation time ($16\ m$long jump, showing the effectiveness of the proposed approach, and confirming the interest of our concept. Finally, we performed a reachability analysis showing that the region of achievable targets is strongly affected by the friction properties of the foot-wall contact.

The fifth-generation and beyond (B5G) communication systems are evolving for computation-intensive and communication-sensitive applications with diverse Quality-of-Service (QoS) requirements on processing, bandwidth, latency, and reliability. This work focuses on an ultra-dense edge network with Multi-access Edge Computing (MEC) facilities, serving agents that execute their tasks by touring the cells. Specifically, we propose a novel methodology for optimally and flexibly managing task offloading in the context of heterogeneous computing and communication services required by real-time robotic applications. Differing from many related work, the proposed approach takes the number of admitted service migrations and the QoS upper and lower bounds as binding constraints. We model the QoS evolution based on the agent positions, the MEC servers serving the agents, the QoS requirements, the communication capabilities in the edge network, and the computing capabilities of the servers. The model is formalized as a mixed-integer linear program (MILP) to obtain an optimal schedule for the service migrations and communication and computation bandwidth allocation. Experimental results show that the approach outperforms baseline approaches and can scale to large deployments.

This paper presents a parallel computing implementation of the Iterative Dynamic Programming (IDP) solution to the multipoint Markov-Dubins problem using GPUs. The multi-point Markov-Dubins problem requires the computation of the shortest path with bounded curvature that connects a sequence of planar points (waypoints). As well as being interesting in its own right, an efficient solution to this problem is key to finding optimal or suboptimal solutions of other problems such as the Dubins Travelling Salesman and the Dubins Orienteering problem. The constraint on the curvature makes the problem highly non-linear and complicates its solution. Classic methods are optimisation-based and cast the problem into the Nonlinear Programming (NLP) or Mixed Integer Nonlinear Programming (MINLP) frameworks, for which existing solutions cannot be significantly parallelised. On the contrary, the IDP solution proposed here is well suited for parallel execution. In the paper, we show that the parallel implementation of the IDP outperforms both the NLP/MINLP methods and the iterative version of the IDP methods in terms of accuracy, computation time and power consumption. Computation time and power consumption will be the main focus of the paper, because they are closely related to the implementation on an embedded platform.

The problem addressed in this paper is the localisation of a mobile robot using a combination of on-board sensors and Ultra-Wideband (UWB) beacons. Specifically, we consider a scenario in which a mobile robot travels across an area infrastructured with a small number of UWB anchors. The presence of obstacles in the environment introduces an offset in the measurements of the distance between the robot and the UWB anchors causing a degradation in the localisation performance. By using a discrete–time formulation of the system dynamics, we show that, under mild condition, the trajectories can be observed and the offset can be estimated in a finite number of steps. Besides being interesting in its on right, the global observability results offer a clear pathway towards the definition of a new generation of estimation algorithms.

In this article, we present a framework for planning an activity to be executed with the support of a robotic navigation assistant. The two main components are the activity and the motion planner. The activity planner composes a sequence of abstract activities, chosen from a given set, to synthesize a plan. Each activity is associated with a point of interest in the environment and with probabilistic parameters that depend on the plan, which are characterized by simulations in realistic scenarios. The low-level action to pass from an activity to the next is handled by the motion planner, which secures the physical feasibility of the chosen actions and their compatibility with the constraints posed by the user and the environment. Indeed, the final plan must respect the user constraints and optimise his/her satisfaction from the activity. We show a possible model for the problem as a chance constrained optimization along with an efficient technique to find high-quality solutions.

We present an algorithm for collision free and socially-aware navigation of multiple robots in an environment shared with human beings, other robots and with the presence of static obstacles. We formulate the problem as a constrained optimization problem, where the cost function is chosen in order for the robotic agents to exhibit bio-inspired behaviors, such as cooperation inside the group and cohesive motion. Some of the constraints are required to avoid collision between the agents and with other obstacles and emanate from the application of Velocity Obstacle approach. The nonholonomic dynamics of the vehicles, is managed through the application of the feedback linearization technique to map the velocities in the control values. In this paper we propose both centralized solution and a completely decentralized solution. The overall strategies are extensively tested in simulations.

We present a Lloyd-based navigation solution for robots that are required to move in a dynamic environment, where static obstacles (e.g, furnitures, parked cars) and unpredicted moving obstacles (e.g., humans, other robots) have to be detected and avoided on the fly. The algorithm can be computed in real-time and falls in the category of the reactive methods. Moreover, we propose an extension to the multi-agent case that deals with cohesion and cooperation between agents. The goodness of the method is proved through extensive simulations and, for the single agent navigation in human-shared environment, also with experiments on a unicycle-like robot.

Summary Model‐based development (MBD) holds the promise to capture potential timing problems in embedded software during the early phases of the development, securing the production of bug‐free embedded software. For most MBD approaches, the source code is just an intermediate artifact that can be generated automatically from the models. This assumption clashes with an undeniable fact: a large share of the commercial embedded software exploits existing libraries or is developed using C/C++ natively. A way to reconcile the ambitions of MBD with the use of a programming language is by offering new language constructs and an innovative compilation tool‐chain that prevents model error and timing problems “by construction.” However, the persistent popularity of C/C++ among embedded programmers and the limited availability of tools have severely limited the uptake of alternative programming languages for embedded software. Therefore, we propose an original route. Our language proposal, named Tice, has been shaped as a C++ active library. Tice retains full compatibility with existing C++ code, which can be integrated easily into new Tice‐based projects. The enforcement of Tice syntax and semantics can be made by a standard C++ compiler, forgoing the need for new tools. In this article, we describe Tice's syntax, semantics, and model of computation and communication. We demonstrate Tice's practical applicability on an industrial scale use‐case and give ample evidence for Tice's efficient compilation using off‐the‐shelf C++ compilers. Finally, we show Tice's code generation process.

Among the manifold wireless technologies recently adopted for indoor localization and tracking, radars based on phased-array transceivers at 60 GHz are gaining momentum. The main advantages of this technology are: high accuracy, good ability to track multiple target with a low computation burden and preservation of privacy. Despite the growing commercial success of low-cost radar platforms, accurate studies to evaluate their tracking performance are not frequent in the literature, the main reasons being the commercial policies that prevent a direct access to the processed data and the difficult calibration of indoor positioning systems under dynamic conditions. This paper aims to fill this gap providing an extensive and scientifically sound performance analysis of one of these sensors (i.e., the System-onChip (SOC) TI IWR6843) and exposing benefits and limitations of 60-GHz mm-wave sensors for people localization and tracking. Multiple experimental results show that the average standard positioning uncertainty is about 30 cm under dynamic conditions. Our study also reveals the critical impact of three parameters, which are not properly documented by the manufacturer. Localization accuracy and robustness are also significantly affected by the risk of delayed and spurious detections. In the paper, a possible mitigation strategy of these anomalies is presented.

We consider the problem of accurate and high-rate self-localization for a mobile robot. We adaptively combine the speed information acquired by proprioceptive sensors with intermittent positioning samples acquired via ultra-wideband (UWB) radios. These are triggered only if and when needed to reduce the positioning uncertainty, itself modeled by a probabilistic cost function. Our formulation is agnostic w.r.t. the source of uncertainty and enables an intuitive specification of user navigation requirements along with stochastic guarantees on the system operation. Experimental results in simulation and with a real platform show that our approach i) meets these guarantees in practice ii) achieves the same accuracy of a fixed periodic sampling but with significantly higher scalability and lower energy consumption iii) is resilient to errors in UWB estimates, enabling the use of low-accuracy ranging schemes which further improve these two performance metrics.

We consider the problem of guiding a senior user along a path using a robotic walking assistant. This is a particular type of path following problem, for which most of the solutions available in the literature require an exact localisation of the robot in the environment. An accurate localisation is obtained either with a heavy infrastructure (e.g., an active sensing system deployed in the environment or deploying landmarks in known positions) or using SLAM approaches with a massive data collection. Our key observation is that the intervention of the system (and a good level of accuracy) is only required in proximity of difficult decision points, while we can rely on the user in an environment where the only possibility is just to maintain a course (e.g., a corridor). The direct implication is that we can instrument the environment with a heavy infrastructure only in certain areas. This design strategy has to be complemented by an adequate control law that shifts the authority (i.e., the control of the actuators) between the robot and the user according to the accuracy of the information available to the robot. Such a control law is exactly the contribution of this paper.

Summary In recent years, series of important achievements have paved the way for the introduction of probabilistic analysis in the area of soft real‐time systems design. In this article, we present an extensible design tool, called PROSIT, which facilitates the access to this technology for a potentially large number of researchers and industrial practitioners. The tool enables the probabilistic analysis of the temporal performance of a real‐time task under fixed‐priority and resource reservations scheduling algorithms. For resource reservations, the tool also offers an automatic procedure for the synthesis of scheduling parameters that optimize a quality metric related to the probabilistic behavior of the tasks.

Assistive passive robotic walkers are naturally modelled as rear-driven bicycle with control on the front steering wheels. Standard path following algorithms used for unicycle-like robots can then be readily available, e.g. using backstepping techniques, to control the walker on desired paths. However, such an approach is usually singular in the very common situation of zero velocity, e.g. whenever the vehicle starts its motion or the user stops for any reason. The paper proposes a non-singular passive path following algorithm for an assistive robotic walker equipped with front steering wheels. The control law avoids the singularities, since it is velocity-independent, and allows the designer to specify saturation limits on the steering angles. The converging properties of the non-singular velocity-independent controller in the presence of saturation constraints are firstly formally proved and tested in simulations. Then extensive experiments performed on 14 testers are presented. These tests underline the promising performance of the proposed controller and the importance of singularities-avoidance in real-world scenarios to increase human comfort along the planned trajectory.

Indoor applications based on vehicular robotics require accurate, reliable and efficient localisation. In the absence of a GPS signal, an increasingly popular solution is based on fusing information from a dead reckoning system that utilises on-board sensors with absolute position data extracted from the environment. In the application considered in this paper, the information on absolute position is given by visual landmarks deployed on the floor of the environment considered. This solution is inexpensive and provably reliable as long as the landmarks are sufficiently dense. On the other hand, a massive presence of landmark has high deployment and maintenance costs. In this paper, we build on the knowledge of a large number of trajectories (collected from environment observation) and seek the optimal placement that guarantees a localisation accuracy better than a specified value with a minimal number of landmarks. After formulating the problem, we analyse its complexity and describe an efficient greedy placement algorithm. Finally, the proposed approach is validated in realistic use cases.

Robotic walkers are a particular class of devices used to assist users with physical or cognitive impairments in their navigation of large public spaces. In this context, a guidance mechanism is a controller that steers the user towards the desired path when he/she deviates. Passive guidance mechanisms do not directly propel the vehicle and leave the user in control of his/her walk. The inherent safety of passive guidance and the intuitive behaviour of the device make this class of solutions preferable to any other for robot assisted walking. The possible ways to obtain a passive behaviour in robotic walkers either require complex and expensive sensors or generate potentially rough (bang-bang) manoeuvres that are detrimental to the user's comfort. The contribution of this work is a path following controller that simulates passivity using a pair of active motors operating on the rear wheels of the walker. The system estimates and tracks the velocity desired by the user and the motors generate a rotational torque only when a turn toward the path is required. The proposed solution delivers high levels of comfort, does not rely on expensive hardware and preserves all the important properties of a passive mechanism.

A large class of modern real-time applications exhibits important variations in the computation time and is resilient to occasional deadline misses. In such cases, probabilistic methods, in which the probability of a deadline miss can be guaranteed and related to the scheduling design choices, can be an important tool for system design. Several techniques for probabilistic guarantees exist for the resource reservation scheduler and are based on the assumption that the process describing the application is independent and identically distributed (i.i.d.). In this paper, we consider a particular class of robotic application for which this assumption is not verified. For such applications, we have verified that the computation time is more faithfully described by a Markov model. We propose techniques based on the theory of hidden Markov models to extract the structure of the model from the observation of a number of execution traces of the application. As a second contribution, we show how to adapt probabilistic guarantees to a Markovian computation time. Our experimental results reveal a very good match between the theoretical findings and the experiments.

This paper presents a control algorithm that steers a robotic walking assistant along a planned path using electromechanical brakes. The device is modeled as a Dubins' car, i.e., a wheeled vehicle that moves only forward in the plane and has a limited turning radius. In order to reduce the cost of the hardware, no force sensor is employed. This feature hampers the application of control algorithms based on a modulated braking action. A viable solution is based on the application of on/off braking action, thus forcing the vehicle to turn with a fixed turning radius. In order to avoid the annoying chattering behaviour, which is the inevitable companion of all bang-bang solutions, we propose a hybrid controller based on three discrete states that rule the application of the braking action. The resulting feedback controller secures a gentle convergence of the user toward the planned path and his/her steady progress towards the destination. This is obtained by using two independent hystereses thresholds, the first one associated with the approaching phase and the second with the following phases. The system convergence toward the path is formally proved. Simulations and experiments show the effectiveness of the proposed approach and the good level of comfort for the user.

We consider the problem of self-localisation for a mobile robot in an environment with a requested level of accuracy. The robot moves in a known environment following typical trajectories, which can be characterised in statistical terms. One of the main drivers of this paper is its application to assistive robots guiding senior or impaired users in shopping centres or in other public spaces. To localise itself the robot uses onboard sensors such as encoders and inertial platforms. The level of noise in these sensors and the lack of absolute measurements determines a steady growth of the uncertainty on its position. To alleviate the problem, we assume the presence of a number of visual markers deployed in the environment. Whenever the robot comes across one of these sensors, the uncertainty on its position is reset. In the paper, we show a methodology to minimise the number of these sensors and to select their position so that the uncertainty is never worse than a given target threshold with an assigned probability.

We show a methodology for the computation of the probability of deadline miss for a periodic real-time task scheduled by a resource reservation algorithm. We propose a modelling technique for the system that reduces the computation of such a probability to that of the steady state probability of an infinite state Discrete Time Markov Chain with a periodic structure. This structure is exploited to develop an efficient numeric solution where different accuracy/computation time trade-offs can be obtained by operating on the granularity of the model. More importantly we offer a closed form conservative bound for the probability of a deadline miss. Our experiments reveal that the bound remains reasonably close to the experimental probability in one real-time application of practical interest. When this bound is used for the optimisation of the overall Quality of Service for a set of tasks sharing the CPU, it produces a good sub-optimal solution in a small amount of time.

An accurate model of human behaviour is crucial when planning robot motion in human environments. The Social Force Model (SFM) is such a model, having parameters that control both deterministic and stochastic elements. We have constructed an efficient motion planning algorithm by embedding the SFM in a control loop that determines higher level objectives and reacts to environmental changes. Low level predictive modelling is provided by the SFM fed by sensors; high level logic is provided by statistical model checking. To parametrise and improve our motion planning algorithm, we have conducted experiments to consider typical human interactions in crowded environments. We have identified a number of behavioural patterns which may be explicitly incorporated in the SFM to enhance its predictive power. In this paper we describe the results of these experiments and how we parametrise the SFM.

Models are used in control domains for early validation of system properties, using simulation or formal verification, and for the automatic generation of a software implementation. We propose an approach in which a functional model of the controls is matched to a model of the execution platform through an intermediate mapping model, that represents the software tasks and communication messages. The functional model is (partly) developed in Simulink and code is generated for each subsystem. Next, an abstract view of the functional model is imported in SysML. Using SysML, a model of the execution platform is created, and an implementation of the subsystems as a set of tasks and messages is defined and evaluated. The M2T Acceleo tool processes the mapping model and generates the Orocos-compliant task code executing the C/C++ functions generated from Simulink, and the inter-task communication. This paper outlines the proposed flow and provides the description of a robotic car testbench used to show the application of the methodology. The testbench has enough functional complexity and a distributed implementation to justify the creation of architecture models, while requiring a moderate cost and effort for its construction by the interested researchers.

We consider a set of control tasks sharing a CPU and having stochastic execution requirements. Each task is associated with a deadline: when this constraint is violated the particular execution is dropped. Different choices of the scheduling parameters correspond to a different probability of deadline violation, which can be translated into a different level for the Quality of Control experienced by the feedback loop. For a particular choice of the metric quantifying the global QoC, we show how to find the optimal choice of the scheduling parameters.

This paper presents a position tracking technique based on multisensor data fusion for rollators helping elderly people to move safely in large indoor spaces such as public buildings, shopping malls or airports. The proposed technique has been developed within the FP7 project DALi, and relies on an extended Kalman filter processing data from dead-reckoning sensors (i.e. encoders and gyroscopes), a short-range radio frequency identification (RFID) system and a front Kinect camera. As known, position tracking based on dead-reckoning sensors only is intrinsically affected by growing uncertainty. In order to keep such uncertainty within wanted boundaries, the position values are occasionally updated using a coarse-grained grid of low-cost passive RFID tags with known coordinates in a given map-based reference frame. Unfortunately, RFID tag detection does not provide any information about the orientation of the rollator. Therefore, a front camera detecting some markers on the walls is used to adjust direction. Of course, the data rate from both the RFID reader and the camera is not constant, as it depends on the actual user's trajectory and on the distance between pairs of RFID tags and pairs of markers. Therefore, the average distance between tags and markers should be properly set to achieve a good trade-off between overall deployment costs and accuracy. In the paper, the results of a simulation-based performance analysis are reported in view of implementing the proposed localization and tracking technique in a real environment.

This paper presents a new Model of Computation (MoC) for real-time tasks used in control systems. This new model, named continuous stream task model, relaxes some of the constraints imposed by the traditional hard and soft real-time task models. A key advantage of the model is the possibility to easily analyse the probabilistic evolution of the delays. This leads to an easy formalisation of necessary and sufficient conditions for the stochastic stability of the closed loop system producing considerable savings in the amount of CPU bandwidth required to stabilise the system. This fact is confirmed by an extensive set of simulations.

The application of a resource reservation scheduler to soft real -- time systems requires effective means to compute the probability of a deadline miss given a particular choice for the scheduling parameters. This is a challenging research problem, for which only numeric solutions, complex and difficult to manage, are currently available. In this paper, we adopt an analytical approach. By using an approximate and conservative model for the evolution of a periodic task scheduled through a reservation, we construct a closed form lower bound for the probability of a deadline miss. Our experiments reveal that the bound remains reasonably close to the experimental probability for many real -- time applications of interest.

High performance robotics is traditionally considered as an application area reserved to university laboratories and to the research centres of a limited group of company, which can afford high investments in equipments and system engineering. We make the point that this is not necessarily true if an adequate design is used to compensate for the lack of sophisticated sensors. To prove the validity of this idea we propose a concrete case study: driving a car-like vehicle at a high speed with a cost of the hardware below 500 Euros.

This paper presents an efficient algorithm for providing probabilistic guarantees in soft real-time systems using resource reservations. We use a conservative model for the temporal evolution of a resource reservation, which has a particular structure - a quasi birth death process - enabling an efficient computation of the stationary probability of respecting deadlines. We show the accuracy and the efficiency of the method in a large set of experiments.

Multimedia applications are often executed on standard personal computers. The absence of established standards has hindered the adoption of real-time scheduling solutions in this class of applications. Developers have adopted a wide range of heuristic approaches to achieve an acceptable timing behavior but the result is often unreliable. We propose a mechanism to extend the benefits of real-time scheduling to legacy applications based on the combination of two techniques: (1) a real-time monitor that observes and infers the activation period of the application, and (2) a feedback mechanism that adapts the scheduling parameters to improve its real-time performance.

In this paper, we propose two adaptive scheduling approaches to support real-time control applications with highly varying computation times. The use of a resource reservation scheduler enables the construction of a dynamic model describing the evolution of the computing delays, which can be incorporated in the system closed loop dynamics. The two approaches differ for the assumptions on the sequence of computation time. In the first approach, we have only an aggregate information (best case and worst case computation time) and design an adaptive scheduler that maintains the delay within the maximum bound compatible with the asymptotic stability of the system. In the second case, we assume a deeper knowledge on the distribution of the computation time and design an adaptive scheduler that ensures second moment stability of the system. The two approaches are evaluated on a case study exposing the different trade-offs between bandwidth and performance.

We propose an adaptive scheduling technique to schedule highly dynamic multimedia tasks on a CPU. We use a combination of two techniques: the first one is a feedback mechanism to track the resource requirements of the tasks based on “local” observations. The second one is a mechanism that operates with a “global” visibility, reclaiming unused bandwidth. The combination proves very effective: resource reclaiming increases the robustness of the feedback, while the identification of the correct bandwidth made by the feedback increases the effectiveness of the reclamation. We offer both theoretical results and an extensive experimental validation of the approach.

Sensor networks are increasingly used to control and monitor industrial and manufacturing processes. In this paper, we consider the problem of optimizing a cost function for wireless sensor networks of this kind under energy consumption constraints. We focus, in particular, on the problem of coverage optimization through scheduling. Following existing approaches, we use a mixed integer linear program formulation. We show how to use partitioning techniques to decompose the problem into separate subproblems, solved individually, overcoming the exponential complexity typical of integer linear programming, while minimizing the loss in optimality. In addition, we evaluate the achieved degree of optimality by computing relatively tight bounds with respect to the optimal solution. Finally, we employ simple but effective heuristics to further improve our solution. The results show that our procedure is very efficient and scalable, and is able to find solutions that are very close to optimal. These characteristics make our approach a perfect fit for large and fixed deployments of wireless sensors, typical in factory automation and industrial applications. To show the generality of the approach, we apply our methodology to three different models of varying complexity.

A new generation of distributed real-time systems (DRTS) is based on heterogeneous models of computation and communication and is associated with flexible real-time constraints. Classical design flows based on realtime scheduling theory display important limitations related to the restrictive assumption on the system model. On the other hand, formal verification of timed automata is far more general, but it suffers a different limitation: it does not provide any guide on how to choose the design parameters, nor does it permit to gauge the robustness of the design against unknown parameters. In this paper, we advocate the use of formal verification of parametric timed automata as a means to combine the best of the two approaches. The feasibility of the idea is shown on a significant industrial case study.

We present an approach for adaptive scheduling of soft real-time legacy applications (for which no timing information is exposed to the system). Our strategy is based on the combination of two techniques: 1) a real-time monitor that observes the sequence of events generated by the application to infer its activation period, 2) a feedback mechanism that adapts the scheduling parameters to ensure a timely execution of the application. By a thorough experimental evaluation of an implementation of our approach, we show its performance and its efficiency.

We consider the problem of predicting the probability of an accident in working environments where human operators and robotic manipulators co-operate. We show how, starting from a stochastic discrete time system describing human motion, it is possible to construct a discrete abstraction of the system (a discrete time Markov Chain) to predict the possible trajectories starting from an initial point. The DTMC is used to predict the future evolution for the system, for a fixed horizon, pinpointing the states that, at each step, can be marked as dangerous. This way, the system estimates the probability of an accident and stops the robot when the result is greater than a threshold.

Some real-time kernels (such as a recent real-time version of Linux) permit to execute interrupt handlers in dedicated threads, to control their interference on real-time applications. However, from the stand-point of real-time analysis, such threads are challenging and cannot be dealt with in the traditional ways. Furthermore, the application of traditional scheduling solutions (such as fixed priorities) proves ineffective in striking a good trade-off between predictability and hardware performance. This paper shows how the problem can be tackled by using the resource reservation abstraction and an appropriate model for schedulability analysis.

We consider soft real-time applications organized as pipelines of tasks using resources of different type (communication, computation, and storage). The applications are assumed to be periodically triggered and the different tasks communicate by unidirectional buffers. The problem we cope with is how to effectively share the resources so that some specified Quality of Service (QoS) requirements are met. The QoS considered here is tightly related to the end-to-end temporal behavior of the application. To compensate for time-varying resource requirements, we advocate a distributed control approach whereby the scheduling parameters of each task are tuned depending on the temporal behavior of the application measured by appropriate sensors. The use of real-time scheduling strategies enables a mathematically safe control design in which the QoS requirements are translated into control goals, and formal proofs are provided on the ability of the controller to fulfil these goals. We also offer extensive simulations that validate the approach for multimedia applications.

We consider a dynamic set of soft real-time applications using a set of shared resources. Each application can execute in different modes, each one associated with a level of Quality-of-Service (QoS). Resources, in their turn, have different modes, each one with a speed and a power consumption, and are managed by a Reservation-Based scheduler enabling a dynamic allocation of the fraction of resources (bandwidth) assigned to each application. To cope with dynamic changes of the application, we advocate an adaptive resource allocation policy organized in two nested feedback loops. The internal loop operates on the scheduling parameter to obtain a resource allocation that meets the temporal constraints of the applications. The external loop operates on the QoS level of the applications and on the power level of the resources to strike a good tradeoff between the global QoS and the energy consumption. This loop comes into play whenever the workload of the application exceeds the bounds that permit the internal loop to operate correctly, or whenever it decreases below a level that permit more aggressive choices for the QoS or substantial energy saving.

In this paper, we present a methodology for designing embedded controllers based on the so-called anytime control paradigm. A control law is split into a sequence of subroutine calls, each one fulfilling a control goal and refining the result produced by the previous one. We propose a design methodology to define a feedback controller structured in accordance with this paradigm and show how a switching policy of selecting the controller subroutines can be designed that provides stability guarantees for the closed-loop system. The cornerstone of this construction is a stochastic model describing the probability of executing, in each activation of the controller, the different subroutines. We show how this model can be constructed for realistic real-time task sets and provide an experimental validation of the approach.

Mobile robots are increasingly utilised in automated plants to the purpose of moving wares and material between the different production lines and logistic areas. In this context, the presence of human operators in the facility is frequently allowed to carry out or supervise some phases of the production. The problem arises of how to make the coexistence possible with controlled risks for the operator and without affecting the productivity with frequent interruptions. In this paper we propose a solution to this problem based on a probabilistic technique. A system of visual sensor (mounted on the mobile robots) detects the presence of a human operator and a discrete abstraction (essentially a discrete-time Markov chain) is used to predict his/her motion and hence find the probability of an accidental injury. For the computation of the latter, we combine the probability of having a collision with a given speed with the probability of receiving an injury out of the collision (taken from physiological models suggested by the automotive literature).

We consider the problem of power-aware quality of service (QoS) control for soft real-time embedded systems. Applications can have time-varying and scarcely known resource requirements, and can be activated and terminated at any time. However, they have the capability to switch among a discrete set of operation modes with different QoS levels and resource requirements. In addition, the platform provides resources with power-scaling capabilities and may be subject to power constraints. We present a QoS control architecture achieving optimum trade-offs between overall QoS and power consumption of the system, based on two nested control loops. The external one decides dynamically the optimum configuration for the system, in terms of application QoS modes and resource power modes, while the internal one modulates the resource allocations on a job by job basis, so as to respect timing constraints. We demonstrate the effectiveness of the approach by extensive simulations with trace data of real multimedia applications.

In this paper we present a methodology for designing embedded controllers with a variable accuracy. The adopted paradigm is the so called any-time control, which derives from the computing paradigm known as "imprecise computation". The most relevant contributions of the paper are a procedure for designing an incremental control law, whose different pieces cater for increasingly aggressive control requirements, and a modelling technique for the execution platform that allows us to design provably correct switching policies for the controllers. The methodology is validated by both simulations and experimental results.

Abstract This paper presents an architecture for quality of service (QoS) control of time‐sensitive applications in multi‐programmed embedded systems. In such systems, tasks must receive appropriate timeliness guarantees from the operating system independently from one another; otherwise, the QoS experienced by the users may decrease. Moreover, fluctuations in time of the workloads make a static partitioning of the central processing unit (CPU) that is neither appropriate nor convenient, whereas an adaptive allocation based on an on‐line monitoring of the application behaviour leads to an optimum design. By combining a resource reservation scheduler and a feedback‐based mechanism, we allow applications to meet their QoS requirements with the minimum possible impact on CPU occupation. We implemented the framework in AQuoSA (Adaptive Quality of Service Architecture (AQuoSA). http://aquosa.sourceforge.net ), a software architecture that runs on top of the Linux kernel. We provide extensive experimental validation of our results and offer an evaluation of the introduced overhead, which is perfectly sustainable in the class of addressed applications. Copyright © 2008 John Wiley & Sons, Ltd.

A remarkable research activity has been carried out in the past few years to support real-time applications with appropriate scheduling solutions. Unfortunately, most of such techniques can be used only if real-time applications use a specialized API, and if some important information (such as the worst-case execution-time) are knownapriori. In this paper, we present a novel technique, the legacy feedback scheduler (LFS), for a class of legacy applications that need the support of a real-time scheduler but are not written using a specialized API and have unknown or varying execution requirements. The approach is based on the combination of a resource reservation scheduler and a feedback-based adaptation mechanism for identifying the correct scheduling parameters.

Resource reservations are an effective technique to support hard and soft real-time applications in open systems. However, they generally focus on providing guarantees to real-time applications, without paying too much attention to the performance of non-real-time activities. In this paper, the main limitations encountered when using a conventional reservation-based scheduler for serving non-real-time tasks are described and formally analyzed. Then, a novel algorithm that overcomes these problems (called HGRUB) is proposed, and both theoretical and experimental evidence of its effectiveness is provided.

A remarkable research activity has been carried out in the past few years to support real-time applications by means of appropriate scheduling solutions. For scarcely known or highly dynamic applications, feedback scheduling has emerged as an effective technique to tune the scheduling parameters, based on a run-time monitoring of the timing performance of the application. This technique requires a specialised API for the application and is therefore unfit for legacy applications, for which the source code is not accessible. In this paper, we present an alternative technique, called legacy feedback scheduling (LFS), for feedback based adaptation of the scheduling parameters. LFS does not make any assumption on the programming model of the application and is applicable to legacy applications. Throughout the paper, we carry out a comparison between a well settled technique (called adaptive reservations), which leverages a particular structure for the application, and LFS. The conclusion is that the greater generality and flexibility of the LFS has to be paid in terms of timing performance. However, LFS successfully identifies the ldquoaveragerdquo scheduling parameters needed to sustain a given execution rate.

In this paper, we consider the problem of providing QoS guarantees to the execution of applications using the X11 window system. In particular, we offer a system level analysis of the issues encountered when using X11 to serve realtime applications. By using a tracer developed for the purpose we analyse in depth the internal behaviour of the system. The result of the analysis puts on display the adverse effect played by a non real-time scheduler on the performance of time-sensitive applications. Based on this analysis, we propose an alternative solution based on the CBS scheduler and prove its effectiveness by an extensive set of experiments on real hardware.

In this paper, we address the problem of symbolically computing the region in the parameter's space that guarantees a feasible schedule, given a set of real-time tasks characterised by a set of parameters and by an activation pattern. We make three main contributions. First, we propose a novel and general method, based on parametric timed automata. Second, we prove that the algorithm terminates for the case of periodic processes with bounded offsets. Third, we provide an implementation based on the use of symbolic model checking techniques for parametric timed automata, and present some case studies.

This paper tackles the problem of traffic and packet scheduling in HCCA, the contention-free portion of the 802.11e MAC protocol. Scheduling traffic belonging to different categories or priorities on a shared channel, as in wireless LANs, is a multi-class complex optimization problem. We consider two different scheduling techniques, one based on an extremely simple positional controller and one based on the optimal solution of the linearized control problem based on a fluid approximation. Simulation results are presented discussing pros and cons of the two solutions both in terms of performances (resource exploitation and traffic differentiation properties) and in terms of implementation complexity and fairness.

In this paper, we address the problem of adaptively reserving the CPU to concurrent soft real-time tasks, in order to meet target quality of service requirements. First, we present two new techniques inspired to the idea of stochastic control. Then, we present a flexible and modular software architecture suitable for adaptive scheduling, realised as a minimally invasive set of modifications to the Linux kernel. Finally, we show experimental results that validate our approach and prove its effectiveness in the context of multimedia applications.

When executing soft real-time tasks in a shared processor, it is important to properly allocate the computational resources such that the quality of service requirements of each task are satisfied. In this paper we propose Adaptive Reservations, based on applying a feedback scheme to a reservation based scheduler After providing a precise mathematical model of the scheduler, we describe how this model can be used for synthesising the controller by applying results from control theory. Finally, we show the effectiveness of our method by simulation and by experiments with an MPEG player running on a modified Linux kernel.

Abstract This paper presents an object‐oriented software tool, called RTSIM, aimed at simulating real‐time embedded controllers. The tool consists of a collection of C++ libraries permitting a separate specification of the functional behaviour of the controller and of the hardware/software architecture to be used for its deployment. In particular, it is possible to provide an accurate modelling of the concurrent architecture of the control tasks and of the run‐time support offered by the operating system for the real‐time scheduling of the shared resources (CPU, memory buffers and network links). In this way, it is possible to compare different scheduling solutions by evaluating their simulated performance directly in the domain of the control application. Moreover, the tool can be utilized to tune up design parameters such as the activation frequencies of the tasks. The application of the tool is shown in a meaningful case study. Copyright © 2002 John Wiley & Sons, Ltd.

This paper presents a discrete-time multirate technique for the set-point stabilization of nonholonomic systems in a chained form. Global practical exponential stabilization of the trivial equilibrium in the continuous-time system dynamics is achieved, by using multirate piecewise inputs and an inter-sampling analysis. As a case study, we consider the car-like robot stabilization problem. The control system quality is assessed through a quadratic cost function associated to the multi-sampling period. Robustness with respect to measurement errors and switching delays is also investigated.

A remarkable class of soft real time applications exhibits a very dynamical behaviour due to the variations in the treated data. Moreover, such programs have to be able to run on hundreds of different platforms. As a consequence, classical real time scheduling algorithms are not flexible enough since they are based on the exact knowledge of the tasks' timing parameters. Some of the approaches proposed so far in the literature guarantee temporal isolation, but they make a static assignment of resources to each task, which, once again, is based on an a priori knowledge. The authors propose a closed loop method for online adapting of the fraction of assigned resource to the task requirements. The approach is based on adaptive control techniques and has resulted in being effective in a significant set of real life experiments.

This paper focuses on the problem of controlling the relative position between a moving target and an autonomous nonholonomic vehicle. This problem represents a meaningful case study belonging to the class of multirate nonlinear control systems. A multi-level stabilizing control system is synthesized in continuous-time, based on a detailed analytic model of the vehicle, including the robot kinematics and nonholonomic dynamics, and actuators transfer functions. The discrete-time implementation provides a multirate nonlinear digital controller. Simulations in both continuous and discrete-time domain highlights interesting performance variations effects arising from different choices of the sampling frequencies.

A typical approach for realizing digital controllers is to synthesize the control law in the continuous-time domain and then to implement it as a set of periodic threads complying with tight temporal constraints. The strict respect of all deadlines can often be obtained only by selecting low activation rates which determine a remarkable performance degradation. On the other hand, many control systems are known to tolerate a certain amount of deadline misses. We realized a software tool which allows to numerically evaluate the quality of the control resulting from the scheduling. The tool has been applied to a robotic case study. Considering a meaningful set of trajectories, we have drawn experimental evidence that the use of soft real-time constraints on the threads leads to significant improvements in the system performance. The performance improvement is more evident if scheduling approaches like resource reservation schemes, able to separate the thread importance from its activation rate, are used.