Optimization of vehicle maneuvers using dynamic models in constrained spaces is challenging. Homotopic optimization, which has shown success for vehicle maneuvers with kinematic models, is studied in the case where the vehicle model is governed by dynamic equations considering road-tire interactions. This method involves a sequence of optimization problems that start with a large free space. By iteration, this space is progressively made smaller until the target problem is reached. The method uses a homotopy index to iterate the sequence of optimizations, and the method is verified by solving challenging maneuvering problems with different road surfaces and entry velocities using a double-track vehicle dynamics model. The main takeaway is that homotopic optimization is also efficient for dynamic vehicle models at the limit of road-tire friction, and it demonstrates capabilities in solving demanding maneuvering problems compared with alternative methods like stepwise initialization and driver model-based initialization.

The generalization in the use of virtualization in the upcoming generation of cellular networks involves new paradigms and approaches for their management. The correct sharing of the underlying resources between multiple virtualized network functions as well as any other processes sharing the same computational platform implies a complex architecture that makes virtualization challenging. As a result, numerous variables (e.g., computational capacity) were mostly ignored by previous management systems, but that can now lead to relevant impacts on the service performance. In this virtualized scenario with multiple coexistent processes over the same hardware, a “Noisy Neighbour” (NN) is identified as an entity that uses most of the underlying resources while other virtual units suffer a lack of them. While difficult to identify, such situations can affect the network service. In this context, the present work analyzes and assesses the NN problem for 5G Core scenarios. A complete emulated 5G network and analysis framework is defined and developed to evaluate the impact of a noisy entity. In this way, the degradation that Key Performance Indicators suffer in the network and by the end-users when a NN appears is assessed. Thus, the present work proposes a baseline for handling NN through a novel lifecycle management flow. For this purpose, it is evaluated the effectiveness of multiple Machine Learning (ML) models for the identification of NN, based on the metrics gathered from the proposed framework, achieving 99% of accuracy. Moreover, ML is applied to develop a method for network performance inference, along with a prediction model to forecast the number of CPU resources the network may demand at any given time supporting the proposed management flow.

Deduplication exploits the presence of similar data chunks to reduce storage overhead. Generalised deduplication (GD) uses transformation functions to split data into a basis (common to millions of chunks) and a deviation with respect to the basis. Doing so, it avoids computing additional hashes, comparing or differentiating against previously stored chunks. Minervafs is the first FUSE-based file system for GD. We implement and evaluate it using several real-world datasets, e.g., satellite images and virtual machine images, comparing against classical deduplication approaches (ZFS, SDFS), delta compression (xdelta) or compression (Gzip). Compared to ZFS, Minervafs achieves up to 63.53% (average of 27.38%) saving in storage usage and a speedup of 16% in read-heavy workloads. For VM images, MINERVAFS's data compression is on par with Gzip, while outperforming ZFS by severalfold. In contrast to ZFS’ growing RAM costs when more data is stored, MinervaFS’ RAM usage is independent from the amount of data stored, making it well suited to handle growing storage demands.

Autonomous vehicle functions in safety-critical situations show promise in reducing the risk and saving lives in accidents compared to existing safety systems. Consequently, it is from many perspectives advantageous to be able to quantify the potential benefits of new autonomous systems for vehicle maneuvers at-the-limit of tire friction. Here, to estimate the potential in terms of saved lives and reduced degree of injuries in accidents for new, not yet existing systems, a framework has been developed by combining available historic data, in the form of crash databases, and statistical methods with comparative calculations of vehicle behavior using numerical optimization rather than simulation. The framework performs effectively, it gives interesting insights into the relation between more traditional active yaw control and optimal autonomous lane-keeping control, and it clearly demonstrates the potential of saved lives by using autonomous vehicle maneuvers.

Vehicles generate a large amount of data from their internal sensors. This data is not only useful for a vehicle's proper operation, but it provides car manufacturers with the ability to optimize the performance of individual vehicles and companies with fleets of vehicles (e.g., trucks, taxis, tractors) to optimize their operations to reduce fuel costs and plan repairs. This paper proposes algorithms to compress CAN bus data, specifically, packaged as MDF4 files. In particular, we propose lightweight, online and configurable compression algorithms that allow limited devices to choose the amount of RAM and flash memory allocated to them. We show that our proposals can outperform LZW for the same RAM footprint, and can even deliver comparable or better performance to DEFLATE under the same RAM limitations.

This paper describes a decoupled sampling-based motion-planning method, based on the rapidly-exploring random tree (RRT) approach, that is applicable to autonomous vehicles, in order to perform different traffic maneuvers. This is a two-step motion-planning method including path-planning and motion timing steps, where both steps are sampling-based. In the path-planning part, an improved RRT method is defined that increases the smoothness of the path and decreases the computational time of the RRT method; it is called smooth RRT, SRRT. While some other RRT-based methods such as RRT can perform better in winding roads, in the problem of interest in this paper (which is performing some regular traffic maneuvers in usual urban roads and highways where the passage is not too winding), SRRT is more efficient since the computational time is less than for the other considered methods. In the motion timing or velocity-tuning step (VTS), a sampling-based method is introduced that guarantees collision avoidance between different vehicles. The proposed motion-timing algorithm can be very useful for collision avoidance and can be used with any other path-planning method. Simulation results show that because of the probabilistic property of the SRRT and VTS algorithms, together with the decoupling feature of the method, the algorithm works well for different traffic maneuvers.

In this paper, a cooperative fuel and comfort efficient control for autonomous vehicles is presented in order to perform different traffic maneuvers. The problem is formulated as an optimal control problem in which the cost function takes into account the fuel consumption and passengers comfort, subject to safety and speed constraints. The optimal solution takes into account the comfort and fuel consumption, which is obtained by minimizing a jerk, an acceleration, and a fuel criterion. It is shown that the method can be applied to control different groups of vehicles in different traffic scenarios. Simulation results are used to illustrate the generality property and performance of the proposed approach.

In order to obtain a realistic model of a complex system, thousands of possible residual generators need to be used for diagnosis. Based on engineering insights of the system to be monitored, certain algebraic and dynamic properties of the residual generators may be preferred, and therefore, a method for finding sequential residual generators is developed that accounts for these properties of the residual generator candidates. It is shown that only a small fraction of all residual generator candidates fulfill fundamental requirements, and thereby, proves the value of systematic methods. Furthermore, methods are devised for utilization of the residual generators, such as initialization of dynamic residual generators. A proposed method, considering the fault excitation in the residuals using the internal form of the residuals, significantly increases the diagnosis performance. A hybrid electric vehicle is used in a simulation study for demonstration, but the methods used are general in character and provides a basis when designing diagnosis systems for other complex systems.

Application-oriented programming of industrial robots is considered. The authors formulate the problems and suggest one approach towards a solution. The problem is motivated by some typical examples that are outlined. These examples show some requirements from different applications. Even a very mature application like spot welding needs special care to obtain best possible performance in the short moves between spots. In more complicated sensor-based applications the need is even greater. An architecture called the open robot control (ORC) architecture is proposed for this type of application-oriented programming. Solutions to the examples using ORC are discussed. A brief discussion of related works is given. Some implementation aspects are presented.>

Examples are given that modify some oversimplified statements usually made about the motion of robots in singular configurations. A robot may be controlled in an arbitrary direction from a singular configuration if the velocity profiles are shaped in a proper way. The observations on possible motions open a number of questions, since a wider class of motions is possible that at first sight. Hence, the results are of relevance to path programming, to path following, and to controller design.>

A simple servo controller built from components having neuronlike features is described. This VLSI servo controller uses pulses for control and is orders of magnitude smaller than a conventional system. The basic circuit elements are described. A key element is a component and neuronlike capability that takes voltages as inputs and generates a pulse train as the output. It is shown how the circuits are combined to a proportional and derivative controller. The advantages of using a pulsed output representation to improve slow-speed operation of a friction-limited system is demonstrated. The utility of exploiting parallelism, aggregation, and redundancy to improve system-level performance given imprecise low-level components is discussed. Experimental results illustrate the properties of the system compared with conventional controllers.

A secondary controller for online time scaling of nominal high-performance trajectories has been proposed to handle path following with torques close to the limits. Such nominal reference trajectories are typically available from an offline optimization. The stability properties of a closed-loop system including a robot, a primary controller, and a new secondary controller are studied. The analysis shows that if the nominal velocity profile is within certain limits, the result is a bounded actual velocity profile. The stability of the closed-loop system is then obtained by requiring a specified tracking performance for the primary controller.>

A design frame and a set of circuit elements with which generic motor controllers can be implemented are described. A simple proportional-derivative motor controller was embedded in the design frame. Its performance advantages over a more traditional controller are described. Also discussed are the merits of biologically inspired systems used in applications related to robotics.>

A feedback scheme for path following by trajectory time scaling is presented. The scheme is used in execution of fast trajectories along a geometric path, where the motion is limited by torque constraints. The time scaling is done by using a secondary controller that modifies a nominal trajectory during motion. The nominal, high-performance trajectory typically leads to torques that are at the limits, which leaves no control authority to compensate for modeling errors and disturbances. By modifying the time scaling of the nominal trajectory when the torques saturate, closed-loop action is possible. A key idea is that a scalar quantity (the path acceleration) is modified, resulting in coordinated adjustment of the individual joint motions. Two algorithms for online trajectory scaling are presented. One is based on online bounds on path acceleration, and one is designed to handle nominal minimum time trajectories. The functionality of the secondary controller is verified by simulations and experiments.>

A feedback scheme for path following by trajectory time scaling is presented. The scheme is used in execution of fast trajectories along a geometric path, where the motion is limited by torque constraints. The time scaling is done by using a secondary controller that modifies a nominal trajectory during motion. The nominal, high-performance trajectory typically leads to torques that are at the limits, hence leaving no control authority to compensate for modeling errors and disturbances. By modifying the time scaling of the nominal trajectory when the torques saturate, closed-loop action is possible. A key idea is that a scalar quantity, the path acceleration, is modified, resulting in coordinated adjustment of the individual joint motions. Two algorithms for online trajectory scaling are presented. One is based on online bounds on path acceleration, and one is designed to handle nominal minimum-time trajectories. The functionality of the secondary controller is verified by simulations and experiments.>