Closed-loop validation of autonomous vehicles is an open problem, significantly influencing development and adoption of this technology. The main contribution of this paper is a novel approach to reproducible, scenario-based validation that decouples the problem into several sub-problems, while avoiding to brake the crucial couplings. First, a realistic scenario is generated from the real urban traffic. Second, human participants, drive in a virtual scenario (in a driving simulator), based on the real traffic. Third, human and automated driving trajectories are reproduced and compared in the real vehicle on an empty track without traffic. Thus, benefits of automation with respect to safety, efficiency and comfort can be clearly benchmarked in a reproducible manner. Presented approach is used to benchmark performance of SBOMP planner in one scenario and validate SuperHuman driving performance.

Enabling humans and robots to safely work close to each other deserves careful consideration. With the publication of ISO/TS 15066 directives on this matter, two different strategies, namely the Speed and Separation Monitoring and the Power and Force Limiting, have been proposed. This letter proposes a method to efficiently combine the two aforementioned safety strategies for collaborative robotics operations. By exploiting the combination of the two, it is then possible to achieve higher levels of productivity, while still preserving safety of the human operators. This is achieved by the optimal scaling of the initially prescribed velocity, while preserving the path consistency of the robot trajectory. In a nutshell, the state of motion of each point of the robot is monitored so that at every time instant the robot is able to modulate its speed to eventually come into contact with a body region of the human, consistently with the corresponding biomechanical limit. Validation experiments have been conducted to establish that the proposed method enables substantially less stringent limits on robot performance while still allowing for the safety limits prescribed by ISO directives.

In this paper, we approach the problem of ensuring safety requirements within human-robot collaborative scenarios. The safety requirements considered herein are consistent with the paradigm of speed and separation monitoring. In such a setup, safety guarantees for human operators usually imply limited robot velocities and/or significant distance margins, which in turn may have adverse effects regarding the productivity of the robot. In this paper, we propose a novel approach that minimally affects the productivity while being consistent with such a safety prescription. A comprehensive simulation study shows that our method outperforms the current state of the art algorithm.

The reliability of the operations of the high-voltage circuit breaker is the key to the stable power system, so it’s fault diagnosis and condition assessment it is of great significance. Considering that high-voltage circuit breaker vibration fingerprints contain valuable information about its mechanical integrity and that the vibration measurements are non-invasive, this paper presents the algorithm for the analysis of residual life of a high-voltage circuit breaker. The algorithm is based on the variational mode decomposition (VMD) and Shannon information entropy mean (EM) in order to obtain indices that are used as an indicator of the circuit breaker structural deterioration.

We present a simple method to quickly explore C-spaces of robotic manipulators and thus facilitate path planning. The method is based on a novel geometrical structure called generalized bur. It is a star-like tree, rooted at a given point in free C-space, with an arbitrary number of guaranteed collision-free edges computed using distance information from the workspace and simple forward kinematics. Generalized bur captures large portions of free C-space, enabling accelerated exploration. The workspace is assumed to be decomposable into a finite set of (possibly overlapping) convex obstacles. When plugged in a suitable RRT-like planning algorithm, generalized burs enable significant performance improvements, while at the same time enabling exact collision-free paths.

In this paper, a novel global optimization algorithm – Wingsuit Flying Search (WFS) is introduced. It is inspired by the popular extreme sport – wingsuit flying. The algorithm mimics the intention of a flier to land at the lowest possible point of the Earth surface within their range, i.e., a global minimum of the search space. This is achieved by probing the search space at each iteration with a carefully picked population of points. Iterative update of the population corresponds to the flier progressively getting a sharper image of the surface, thus shifting the focus to lower regions. The algorithm is described in detail, including the mathematical background and the pseudocode. It is validated using a variety of classical and CEC 2020 benchmark functions under a number of search space dimensionalities. The validation includes the comparison of WFS to several nature-inspired popular metaheuristic algorithms, including the winners of CEC 2017 competition. The numerical results indicate that WFS algorithm provides considerable performance improvements (mean solution values, standard deviation of solution values, runtime and convergence rate) with respect to other methods. The main advantages of this algorithm are that it is practically parameter-free, apart from the population size and maximal number of iterations. Moreover, it is considerably “lean” and easy to implement.

Planning and Learning are complementary approaches. Planning relies on deliberative reasoning about the current state and sequence of future reachable states to solve the problem. Learning, on the other hand, is focused on improving system performance based on experience or available data. Learning to improve the performance of planning based on experience in similar, previously solved problems, is ongoing research. One approach is to learn Value function (cost-to-go) which can be used as heuristics for speeding up search-based planning. Existing approaches in this direction use the results of the previous search for learning the heuristics. In this work, we present a search-inspired approach of systematic model exploration for the learning of the value function which does not stop when a plan is available but rather prolongs search such that not only resulting optimal path is used but also extended region around the optimal path. This, in turn, improves both the efficiency and robustness of successive planning. Additionally, the effect of losing admissibility by using ML heuristic is managed by bounding ML with other admissible heuristics.

This paper presents preliminary work on learning the search heuristic for the optimal motion planning for automated driving in urban traffic. Previous work considered search-based optimal motion planning framework (SBOMP) that utilized numerical or model-based heuristics that did not consider dynamic obstacles. Optimal solution was still guaranteed since dynamic obstacles can only increase the cost. However, significant variations in the search efficiency are observed depending whether dynamic obstacles are present or not. This paper introduces machine learning (ML) based heuristic that takes into account dynamic obstacles, thus adding to the performance consistency for achieving real-time implementation.

This paper presents a framework for fast and robust motion planning designed to facilitate automated driving. The framework allows for real-time computation even for horizons of several hundred meters and thus enabling automated driving in urban conditions. This is achieved through several features. Firstly, a convenient geometrical representation of both the search space and driving constraints enables the use of classical path planning approach. Thus, a wide variety of constraints can be tackled simultaneously (other vehicles, traffic lights, etc.). Secondly, an exact cost-to-go map, obtained by solving a relaxed problem, is then used by A*-based algorithm with model predictive flavour in order to compute the optimal motion trajectory. The algorithm takes into account both distance and time horizons. The approach is validated within a simulation study with realistic traffic scenarios. We demonstrate the capability of the algorithm to devise plans both in fast and slow driving conditions, even when full stop is required.