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.

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.

In this paper, we present a method of learning desired behaviour of the specific robotic system and transfer of the existing knowledge in the event of partial system failure. Six-legged robot (hexapod) built on top of the Bioloid platform is used for the method verification. We use genetic algorithms to optimize the hexapod's gait, after which we simulate physical damage caused to the robot. The goal of this method is to optimize the gait in accordance with the actual robot morphology, instead of the assumed one. Also, knowledge that was previously gained will be transferred in order to improve the results. Nonstandard genetic algorithm with the specific mixed population is used for this.

This paper presents an adaptive version of the path planning algorithm based on the recently proposed structure called bur of free configuration space. The original planning algorithm — rapidly exploring bur tree (RBT) is based on the multi-directional extension of tree nodes for efficient exploration of free configuration space. A suitable number of directions for extension (extension degree) was empirically determined and has been kept fixed during the algorithm run. This paper investigates the possibility to adapt the extension degree during the algorithm execution in order to further boost the efficiency of the path planner in terms of number of iterations and runtime. Validation study demonstrates that the proposed adaptive version of RBT algorithm (aRBT) outperforms the original algorithm.

This paper presents a fault-tolerant PD tracking system for Multirotor Aerial Vehicles (MAV) based on a novel Recursive Least Squares (RLS) Fault Detection and Isolation (FDI) algorithm utilized to diagnose propulsion system faults. As a test platform we investigate an octorotor model, including rigid body dynamics, the gyroscopic effect and motor dynamics. A hover configuration control is extended into an adaptive, fault-tolerant PD tracking controller. The approach is validated within a simulation study that includes a severe triple rotor fault scenario.

This paper presents a new approach to C-space exploration and optimal path planning for robotic manipulators and planar scenarios using the structure named bur of free C-space. This structure builds upon the so-called bubble, which is a local volume of free C-space, easily computed using the distance information in the workspace. It is previously shown how burs can be used to form a rapidly exploring bur tree (RBT): a space-filling tree that resembles RRT. Now, we exploit the burs of free C-space approach to develop a new algorithm called RBT*, which is, like RRT* algorithm, provably asymptotically optimal, i.e., such that the cost of the returned solution converges almost surely to the optimum. Burs of free space offer better performance and faster convergence because it enables faster exploration of free space.

This paper presents a novel method for using volumetric information in the C-space for generating collision-free paths for robotic manipulators in the presence of obstacles. The method is based on the RRT paradigm, i.e., incrementally building trees in C-space in order to connect the initial configuration to the final one. Bubbles of free C-space are used within a carefully modified extend procedure to add lengthy edges to the tree. In particular, we propose an expanded version of the bubble, which enables even more efficient exploration. The method is validated and compared to other planning algorithms within a simulation study that includes several types of manipulators and obstacle scenarios.

This paper presents a new approach to C-space exploration and path planning for robotic manipulators using the structure named bur of free C-space. This structure builds upon the so-called bubble, which is a local volume of free C-space, easily computed using the distance information in the workspace. We show how the same distance information can be used to compute the bur that can reach substantially beyond the boundary of the bubble. It is shown how burs can be used to form a rapidly exploring bur tree (RBT): a space-filling tree that resembles RRT. Such a structure can easily be used within a suitably tailored path planning algorithm. Simulation study shows how the RBT-based algorithm outperforms the classical RRT-based method.

This paper presents a novel approach to the control of articulated robots in unstructured environments. The proposed control ensures several properties. First, the controller guarantees the achievement of a goal position without getting stuck in local minima. Then, the controller makes the closed-loop system passive, which renders the approach attractive for applications where the robot needs to safely interact with humans. Finally, the control law is explicitly shaped by the safety measure – the danger field. The proposed control law has been implemented and validated in a realistic experimental scenario, demonstrating the effectiveness in driving the robot to a given configuration in a cluttered environment, without any offline planning phase. Furthermore, the passivity of the system enables the robot to easily accommodate external forces on the tool, when a physical contact between the robot and the environment is established.

In this paper, a planning algorithm for computing collision-free paths for robotic manipulators is described. The algorithm is based on incremental growth of tree-like structures that are formed by so-called bubbles, which represent volumes of free configuration space easily computed using the distance information in the workspace. The growth of the trees is guided by a criterion function that reflects several aspects including exploration of the configuration space, distance from the goal, size of the bubble, etc. The problem of learning, i.e., assigning the weights to specific criteria is tackled using evolutionary algorithm. Learning itself is aimed at minimizing the number of distance computations that represent the most of the computational burden. The performance of the resulting planer is validated within several scenarios that include 3-dof and 6-dof robotic manipulators.

This paper presents a synergistic approach to danger assessment and safety-oriented control of articulated robots that are based on a quantity called danger field. This quantity captures the state of the robot as a whole and indicates how dangerous the current posture and velocity of the robot are to the objects in the environment. The field itself is invariant with respect to objects around the robot and can be computed in any given point of the robot's workspace using measurements from the proprioceptive sensors. Furthermore, the danger field can be expressed in the closed form, which enables its fast computation. Apart from being a pure safety assessment, the danger field provides a natural prelude to safety-oriented control strategy. Namely, the information about the danger field can easily be fed back to shape standard control schemes in order to make the motion of the robot safer to the environment. The proposed method is validated through simulations and experiments.