Heuristic search is often used for motion planning and pathfinding problems, for finding the shortest path in a graph while also promising completeness and optimal efficiency. The drawback is it's space complexity, specifically storing all expanded child nodes in memory and sorting large lists of active nodes, which can be a problem in real-time scenarios with limited on-board computation. To combat this, we present the Search with Learned Optimal Pruning-based Expansion (SLOPE), which, learns the distance of a node from a possible optimal path, unlike other approaches that learn a cost-to-go value. The unfavored nodes are then pruned according to the said distance, which in turn reduces the size of the open list. This ensures that the search explores only the region close to optimal paths while lowering memory and computational costs. Unlike traditional learning methods, our approach is orthogonal to estimating cost-to-go heuristics, offering a complementary strategy for improving search efficiency. We demonstrate the effectiveness of our approach evaluating it as a standalone search method and in conjunction with learned heuristic functions, achieving comparable-or-better node expansion metrics, while lowering the number of child nodes in the open list. Our code is available at https://github.com/dbokan1/SLOPE.

This paper presents an effective approach to enable performance improvement in human-robot collaboration scenarios. The problem is tackled from the perspective of speed and separation monitoring principle, which stems from the recently instituted safety standard. The proposed approach attempts to seek for performance gains, measured by the speed-up of the production cycle, without compromising the safety constraints consistent with the standard. The approach is based on the notion of braking surface - an abstraction of the swept volume described by the manipulator during braking motion. We address two types of braking behavior: general and path-consistent. In both cases, the braking surface can be evaluated in a receding horizon manner. The robot velocity is continuously scaled such that, in case of a controlled stop, the corresponding volume spanned by the robot (braking surface) does not interfere with the surrounding obstacles. The approach is entirely kinematic and does not require the knowledge of the robot's dynamic model. Simulation study indicates that the pro-posed approach offers performance improvements compared to other state of the art methods. Moreover, the experiments demonstrate the real-time applicability of the method with the real robot in human-shared environment.

This article presents two approaches to power circuit breakers condition assessment. The first one covers a wide variety of machine learning classification algorithms where the input for the classification is a manually selected feature set. The second one utilizes deep learning classification based on the convolutional neural network. Both approaches revolve around the idea behind spectral kurtosis, one of which exploits its visual representation in the form of kurtogram. The first approach uses a spectral kurtosis curve as the base for feature extraction while the second approach uses a spectral kurtosis kurtogram as a single input into the convolutional neural network. The validation is performed on a large set of vibration signatures and compared to competing state-of-the-art algorithms. The results indicate promising features of the proposed approach.

This paper presents KF-RRT algorithm: a novel approach to path planning for robotic manipulators in dynamic environments. It is based on a modified RRT algorithm combined with Kalman filtering technique. RRT modification implies two aspects. The first one is related to continuous update of struc-ture/ordering within the tree to accommodate for online execution of the algorithm. The second one relies on forest-based replanning by combining connected components. On the other hand, Kalman filter is used to track/predict the motion of obstacles. Virtually augmented obstacles influence the growth of trees, which yields the improved safety margin of the resulting motion. KF-RRT is validated within a simulation study, where it is compared to comneting algorithms,

This paper provides an overview of the influential parameters for the power circuit breaker condition assessment based on the vibration fingerprint. By creating the feature subsets based on the domain of computation originating from the vibration fingerprint, the features are firstly ranked by four features ranking algorithms. To confirm the ranked feature contribution to the classification performance, 11 different machine learning classifiers are trained. The training of the classifier is performed on the complete feature set where afterward the same classifiers are trained with the subset of the features ordered by the ranking algorithms. The ranking and the classifier performance yield the concept of kurtosis in the time and frequency domain as a highly promising feature for binary classification which credibly reflects the circuit breaker's mechanical condition.

This paper deals with safe human-robot collaboration in the context of speed and separation monitoring paradigm. The core of the approach is to continuously track the separation distance between the robot and the human. The robot speed is then adjusted according to the perceived distance so that it will be able to stop before eventually come into contact with the human. We present an approach that aims at maximizing the productivity of the robot, i.e., its speed, while keeping the prescribed safety requirements satisfied. The method is based on explicit representation of danger zones – regions around the robot, where safety requirements are violated. The motion is then generated such that the robot moves as fast as possible, while its danger zone still does not collide with human operators. The approach is validated within an experimental study. Note to Practitioners—This article was motivated by the problem of maximizing productivity of the robotic manipulator while ensuring the safety of human collaborator. The increase in productivity is achieved by a faster traversal of predefined paths without compromising the safety of the human, which is specifically defined by industrial standard. The approach requires limited knowledge on robot’s dynamical properties. More precisely, we only need the braking time as a “lumped” representation of robot’s inertia. The underlying optimization problem is conveniently resolved by introducing danger zones that allow for intuitive visualization and geometrical representation of the regions around the robot that must be avoided. On the other hand, the method assumes the representation of humans via typical geometric primitives, which can be obtained using of-the-shelf depth perception systems. The solution to the problem reduces to a repeated collision checking between danger zones and the human. Such an approach turns out to be suitable for real-time implementation due to availability of fast and efficient collision checking algorithms/libraries.

This book provides a solution to the control and motion planning design for an octocopter system. It includes a particular choice of control and motion planning algorithms which is based on the authors' previous research work, so it can be used as a reference design guidance for students, researchers as well as autonomous vehicles hobbyists. The control is constructed based on a fault tolerant approach aiming to increase the chances of the system to detect and isolate a potential failure in order to produce feasible control signals to the remaining active motors. The used motion planning algorithm is risk-aware by means that it takes into account the constraints related to the fault-dependant and mission-related maneuverability analysis of the octocopter system during the planning stage. Such a planner generates only those reference trajectories along which the octocopter system would be safe and capable of good tracking in case of a single motor fault and of majority of double motor fault scenarios. The control and motion planning algorithms presented in the book aim to increase the overall reliability of the system for completing the mission.

Abstract The importance of the high voltage circuit breaker for the power system’s safe and reliable operation is paramount. This research aims to analyse and provide the most significant high voltage circuit breaker health state indices based on the non-invasive vibration fingerprint measurement method. Results obtained and presented in this paper are validated on the data set acquired from the vacuum circuit breaker.

In this paper, we present a novel algorithm – DRGBT (Dynamic Rapidly-exploring Generalized Bur Tree), intended for motion planning in dynamic environments. The main idea behind DRGBT lies in a so-called adaptive horizon, consisting of a set of prospective target nodes that belong to a predefined $\mathcal{C}$-space path, which originates from the current node. Each node is assigned a weight that depends on relative distances and captured changes in the environment. The algorithm continuously uses a suitable horizon assessment to decide when to trigger the replanning procedure. A comprehensive simulation study is performed, covering a variety of manipulators, where DRGBT is compared to a state-of-the-art algorithm. Results indicate some promising features of the proposed method.

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.