Electrical distribution equipment inspection is crucial for the electric power industry. With the rapid increase in the number of electrical distribution rooms, an unattended inspection method, for example, autonomous inspection robot, is eagerly desired by the industry to make up for the deficiencies of traditional manual inspection in effectiveness and validity. Existing inspection robots designed for indoor substations are generally lack of practicality, due to the factors such as inspection requirements and robot weight. To bridge the gap between prototype and practicality, in this work, we design the first completely autonomous robotic system, LongSword, which provides a satisfying technical solution for equipment inspection with an optical zoom camera, a thermal imaging camera or a partial discharge detector. Firstly, we design a novel and flexible hardware architecture which allows the robot to move, lift, and rotate in the station to reach any desired position. Secondly, we develop an intelligent software framework which consists of several modules to achieve accurate equipment recognition and reliable failure diagnosis. Thirdly, we achieve an apposite integration of the existing technologies to implement an applicable robotic system that can fulfill the requirements of indoor equipment inspection. There are over 200 LongSwords currently serving about 160 electrical distribution rooms, some of which have been working for more than 1 year. The average precision of device status recognition is up to 99.70%, and the average inspection time of a single device is as short as 13.5 s. The feedback from workers shows that LongSword can significantly improve the efficiency and reliability of equipment inspection, which accelerates the process of setting up unmanned stations.

Path planning on a two-dimensional grid is a well-studied problem in robotics. It usually involves searching for a shortest path between two vertices on a grid given that some of the grid cells are impassable (occupied by obstacles). Single-source path planning finds shortest paths from a given source vertex to all other vertices of the grid. Singles-source path planning enhances robot autonomy by calculating multiple possible paths for various navigation scenarios when the destination state is unknown. A high-performance algorithm for single-source any-angle path planning on a grid called CWave is proposed here. Any-angle attribute implies that the algorithm calculates paths which can include line segments at any angle, as opposed to standard A* that runs on an 8-connected graph, which permits turns with 45° increments only. The key idea of CWave is to abandon the graph model and operate directly on the grid geometry using discrete geometric primitives (instead of individual vertices) to represent the wave front. In its most basic form (CWaveInt), CWave requires only integer arithmetics. CWaveInt, however, can accumulate the distance error at turning points. A modified version of CWave (CWaveFpuSrc) with minimal usage of floating-point calculations is also developed to eliminate any accumulative errors, which is proven mathematically and experimentally on several maps. The performance of the algorithm on most of the tested maps is demonstrated to be significantly faster than that of Theta*, Lazy Theta*, Field A*, ANYA, Block A*, and A* adapted for single-source planning (on maps with lower number of isolated obstacles, CWaveFpuSrc is 2−3 times faster than its fastest tested alternative Block A*). An N-threaded implementation (CWaveN) of CWave is presented and tested to demonstrate an improved performance (multithreaded implementation is 1.5−3 times faster than single-threaded CWave). The paper discusses foundations and experimental validation of CWave, and presents future work to address the limitations of the current implementations and obtain further performance enhancements.

This paper presents a vision-based path planning strategy that aims to reduce the computational time required by a robot to find a feasible path from a starting point to the goal point. The proposed algorithm presents a novel strategy that can be implemented on any well-known path planning algorithm such as A*, D* and probabilistic roadmap (PRM), to improve the swiftness of these algorithms. This path planning algorithm is suitable for real-time scenarios since it reduces the computational time compared to the basis and traditional algorithms. To test the proposed path planning strategy, a tracking control strategy is implemented on a mobile platform. This control strategy consists of three major stages. The first stage deals with gathering information about the surrounding environment using vision techniques. In the second stage, a free-obstacle path is generated using the proposed reduced scheme. In the final stage, a Lyapunov kinematic tracking controller and two Artificial Neural Network (ANN) based-controllers are implemented to track the proposed path by adjusting the rotational and linear velocity of the robot. The proposed path planning strategy is tested on a Pioneer P3-DX differential wheeled mobile robot and an Xtion PRO depth camera. Experimental results prove the efficiency of the proposed path planning scheme, which was able to reduce the computational time by a large percentage which reached up to 88% of the time needed by the basis and traditional scheme, without significant adverse effect on the workability of the basis algorithm. Moreover, the proposed path planning algorithm has improved the path efficiency, in terms of the path length and trackability, challenging the traditional trade-off between swiftness and path efficiency.

Visual simultaneous localization and mapping (visual SLAM) has been well developed in recent decades. To facilitate tasks such as path planning and exploration, traditional visual SLAM systems usually provide mobile robots with the geometric map, which overlooks the semantic information. To address this problem, inspired by the recent success of the deep neural network, we combine it with the visual SLAM system to conduct semantic mapping. Both the geometric and semantic information will be projected into the 3D space for generating a 3D semantic map. We also use an optical-flow-based method to deal with the moving objects such that our method is capable of working robustly in dynamic environments. We have performed our experiments in the public TUM dataset and our recorded office dataset. Experimental results demonstrate the feasibility and impressive performance of the proposed method.

Linear quadratic regulator (LQR), a popular technique for designing optimal state feedback controller, is used to derive a mapping between continuous and discrete time inverse optimal equivalence of proportional integral derivative (PID) control problem via dominant pole placement. The aim is to derive transformation of the LQR weighting matrix for fixed weighting factor, using the discrete algebraic Riccati equation (DARE) to design a discrete time optimal PID controller producing similar time response to its continuous time counterpart. Continuous time LQR-based PID controller can be transformed to discrete time by establishing a relation between the respective LQR weighting matrices that will produce similar closed loop response, independent of the chosen sampling time. Simulation examples of first/second order and first-order integrating processes exhibiting stable/unstable and marginally stable open loop dynamics are provided, using the transformation of LQR weights. Time responses for set-point and disturbance inputs are compared for different sampling times as fraction of the desired closed loop time constant.

This paper investigates the vibrations of hexarot simulators. The generalized modeling of kinematics and dynamics formulation of a hexarot mechanism is addressed. This model considers the flexible manipulator with the base motion. The dynamic formulation has been developed based on the principle of virtual work. The dynamic model consists of the stiffness of the different parts of the mechanism, the effects of gravity and inertia, torque and force related to the joints viscous friction. Finally, the response of the end effector at various frequencies has been presented, and the vibrations of the mechanism and the dynamic stability index have been investigated.

This paper investigates a battery snap-in operation performed by an industrial robot. The snap-fit phenomenon is experienced during the insertion of batteries into the battery holder. In order to understand the nature of the snap-fit phenomenon, the large displacement but small deformation theory is used to model the insertion operation. The stability of equilibrium points is analysed in order to determine the so-called trip point. The purpose of the investigation is to augment the displacement control of the robot with force feedback. A microcontroller-based measurement system is implemented to provide the force feedback. The proposed method is valid for a class of snap-fit problems not only for the investigated specific one.

The demand for rehabilitation services has increased in recent years due to population aging. Due to the limitations of therapist’s time and healthcare resources, robot-assisted rehabilitation is becoming an appealing, powerful, and economical solution. In this paper, we propose a solution that combines Learning from Demonstration (LfD) and robotic rehabilitation to save the therapist’s time and reduce the therapy costs when the therapy involves periodic or semi-periodic motions.We begin by modeling the therapist’s behavior (a periodic or semi-periodic motion) using a Fourier Series (FS). Later, when the therapist is no longer involved, the system reproduces the learned behavior modeled by the FS using a robot. A second goal is to combine the above with Gaussian Mixture Model (GMM) and Gaussian Mixture Regression (GMR) to obtain a more flexible and generalizable reproduction of the therapist’s behavior. This algorithm allows learning and imitating repetitive movement tasks. Our experimental results show the application of these algorithms to repetitive motion task.

Conventional simultaneous localization and mapping (SLAM) has concentrated on two-dimensional (2D) map building. To adapt it to urgent search and rescue (SAR) environments, it is necessary to combine the fast and simple global 2D SLAM and three-dimensional (3D) objects of interest (OOIs) local sub-maps. The main novelty of the present work is a method for 3D OOI reconstruction based on a 2D map, thereby retaining the fast performances of the latter. A theory is established that is adapted to a SAR environment, including the object identification, exploration area coverage (AC), and loop closure detection of revisited spots. Proposed for the first is image optical flow calculation with a 2D/3D fusion method and RGB-D (red, green, blue + depth) transformation based on Joblove–Greenberg mathematics and OpenCV processing. The mathematical theories of optical flow calculation and wavelet transformation are used for the first time to solve the robotic SAR SLAM problem. The present contributions indicate two aspects: (i) mobile robots depend on planar distance estimation to build 2D maps quickly and to provide SAR exploration AC; (ii) 3D OOIs are reconstructed using the proposed innovative methods of RGB-D iterative closest points (RGB-ICPs) and 2D/3D principle of wavelet transformation. Different mobile robots are used to conduct indoor and outdoor SAR SLAM. Both the SLAM and the SAR OOIs detection are implemented by simulations and ground-truth experiments, which provide strong evidence for the proposed 2D/3D reconstruction SAR SLAM approaches adapted to post-disaster environments.