This paper presents the most important achievements in the field of electric power substation equipment's inspection by mobile robot. To accelerate the realization of unattended substation, which would improve the reliability, safety and intelligence of power substation, the field of automated power substation equipment's inspection has been greatly developed over the last decade. The paper addresses the power substation inspection robot, which is considered as one of the most active fields of research on mobile robot, from its history to design requirements, the robot system architecture analysis and modeling, reliability technology of the robot, autonomy and universality of substation equipment inspection. Besides, summaries and suggestions on the development and research trend of inspection robot, which should be addressed in the future, are given. The present work may serve as reference for initial design of robots for some complex and dangerous working environments. And some unique insights for the inspection robot provided in this paper may be of help to further research and application of power facilities inspection.

In this article, we presents an effective semantic grounding scheme to long-term mobile robots for dynamic object search in open/dynamic home environments. The challenge of this strategy lies in dealing with situations where the robot suffers the home environment with an unknown layout of the object and the target object is moved without the robot’s knowledge. Therefore, a general probabilistic model and a semantic model are established by investigating typical spatial location relations between object and room type, in order to guide the robot to prioritize the search effort for spaces that are most promising to find the target object. Furthermore, a semantic grounding solution is proposed to address the mutual independence between objects, endowing the robot with inference capability for dynamic object search. Also, our proposal can be shared with other robots that have not explored the environment in order to perform object search tasks. The presented scheme is fully evaluated by extensive experiments in the real-world environment. The results demonstrate the validity of our approach by comparing it with the other three schemes in terms of search time and trajectory length, and show that the proposed method allows the mobile robot to efficiently and robustly find the dynamic object while achieving the human-like performance.

In this article, an effective solution is presented for efficient robotic object search by leveraging metric-topological map. Based on the reference of objects, we construct a novel metric-topological map that is particularly suitable for object search, where the topological nodes represent the object-related locations in the environment. To effectively determine the creation of nodes on the map, a rapid adjustment method of the robot viewing angle is introduced. Compared with traditional maps, the map built in this article not only removes redundant nodes irrelevant to objects, but also facilitates the mobile robot to locate the target object. Besides, inspired by human search behavior, a search strategy is proposed by considering the tradeoff between the current position of the robot and the distance from the robot to the promising location of finding the target object, in order to further improve the search efficiency. The effectiveness and efficiency of our solution are verified through comparative experiments in both simulation and real-world environments. Results show that the presented approach always allows the mobile robot to find the target object efficiently and reliably while achieving humanlike search behavior.

The efficiency of sampling-based motion planning brings wide application in autonomous mobile robots. The conventional rapidly exploring random tree (RRT) algorithm and its variants have gained significant successes, but there are still challenges for the optimal motion planning of mobile robots in dynamic environments. In this paper, based on Bidirectional RRT and the use of an assisting metric (AM), we propose a novel motion planning algorithm, namely Bi-AM-RRT*. Different from the existing RRT-based methods, the AM with a larger connection distance is introduced in this paper to optimize the performance of robot motion planning in dynamic environments with obstacles. On this basis, the bidirectional search sampling strategy is employed to reduce the search time. Further, we present a new rewiring method to shorten path lengths. The effectiveness and efficiency of the proposed Bi-AM-RRT* are proved through comparative experiments in different environments. Experimental results show that the Bi-AM-RRT* algorithm can achieve better performance in terms of path length and search time, and always finds near-optimal paths with the shortest search time when the diffusion metric is used as the AM.

The capability of object search is a prerequisite for mobile robot to perform everyday tasks in the home environment. Due to the dynamic nature of object and the particularity of home environment, it still remains a challenging problem for mobile robot to find the dynamic target object with the low total cost. To address this problem, a priori knowledge-based approach for dynamic object search is presented in this paper. Inspired by human search process, the common sense knowledge about typical spatial location and the relationship between objects is modeled as a priori knowledge. On this basis, we propose a novel search strategy to improve the search efficiency of mobile robot at the scale of the entire home environment. Unlike the existing methods, a trade-off between the inferred spatial location knowledge and the distance of robot position and candidate room is considered, which permits robot to prioritize the search effort for the candidate room in which the target object is most likely to be found. Also, the search space inside the room is further reduced by using a priori knowledge-guided search heuristics. Meanwhile, the update of knowledge is also investigated to maintain its reliability. The proposed approach is implemented in both simulation and real environments, and evaluated through extensive experiments. The experimental results are provided to demonstrate the feasibility and efficiency of our proposal.