In order to achieve collision-free path planning in complex environment, Munchausen deep Q-learning network (M-DQN) is applied to mobile robot to learn the best decision. On the basis of Soft-DQN, M-DQN adds the scaled log-policy to the immediate reward. The method allows agent to do more exploration. However, the M-DQN algorithm has the problem of slow convergence. A new and improved M-DQN algorithm (DM-DQN) is proposed in the paper to address the problem. First, its network structure was improved on the basis of M-DQN by decomposing the network structure into a value function and an advantage function, thus decoupling action selection and action evaluation and speeding up its convergence, giving it better generalization performance and enabling it to learn the best decision faster. Second, to address the problem of the robot’s trajectory being too close to the edge of the obstacle, a method of using an artificial potential field to set a reward function is proposed to drive the robot’s trajectory away from the vicinity of the obstacle. The result of simulation experiment shows that the method learns more efficiently and converges faster than DQN, Dueling DQN and M-DQN in both static and dynamic environments, and is able to plan collision-free paths away from obstacles.

Unmanned Ariel Vehicles (UAVs) are tasked to collect sensory data which are typically retrieved after the flight. The emergence of 5G and Device-to-Device (D2D) networks enables high speed network communication for UAVs to transfer data during a flight mission instead of post flight. UAVs are now subject to constraints of area coverage, battery capacity and network quality of service, making their path planning more challenging. In this paper, we formulate the issue as a combinatorial optimization problem which minimizes the flight cost of multiple UAVs covering the entire area. We show this problem is NP-hard, therefore we propose a Particle Swarm Optimization heuristic along with path encoding and local search techniques to solve the problem. Our numerical simulations demonstrate the effectiveness of the approach and how the size of the area and D2D link affect the number of UAVs needed and their flight time.

Owing to the satisfactory disturbance estimation and compensation capacity, active disturbance rejection control (ADRC) is promising for permanent magnet synchronous motor (PMSM) speed regulation system. Unfortunately, the control performance of ADRC strategy is generally affected by the measurement noise introduced by position sensors. To address this tough issue, an augmented nonlinear extended state observer (ANESO) is constructed in this paper to directly estimate the motor speed. The bode diagrams of the ANESO-based measurement noise suppression approach acquired by the frequency-sweep method are illustrated to show its frequency domain characteristics. Utilizing the advantages of nonlinear control and augmented observer technique, the proposed scheme can obtain satisfactory measurement noise suppression performance without sacrificing the robustness of the PMSM system. Experimental verifications are conducted to verify the superior properties of the proposed measurement noise suppression strategy.

Automatic detection of workers wearing safety helmets at the construction site is essential for safe production. Aiming at the problem of low recognition rate caused by factors such as background and light in the automatic detection of safety helmets using traditional machine learning methods, this paper proposes an object detection framework that combines Online Hard Example Mining (OHEM) and multi-part combination. In our framework, we first use the multi-scale training and the increasing anchors strategies to enhance the robustness of the original Faster RCNN algorithm to detect different scales and small object. Then, the OHEM is to optimize the model to prevent the imbalance of positive and negative samples. Finally, the person wearing the helmet and its parts (helmet and person) are detected by improved Faster RCNN. The multi-part combination method uses the geometric information of the detection objects to determine if a worker is wearing a helmet. Experiments show that compared with the original Faster RCNN, the detection accuracy is increased by 7%. It also has better detection performance for partial occlusion and different-size objects, showing good generalization and robustness.

There are some representative reports in industrial safety engineering, such as the Hazard and Operability Analysis and Pre-Hazard Analysis; however, a large amount of industrial safety knowledge in the report has not been fully explored. In order to reuse and release the value of industrial safety knowledge, this paper constructs a new industrial safety knowledge extraction framework. The framework combines the asset management shell to summarize the knowledge concept entities of machine description language and model description language. According to the safety report template, the framework also constructs a new industrial safety knowledge-mapping standard structure. Specifically, firstly, considering that the knowledge structure of safety reports is different in different processes of the process industry, this paper innovatively proposes a general industrial safety knowledge-mapping standard structure, which provides a practical solution for the integration of industrial knowledge representation problems in different processes. Secondly, based on the research progress of named entities, this paper presents an industrial named entity extraction method (INERM) for the process industry. This method designs an entity weight model to calculate the entity weight of each sentence, and adds part-of-speech weight to improve the entity extraction algorithm, which alleviates the problem that the existing entity extraction methods cannot reasonably use the semantic information and context of word. Finally, we construct a triple of industrial safety knowledge based on the rules and store it in Neo4j. In this paper, four semantic-type templates and five semantic relation templates are constructed based on the new industrial safety knowledge map standardization construction process of the process industry. The comparative experiments show that the accuracy of the INERM on the test set is improved by 17 percentage points on average compared with other key entity extraction algorithms. A total of 1329 entities are constructed in the directional application example of the fluid transportation process, which provides a large number of references for the safety of the fluid transportation process and is more conducive to improving the safety guarantee of the fluid transport process.