Due to the vastly different energy consumption between up-slope and down-slope, a path with the shortest length in a complex off-road terrain environment (2.5D map) is not always the path with the least energy consumption. For any energy-sensitive vehicle, realizing a good trade-off between distance and energy consumption in 2.5D path planning is significantly meaningful. In this paper, we propose a deep reinforcement learning-based 2.5D multi-objective path planning method (DMOP). The DMOP can efficiently find the desired path in three steps: (1) transform the high-resolution 2.5D map into a small-size map, (2) use a trained deep Q network (DQN) to find the desired path on the small-size map, and (3) build the planned path to the original high-resolution map using a path-enhanced method. In addition, the hybrid exploration strategy and reward-shaping theory are applied to train the DQN. The reward function is constructed with the information of terrain, distance, and border. The simulation results show that the proposed method can finish the multi-objective 2.5D path planning task with significantly high efficiency and quality. Also, simulations prove that the method has powerful reasoning capability that enables it to perform arbitrary untrained planning tasks.

As a critical component of power battery, the quality of Nickel-plated punched steel strip (NPSS) is closely related to the performance of battery. In practice, however, the real-time detection for defects of NPSS with limited computating resources is a challenging task. Current researches on Vision-based strip defect detectors emphasize the use of large neural network models to pursue high accuracy, while ignoring the training cost and hardware requirements. To accelerate the model update and deployment cycles, and reduce the application cost, a lightweight detection network for accurate and fast detection of surface defects in NPSS is developed in this paper. First, a new lightweight backbone network, LEDNet, is designed to extract features. Depth dynamic convolution is used as the basic structure of the network to reduce the computational complexity while adaptively extracting effective features. Thereafter, a refine-residual bidirectional aggregation network is built to enhance the bidirectional propagation and reuse high-level and low-level features by further optimizing and adjusting the extracted features. On this basis, feature aggregation capability is improved. Finally, the intersection over union k-means algorithm is used to adjust the anchor box size, which balances the significant difference in aspect ratio of surface defects of NPSS. Experimental results on the NPSS defect data-set show that our network detection accuracy can reach 87.17%, the single-sheet detection rate is 0.09s, and the computational complexity is reduced. Compared with the state-of-the-art detectors, it achieves a strong competitive accuracy and low computational effort, which fully meets the accuracy and real-time requirements in the production environment of NPSS.

The path tracking is a basic function of the autonomous vehicle. Generally, it is difficult to realize the path tracking with high accuracy at high speeds. To solve this problem, an adaptivity-enhanced path tracking system (AEPTS) is investigated in this paper. The adaptive performance of this AEPTS is realized via three aspects. The first is to achieve preview distance adaptivity by a dynamic multi-point preview model (DMPPM), which provides the desired course deviation according to the vehicle speed and the reference path. The second is to improve the adaptivity of an adaptive fuzzy steering controller (AFSC) with an adaptive steering gain model which is built via the path curvature and vehicle speed. The third is that an optimal parameter surface is devised to enhance the adaptivity of the AEPTS at high speeds. Such a surface can provide the optimal coefficients for the AEPTS, and it is implemented using the particle swarm optimization (PSO) and a curve fitting method. At last, simulation experiments are given to verify the adaptivity of the proposed method.