A snake robot is a type of highly redundant mobile robot that significantly differs from a tracked robot, wheeled robot and legged robot. To address the issue of a snake robot performing self-localization in the application environment without assistant orientation, an autonomous navigation method is proposed based on the snake robot’s motion characteristic constraints. The method realized the autonomous navigation of the snake robot with non-nodes and an external assistant using its own Micro-Electromechanical-Systems (MEMS) Inertial-Measurement-Unit (IMU). First, it studies the snake robot’s motion characteristics, builds the kinematics model, and then analyses the motion constraint characteristics and motion error propagation properties. Second, it explores the snake robot’s navigation layout, proposes a constraint criterion and the fixed relationship, and makes zero-state constraints based on the motion features and control modes of a snake robot. Finally, it realizes autonomous navigation positioning based on the Extended-Kalman-Filter (EKF) position estimation method under the constraints of its motion characteristics. With the self-developed snake robot, the test verifies the proposed method, and the position error is less than 5% of Total-Traveled-Distance (TDD). In a short-distance environment, this method is able to meet the requirements of a snake robot in order to perform autonomous navigation and positioning in traditional applications and can be extended to other familiar multi-link robots.

As the principal factor affecting global food production, accurate identification of agricultural pests and diseases is crucial in ensuring a sustainable food supply. However, existing methods lack sufficient performance in terms of accuracy and real-time detection of multiple pests and diseases. Accordingly, accurate, efficient, and real-time identification of a wide range of pests and diseases is challenging. To address this, we propose an MCD-Yolov5 with a fusion design that combines multi-layer feature fusion (MLFF), convolutional block attention module CBAM, and detection transformer (DETF). In this model, we optimize the MLFF design to realize the dynamic adjustment of feature weights of the input feature layer to (1) find an appropriate distribution of feature information proportion for the detection task, (2) enhance detection speed by efficiently extracting effective images and effective features through CBAM, and (3) improve feature extraction capability through DETF to compensate for the accuracy problem of multiple pest detection. In addition, we established an unmanned aerial vehicle system (UAV) for crop pest and disease detection to assist in detection and prevention. We validate the performance of the proposed method through an established UAV platform, and five indicators are employed to quantify the performance. MCD-Yolov5 can detect pests and diseases with a large improvement in detection accuracy and detection efficiency, obtaining an 88.12% accuracy. The proposed method and system provide an idea for the effective identification of pests and diseases.

High-precision relative positioning and navigation is a fundamental requirement for many applications such as flight formation, spacecraft docking and collision avoidance. The main purpose of this paper is to develop a robust multi-model estimation algorithm for reliable navigation when there are abnormities of measurement and motion. In order to deal with these abnormities, we propose a quantitative evaluation method of relative navigation system by introducing the degree of observability (DoO) and the degree of abnormity (DoA). In addition, we design a feedforward information fusion and a feedback information allocation method based on DoO and DoA, and thus form a multi-model robust estimation algorithm. In order to testify the effectiveness and robustness of the proposed algorithm, a practical experiment with real data sets gathered in urban areas has been carried out. The results showed that the maximum relative positioning RMSE reduction ratio can reach 75%, and the maximum relative velocity RMSE reduction ratio can reach 51% compared with EKF. Therefore, the proposed method can guarantee the accuracy and robustness of relative navigation under abnormal conditions.