Research on obstacle avoidance gait planning of quadruped crawling robot based on slope terrain recognition

Abstract

Purpose Aiming at the problem that quadruped crawling robot is easy to collide and overturn when facing obstacles and bulges in the process of complex slope movement, this paper aims to propose an obstacle avoidance gait planning of quadruped crawling robot based on slope terrain recognition. Design/methodology/approach First, considering the problem of low uniformity of feature points in terrain recognition images under complex slopes, which leads to too long feature point extraction time, an improved ORB (Oriented FAST and Rotated BRIEF) feature point extraction method is proposed; second, when the robot avoids obstacles or climbs over bumps, aiming at the problem that the robustness of a single step cannot satisfy the above two motions at the same time, the crawling gait is planned according to the complex slope terrain, and a robot obstacle avoidance gait planning based on the artificial potential field method is proposed. Finally, the slope walking experiment is carried out in the Robot Operating System. Findings The proposed method provides a solution for the efficient walking of robot under slope. The experimental results show that the extraction time of the improved ORB extraction algorithm is 12.61% less than the original ORB extraction algorithm. The vibration amplitude of the robot’s centroid motion curve is significantly reduced, and the contact force is reduced by 7.76%. The time it takes for the foot contact force to stabilize has been shortened by 0.25 s. This fact is verified by simulation and test. Originality/value The method proposed in this paper uses the improved feature point recognition algorithm and obstacle avoidance gait planning to realize the efficient walking of quadruped crawling robot on the slope. The walking stability of quadruped crawling robot is tested by prototype.

Research on DEM geomorphic factor terrain recognition algorithm using probabilistic neural networks based on tactile systems

“…Machine learning is characterized by high accuracy, speed, automation, and scale when dealing with large-scale data, while each neural network has its applicable scenarios and limitations (Wang et al, 2022). PNN has a faster training speed and nonlinear approximation capability compared with BP and ELM, and its outputs can be can be interpreted as probabilistic values, which provide a more intuitive interpretation of the results; the accuracy results of terrain recognition using DGFTRA-PNN, BP, and ELM are listed in Table 2.…”

“…Terrain recognition is a fundamental function of autonomous ground robots, which is very important for gait planning and automatic gait adjustment of autonomous robots (Liu et al, 2020; Wang et al, 2022a), and has attracted a large number of scholars to study robotic terrain recognition methods. The study of robotic terrain can help its application in hazardous scenarios such as wilderness exploration, mine exploration, and arc fault detection (Tian et al, 2023).…”

Research on the Construction of Intelligent Robot Path Recognition System Supported by Deep Learning Network Algorithm

“…Simulation results confirm the algorithm's feasibility. Literature [8] proposes an improved method for extracting ORB feature points and planning robot obstacle avoidance gait based on the artificial potential field method. Finally, simulation experiments are carried out, and the research results show that this method shortens the practice of ORB extraction of feature points by 12.61% compared with the previous method, the amplitude of the robot's zigzag motion curve is significantly reduced, and the foot touch time is shortened by 0.25 S. The performance of this method is better than that of the traditional method.…”