Purpose
The results of obstacle avoidance path planning for the manipulator using artificial potential field (APF) method contain a large number of path nodes, which reduce the efficiency of manipulators. This paper aims to propose a new intelligent obstacle avoidance path planning method for picking robot to improve the efficiency of manipulators.
Design/methodology/approach
To improve the efficiency of the robot, this paper proposes a new intelligent obstacle avoidance path planning method for picking robot. In this method, we present a snake-tongue algorithm based on slope-type potential field and combine the snake-tongue algorithm with genetic algorithm (GA) and reinforcement learning (RL) to reduce the path length and the number of path nodes in the path planning results.
Findings
Simulation experiments were conducted with tomato string picking manipulator. The results showed that the path length is reduced from 4.1 to 2.979 m, the number of nodes is reduced from 31 to 3 and the working time of the robot is reduced from 87.35 to 37.12 s, after APF method combined with GA and RL.
Originality/value
This paper proposes a new improved method of APF, and combines it with GA and RL. The experimental results show that the new intelligent obstacle avoidance path planning method proposed in this paper is beneficial to improve the efficiency of the robotic arm.
Graphical abstract
Figure 1 According to principles of bionics, we propose a new path search method, snake-tongue algorithm, based on a slope-type potential field. At the same time, we use genetic algorithm to strengthen the ability of the artificial potential field method for path searching, so that it can complete the path searching in a variety of complex obstacle distribution situations with shorter path searching results. Reinforcement learning is used to reduce the number of path nodes, which is good for improving the efficiency of robot work. The use of genetic algorithm and reinforcement learning lays the foundation for intelligent control.
In order to eliminate the impact of the illumination on the tomato image segmentation, the paper adopts a new method. The method is based on illumination irrelevant image, and uses minimum entropy criterion to calculate the illumination irrelevant angle of the given camera. We preprocess the colorful tomato image by the efficient median filter method, and obtain the illumination irrelevant image according to the formative principle of images. Then we segment the illumination irrelevant images using the improved Ostu segmentation method, and compare with the result of the segmentation images based on the chromatic aberration method. We also adopt the statistical threshold method in HIS color space to segment tomato images and study the influence of illumination. The experiment shows that the illumination irrelevant method can eliminate the influence of illumination effectively, and segments the objects accurately with the distinguished features.
An underground heat storage system in a double-film-covered greenhouse and an adjacent greenhouse without the heat storage system were designed on the basis of plant physiology to reduce the energy consumption in greenhouses. The results indicated that the floor temperature was respectively 5.2 °C, 4.6 °C and 2.0 °C higher than that of the soil in the adjacent reference greenhouse after heat storage in a clear, cloudy and overcast sky in winter. Results showed that the temperature and humidity were feasible for plant growth in the heat saving greenhouse.
scite is a Brooklyn-based organization that helps researchers better discover and understand research articles through Smart Citations–citations that display the context of the citation and describe whether the article provides supporting or contrasting evidence. scite is used by students and researchers from around the world and is funded in part by the National Science Foundation and the National Institute on Drug Abuse of the National Institutes of Health.