Intelligent obstacle avoidance path planning method for picking manipulator combined with artificial potential field method

Abstract

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.

Evolutionary Computation Techniques for Path Planning Problems in Industrial Robotics: A State-of-the-Art Review

“…The number of recent works, specifically from 2022, on the problem of trajectory planning using GAs, shows that the area of evolutionary algorithms in industrial robotics still has great research potential (as we can see in Table 3). One of these recent articles [103] focused on the intelligent obstacle avoidance path planning method for the tomato-picking manipulator. A GA determined the parameters of the Artificial Potential Field (APF) method, and subsequent path planning was performed using the APF method in conjunction with reinforcement learning (RL).…”

Robot Learning by Demonstration with Dynamic Parameterization of the Orientation: An Application to Agricultural Activities

“…Other approaches able to generate complex and collision-free movements are grounded on (i) grid-based [8] or interval-based [9] search methods, which find optimal obstaclefree paths for both the manipulator and mobile base; (ii) reward-based algorithms, which require the robot to try different paths, whereby it will be rewarded positively or negatively if it is successful or not [10,11]; (iii) artificial potential-fields algorithms, which generate attractive or repulsive paths for the manipulator joints and mobile base [12,13]; and (iv) sampling-based algorithms, which find an optimal path from roadmaps [14] or probabilistic methods [15,16].…”

Three-dimensional continuous picking path planning based on ant colony optimization algorithm

“…To solve this problem, researchers have proposed three types of planning algorithms: traditional algorithms, meta-heuristic algorithms, and deep learning. Among the traditional algorithms are the artificial potential field method(APF) [ 2 ], rapidly exploring random tree (RRT) [ 3 ], etc. Meta-heuristic algorithms include the ant colony algorithm [ 4 ], particle swarm algorithm [ 5 ], and gray wolf algorithm [ 6 ].…”