Collision-Free Motion Planning of a Six-Link Manipulator Used in a Citrus Picking Robot

Abstract

This paper presents the results of a motion planning algorithm that has been used in an intelligent citrus-picking robot consisting of a six-link manipulator. The real-time performance of a motion planning algorithm is urgently required by the picking robot. Within the artificial potential field (APF) method, the motion planning of the picking manipulator was basically solved. However, the real-time requirement of the picking robot had not been totally satisfied by APF because of some native defects, such as the large number of calculations used to map forces into torques by the Jacobian matrix, local minimum trap, and target not reachable problem, which greatly reduce motion planning efficiency and real-time performance of citrus-picking robots. To circumvent those problems, this paper proposed some novel methods that improved the mathematical models of APF and directly calculates the attractive torques in the joint space. By using the latter approach, the calculation time and the total joint error were separately reduced by 54.89% and 45.41% compared with APF. Finally, the novel algorithm is presented and demonstrated with some illustrative examples of the citrus picking robot, both offline during the design phase as well as online during a realistic picking test.

Collision-free Inverse Kinematics of Redundant Manipulator for Agricultural Applications through Optimization Techniques

“…Potential field functions are also employed for collision avoidance during the motion planning of non-holonomic mobile robots in a cluttered environment [24]. Tang et al [25] proposed an improved mathematical model of the APF (artificial potential field) method for the motion planning algorithm of citrus-picking robots. While the potential field principle is attractive due to its elegance and simplicity, however, it has significant problems of trapping at local minima, no passage between closely spaced obstacles, causing unstable motion in the presence of obstacles, and oscillations in narrow passages [26].…”

Collision avoidance trajectory planning for a dual-robot system: using a modified APF method

“…To solve these problems of the traditional APF method, many modified APF methods are proposed, such as Xu et al [32] proposed that when the robot falls into the local minimum, the robot was set to move along the direction of the potential surface of repulsive potential energy, so as to make the robot break away from the local minimum. Tang et al [33] modified the potential field and force model of the APF method, reduced the amount of calculation of the algorithm, and avoided the problems of target unreachable and local minimum trap. Compared with the traditional APF method, the modified APF reduced the operation time by 54.89% and the total joint error by 45.41%.…”

Study on the Fragrant Pear-Picking Sequences Based on the Multiple Weighting Method

“…The third is the exact method, mainly consisting of the exhaustive method and the dynamic planning method. When performing a picking task, the algorithm used to solve the fruit-picking sequence problem and find the shortest path is the TSP [8][9][10][11][12][13]. The second is in the joint space of the robot, arming each joint as a motor unit, with the power or torque calculated using the best pose [14][15][16][17][18][19].…”