Trajectory Optimization of Pickup Manipulator in Obstacle Environment Based on Improved Artificial Potential Field Method

Abstract

In order to realize the technique of quick picking and obstacle avoidance, this work proposes a trajectory optimization method for the pickup manipulator under the obstacle condition. The proposed method is based on the improved artificial potential field method and the cosine adaptive genetic algorithm. Firstly, the Denavit–Hartenberg (D-H) method is used to carry out the kinematics modeling of the pickup manipulator. Taking into account the motion constraints, the cosine adaptive genetic algorithm is utilized to complete the time-optimal trajectory planning. Then, for the collision problem in the obstacle environment, the artificial potential field method is used to establish the attraction, repulsion, and resultant potential field functions. By improving the repulsion potential field function and increasing the sub-target point, obstacle avoidance planning of the improved artificial potential field method is completed. Finally, combined with the improved artificial potential field method and cosine adaptive genetic algorithm, the movement simulation analysis of the five-Degree-of-Freedom pickup manipulator is carried out. The trajectory optimization under the obstacle environment is realized, and the picking efficiency is improved.

Optimization of Motion Planning and Control for Automatic Machines, Robots and Multibody Systems

“…In [10], trajectory optimization is aimed at improving obstacle avoidance in industrial robots. The method proposed therein is based on the improved artificial potential field method and the cosine adaptive genetic algorithm.…”

Entry Guidance Command Generation for Hypersonic Glide Vehicles Under Threats and Multiple Constraints

“…Designing a heading error corridor-based bank angle reversal logic is an effective way to satisfy geographic constraints. The artificial potential field (APF) method is one of the most popular algorithms in obstacle avoidance for mobile robots [19], unmanned aerial vehicles (UAVs) [20], autonomous vehicles [21], [22], and manipulators [23], which has the benefits of brief mathematical description, simplicity, high efficiency, and strong adaptability. But the local minimum problem is a drawback of the APF method that should be noticed.…”

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

“…Different from global planning algorithms such as RRT and PRM, APF is a local motion planning algorithm with low algorithm complexity, low dependence on computer resources, and strong real-time performance. However, APF has problems such as local minimum trap (LMT) [24,25] and target not reachable problem (TNRP) [26]. LMT means that when the object has not reached the target position, the gradient of the potential field of APF disappears, then the resultant force becomes zero, and the object in the potential field no longer can reach the target position, which is the main problem and shortcoming of APF.…”

“…A large number of scholars have also carried out lots of relevant studies on the application of APF in manipulators. Zhou, H. et al [26] improved the repulsive force calculation function of APF and combined it with the cosine adaptive genetic algorithm. Motion planning for the end effector of a 5-DOF manipulator needed 8.72 and 8.31 s respectively in conventional and LMT conditions, and the time was reduced by 51.6% and 53.85% compared with the original APF.…”