## 3 Improved Artificial Potential Field Method Based on Joint Space
### Algorithm Comparison
A real-time obstacle avoidance algorithm that can adapt to changes in the starting position and goal position at any time is needed. In this paper, we propose an improved artificial potential field method in joint space, which can realize the real-time obstacle avoidance of the robotic arm. The experimental results show that the algorithm can achieve good performance under different obstacles. The algorithm completes the computation very quickly, and the performance of the obstacle avoidance trajectory obtained by this algorithm is higher than the trajectory obtained by genetic algorithm. Its main advantages are as follows:

1. The algorithm prevents the transformation of coordinate system from Cartesian space into joint space, and the robotic arm still performs well when it is caught in a singular configuration. 2. The algorithm can solve the gradient expression of repulsive potential field in advance. The potential field function only needs to be calculated once during each iteration, and the calculation efficiency is very fast. 3. The algorithm proposes an effective method to prevent the robotic arm falling into a local minimum state without introducing additional information. In future work, we will further improve this algorithm to make the motion trajectory smoother. In addition, the algorithm can obtain the angle, velocity, and acceleration information of each joint during operation. We intend to combine this algorithm with dynamics to control robotic arms in the future. Y.C. : Conceptualization, Writing--original draft, Data curation, Formal analysis, Validation. L.C. : Writing--Review and editing, Conceptualization, Data curation, Formal analysis, Validation. J.D. : Writing--review and editing, Conceptualization, Data curation, Formal analysis, Validation. Y.L. : Writing--review and editing. All authors have read and agreed to the published version of the manuscript. This research was funded by the National Key R&D Program of China, grant number (2019 YFB1706501). Not applicable. ## References

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