Robots Adapting to the Environment: A Review on the Fusion of Dynamic Movement Primitives and Artificial Potential Fields

Abstract

For the development of autonomous robotic systems, Dynamic Movement Primitives (DMP) and Artificial Potential Fields (APF) are two well known techniques. DMPs are a reference algorithm in robotics for one shot learning as they enable learning complex movements and generating smooth trajectories, while APF are outstanding in navigation and obstacle avoidance tasks. By integrating DMPs and APF, the task automation capability can be significantly enhanced, as the precision of DMPs combined with the reactive nature of APF promises, in theory, adaptability and efficiency for the learning algorithm. Despite the numerous papers discussing and reviewing both techniques independently, there is a lack of an objective comparison of the investigations combining both approaches. This paper aims to provide such a comprehensive literature analysis, using a homogenized mathematical formulation. Moreover, a categorization based on their application scope, the robots used and their characteristics is provided. Finally, open challenges in the combination of DMP and APF are discussed, highlighting further works that are worth conducting for improving the integration of both approaches.

Keywords

• Artificial potential fields
• dynamic movement primitives
• learning from demonstration
• obstacle avoidance
• robot trajectory adaptation