A Hybrid AI-Based Adaptive Path Planning for Intelligent Robot Arms - Ali Abdi; Ju Hong Park (IEEE Access, 2023)
Intelligent robot arms are advanced robotic systems used in Industry 4.0 to perform complex tasks. Unlike conventional robot arms, which perform predefined tasks, intelligent robot arms have autonomy and can operate in changing environments, interact with other machines, and collaborate with humans. In this regard, adaptive path planning is crucial for intelligent robot arms, involving real-time environment monitoring and path generation to continuously update the robot’s trajectory based on changes in the surroundings. This paper presents an adaptive path planning method for intelligent robot arms to be used in dynamic environments. The proposed method is based on a hybrid active-passive approach and has been tested in a dynamic workspace simulation environment. The results indicate the ability of the proposed method to respond dynamically in a complex scenario where the target is fluctuating, and an obstacle is intentionally placed in the robot’s path. Additionally, real-time analysis results show that the method can be categorized as real-time path planning with less than 100 ms reaction time for grid sizes with less than $96× 96 × 96$ cells. This insight presents opportunities for the establishment of smart factories, smart homes, and smart cities, where the presence of intelligent robot arms in dynamic environments becomes essential.