A Safety Motion Planning Algorithm for Mobile Manipulator Based on Improved LSTM Neural Network and Capability Map

Currently, indoor narrow environments, such as assembly in large aircraft cabins, suffer from the inefficiency and compromised quality of manual operations. The existing mobile manipulators for human-machine collaboration still have certain deficiencies, including high redundancy, insufficient constraints, and weak active safety protection capabilities. In light of these challenges, we present a safety motion planning algorithm for mobile manipulator based on improved LSTM neural network and Capability Map (CM). We have modified the robot's capability index (CI) based on the safety potential field function. This provides the spatial representation of robot operation ability for motion planning. In addition, we have added a security gate mechanism to LSTM, enabling more accurate prediction of personnel movements in high-risk areas. The experimental results demonstrate that this algorithm holds great potential in human-machine collaboration. By doing so, the algorithm enables more accurate prediction of the movement trajectory of personnel and facilitates the avoidance of motion conflicts between robots and personnel.