Tool Center Point Path Planning and Robot Allocation Using Neural Models of Inverse Kinematics and Reachability

The automation of construction processes using robotic systems promises considerable increases in efficiency. However, a key challenge lies in the path planning of the tool center point (TCP), taking into account the complex environments on construction sites. Conventional methods for inverse kinematics (IKs) and reachability analysis often reach their limits in terms of flexibility and are less suitable for incorporation into gradient-based optimization. Especially when coordinating multiple robots, simultaneous optimization of TCP poses and assignment is crucial to ensure effective execution. This paper investigates neural networks (NNs) to determine the IKs, compares different network architectures and the effect of positional encoding for manipulators with multiple solutions of the IK. Additionally, an NN for predicting the kinematic reachability is presented. For both NNs, it is shown that encoding the positional values is particularly advantageous for robots with a large workspace and tasks that involve little or no redundancy. Based on the NNs, an augmented Lagrangian optimization problem for planning TCP poses for component transportation is designed, which jointly optimizes path poses and latent IK variables within a unified framework. The optimization takes into account collisions, kinematic reachability, number of handovers, joint configuration changes and path smoothness. The method is examined on three simulative test cases using a manipulator arm from Jekko and the UR10e from Universal Robots. These include simultaneous optimization of multiple paths, obstacle avoidance and assignment of path sections to a specific robot. Compared to sampling-based planners such as IRRT⋆ and BIT⋆, the proposed method achieves lower computation times in the majority of evaluated scenarios while simultaneously producing superior path quality in terms of path length, orientation consistency, and joint configuration changes.