Humanoid dual-arm robots face significant challenges in planning safe and humanoid motions during collaborative tasks due to overlapping workspaces and kinematic redundancy. This paper proposes a real-time joint adjustment planning method that optimizes dual-arm motion by defining a “joint discomfort” metric, which quantifies the deviation of joint angles from their comfort positions, thereby enhancing the naturalness and human-likeness of the motion. Leveraging the parallel computing capability of recurrent neural networks (RNNs) and the redundancy resolution of dual arms, our method dynamically adjusts joint configurations to minimize discomfort while integrating trajectory tracking and obstacle avoidance constraints. Predefined tasks and constraints are formulated as a Quadratic Programming (QP) problem, efficiently solved using an RNN-based approach. Numerical simulations and physical experiments on the Ginger robot – a dual-arm system with 7-degree-of-freedom (DOF) manipulators – validate the efficacy of the proposed planning method in three key aspects: (1) optimizing joint space utilization to enhance workspace flexibility, (2) adaptively regulating joint motions, and (3) achieving sub-millimeter tracking accuracy (position error ¡0.15 mm) under dynamically constrained scenarios.

This paper presents an integrated optimal control framework for velocity and steering control of an autonomous pursuit vehicle, where the control objectives satisfy the requirements of collision avoidance and moving target tracking. A distinctive feature of the proposed velocity and steering control is the application of logarithmic penalty functions to both. The control barrier imposed by logarithmic function provides a unique tool in computing a balanced trajectory with optimal tracking error, control effort and safety margin. Trajectories compliant with the safety regulations for autonomous driving have been planned based on estimated intention of the target and the obstacles. Effects of the controller weights have been extensively simulated to assess the performance of the proposed strategy in a variety of dynamic situations. The controller has been validated on a real-life robot by using a shrinking horizon control policy for iterative optimisation.