Navigation of humanoids in cluttered environments is a complex task which requires sensorimotor coordination while maintaining the balance of the walker. Typically, robots rely on greedy computation of slow, inefficient and unnatural gaits. This contrasts with the relative ease and efficiency characterizing motion planning and execution of humans. In previous contributions, we developed a bio-inspired torque-based controller recruiting virtual muscles driven by reflexes and a central pattern generator. Speed control and steering could be achieved by the modulation of the forward speed and heading. This paper extends this controller to automatically compute both of these inputs, in order to achieve trajectory planning in 3D cluttered environments. To do so, we first develop a method based on internal models, a concept widespread in cognitive neuroscience. We then compare the obtained gait to results generated with a more traditional planning method based on potential fields. In particular, we show that internal models result in more robust gaits by taking the walker dynamics into account.