To date, most autonomous micro air vehicles (MAV-s) operate in a controlled environment, where the location of and attitude of the aircraft are measured be dedicated high-power computers with IR tracking capability. If MAV-s are to ever exit the lab and carry out autonomous missions, their flight control systems needs to utilize on-board sensors and high-efficiency attitude determination algorithms. To address this need, we investigate the feasibility of using body schemas to carry out path planning in the vision space of the MAV. Body schemas are a biologically-inspired approach, emulating the plasticity of the animal brains, allowing efficient representation of non-linear mapping between the body configuration space, i.e. its generalized coordinates and the resulting sensory outputs. This paper presents a numerical experiment of generating landing trajectories of a miniature rotor-craft using the notion of body and image schemas. More specifically, we demonstrate how a trajectory planning can be executed in the image space using a pseudo-potential functions and a gradientbased maximum seeking algorithm. It is demonstrated that a neural-gas type neural network, trained through Hebbian-type learning algorithm can learn a mapping between the rotor-craft position/attitude and the output of its vision sensors. Numerical simulations of the landing performance of a physical model is also presented, The resulting trajectory tracking errors are less than 8 %.