Advances in sensing technology, computer vision, communications, and computational power have contributed towards the emergence of autonomous agents in a wide range of fields and indusrties. A challenging task, often faced in the field of robotics, is the formulationof a strategy that generates a collision free route between start and finish positions within an suitable timeframe. Real-time planners are often limited to path planning and ignore constraints imposed on the vehicle. This research investigate the use vector valued parametric splines in generating steering functions as an alternative to commonly used polynomials and arcs. The development of a steering function that integrates kinematic and dynamic constraints of a vehicle will be undertaken. It is expected that these steering functions will reduce search space dimensions of the planning problem. Eventually, leading to performance improvements in kinodynamic planners performance. Computational, numerical and mathematical analyses will be performed to integrate the presented algorithms with state of the art sampling-based motion planners. It is hoped that a framework for real-time, efficient, kinodynamic planning for unmanned aerial and ground vehicles can be presented as a result of the investigations carried out in this project.