**6. Conclusion**

In practice, motion planning algorithms can be implemented on top of navigation layer such as simultaneous localization and mapping (SLAM) for autonomous robot. While navigation layer enables the robot to perceive surrounding information and its position relative to the surroundings, motion planning layer gives the robot abilities to plan a path in surrounding environment and make decision to avoid obstacles. Because of that fact, navigation and motion planning are always paired up to enable autonomous robot to operate in dynamic and complex environment.

This chapter is a guide to comprehend the foundation of motion planning, in particular, search-based path planning algorithms. In this chapter, we present the steps to develop and formulate a motion planning problem. We also describe the evolution branches of motion planning and then focus on the development of search-based algorithm family. Each algorithm in search-based family is invented to cope with increasing demands in performance or solution quality, for the robot to operate in more complex scenarios. To reinforce the revolution statement of state-of-the-art search-based algorithms, we provide a computation and optimality comparison between search-based algorithms on partially known and unknown environment. Based on the data, we conclude that Anytime Dynamic A\* is the most suitable algorithm that enables the robot to operate in cluttered and fast changing scenario.

Until recently, the mainstream of motion planning development is to enhance the performance of search-based algorithm and their solution optimality by modifying cell decomposition method. There are signals that the trajectory planning paradigm is starting to be active research field after being frozen for a decade. We expect that the future development of trajectory planning will robustly incorporate motion constraints with higher optimality and better computation. The ultimate goal of motion planning field is giving robot spatial decision planning converging to human ability.
