**Author details**

**Figure 17** describes the evaluation case on unknown environment. The environment is initially generated with random obstacles that occupy 15% of the map. The robot does not know the initial conditions; it will replan its path whenever it detects obstacles that do not exist in

For unknown environment scenario, D\*LR performs significantly better than D\* Lite as increasing map scale. The reason is that if the replanned path is much longer than the initial path, which is the common case in unknown environment, the replanning process of D\* Lite is also expensive. AD\* still has the least computation compared to old search-based algorithm; it reduces drastically the computation of D\* Lite with 845% better performance, in the case

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

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

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 plan-

*ε* = 10.0, while still maintains good path solution.

its map.

86 Advanced Path Planning for Mobile Entities

**6. Conclusion**

complex environment.

scenario.

ning converging to human ability.

An T. Le1 \* and Than D. Le2

\*Address all correspondence to: eeit2015\_an.lt@student.vgu.edu.vn

1 Department of Electrical Engineering and Information Technology, Vietnamese-German University, Hồ Chí Minh, Vietnam

2 Faculty of Engineering, Bristol Robotics Laboratory, Bristol University, Bristol, United Kingdom
