**2. Motion planning concepts**

This section will provide an overview of the basic elements that every MP problem must involve. These elements are configuration space of robot and obstacles, environment representation, MP method and search method. The mentioned factors must be analysed consecutively in order to apply suitable MP algorithm family for each scenario.

exponential lower bound [7]. Until recently, the mainstream of non-holonomic MP research is developed based on random rapidly exploring random tree planner (RRT). For example, heuristics property of A\* [8] has been applied to RRT for faster trajectory convergence [9]. Fast Marching Square method was developed for non-holonomic car-like robot based on RRT that

**Figure 1.** Classification of MP algorithm families based on problem type; the deepest leaves of algorithm tree are

Search-Based Planning and Replanning in Robotics and Autonomous Systems

http://dx.doi.org/10.5772/intechopen.71663

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Unlike sampling-based paradigm, search-based paradigm, which represents for path planning algorithms, has a long history of evolution, from basic graph searching to dynamic motion planner with constraints. In this paradigm, robot is treated as point or scalar robot that is able to move in any direction at any time interval. Hence, the configuration obstacle space has the same dimension with the environment, and the generated trajectory is just a path in operating environment. Search-based paradigm is divided into time-invariant and timevariant environment categories. A\* is the representative for time-invariant algorithm family; its cost function is incorporated with heuristic property for faster optimal path planning. When dealing with time-variant problem, although we can ensure the optimality and correctness of path solution, we cannot just rerun A\* from the point that the robot detects changes in environment due to high latency. To efficiently path replanning in dynamic environment, incremental property is combined with heuristic property to develop D\* Lite algorithm; this algorithm is the basis for future development of search-based replanning. Many variants of

The development of search-based algorithm family is described detail in Section 3 and Section 4.

produces smoother trajectory than RRT [10].

representatives for their families.

D\* Lite for different MP problems are presented in **Table 1**.
