**Search-Based Planning and Replanning in Robotics and Autonomous Systems Autonomous Systems**

**Search-Based Planning and Replanning in Robotics and** 

DOI: 10.5772/intechopen.71663

An T. Le and Than D. Le An T. Le and Than D. Le Additional information is available at the end of the chapter

Additional information is available at the end of the chapter

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

#### **Abstract**

In this chapter, we present one of the most crucial branches in motion planning: searchbased planning and replanning algorithms. This research branch involves two key points: first, representing traverse environment information as discrete graph form, in particular, occupancy grid cost map at arbitrary resolution, and, second, path planning algorithms calculate paths on these graphs from start to goal by propagating cost associated with each vertex in graph. The chapter will guide researcher through the foundation of motion planning concept, the history of search-based path planning and then focus on the evolution of state-of-the-art incremental, heuristic, anytime algorithm families that are currently applied on practical robot rover. The comparison experiment between algorithm families is demonstrated in terms of performance and optimality. The future of search-based path planning and motion planning in general is also discussed.

**Keywords:** A\*, RRT, holonomic path planning, trajectory planning, occupancy map, D\* Lite, incremental planning, heuristics planning, ARA\*, anytime dynamic A\*

### **1. Introduction**

Nowadays, as the rapid advances of computational power together with development of state-of-the-art motion planning (MP) algorithms, autonomous robots can now robustly plan optimal path in narrow configuration space or wide dynamic complex environment with high accuracy and low latency. These recent MP developments have a large impact in medical surgery, animation, expedition and many other disciplines. For instance, RRT [1] algorithm was applied for multi-arm surgical robot in [2]. Expedition robot GDRS XUV was implemented field D\* any-angle path planner [3] that enables the robot to optimally move in harsh environment. D\* [4] is implemented for Mars Rover prototypes and tactical mobile robots in [5]. Bug algorithms were implemented in multi-robot cooperation scenarios [6].

Attribution License (http://creativecommons.org/licenses/by/3.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. © 2018 The Author(s). Licensee IntechOpen. This chapter is distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/3.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

© 2016 The Author(s). Licensee InTech. This chapter is distributed under the terms of the Creative Commons

In general, the problem statement of MP can be generalised as follows: Given the initial defined world space and the robot's configuration space, the MP algorithm must generate a series of consecutive collision-free configurations of the robot that connects start configuration and goal configuration. This series configuration must satisfy any inherent motion or non-motion constraints of the robot.

To cope with a wide range of environment characteristics, MP can be divided into two categories: gross MP and fine MP [7]. The gross MP concerns with the scenarios when world space is much wider than obstacles' size and positional error of the robot, whereas the fine MP solves the planning problems in narrow space that requires high accuracy.

This manuscript presents the development of gross MP algorithm family, in particular searchbased planning and replanning paradigm. The foundation concepts of MP, configuration space representations, and the position of mentioned paradigm in MP big picture is presented in Section 2. Section 3 describes historical basis of search-based algorithm family. Section 4 demonstrates the properties and pitfall of D\* Lite, which is one of the most crucial algorithms to plan path in dynamic environment. After that, the variants of D\* Lite, which improve D\* Lite's optimality and performance, are presented. To confirm the improvements, we provide experimental results of recent path planning algorithms and their comparisons in terms of performance and optimality in Section 5. Section 6 will discuss about the future development of MP and provide conclusion.
