  Path planning is a classic problem for autonomous robots. To ensure safe and
efficient point-to-point navigation an appropriate algorithm should be chosen
keeping the robot's dimensions and its classification in mind. Autonomous
robots use path-planning algorithms to safely navigate a dynamic, dense, and
unknown environment. A few metrics for path planning algorithms to be taken
into account are safety, efficiency, lowest-cost path generation, and obstacle
avoidance. Before path planning can take place we need map representation which
can be discretized or open configuration space. Discretized configuration space
provides node/connectivity information from one point to another. While in
open/free configuration space it is up to the algorithm to create a list of
nodes and then find a feasible path. Both types of maps are populated by
obstacle positions using perception obstacle detection techniques to represent
current obstacles from the perspective of the robot. For open configuration
spaces, sampling-based planning algorithms are used. This paper aims to explore
various types of Sampling-based path-planning algorithms such as Probabilistic
RoadMap (PRM), and Rapidly-exploring Random Trees (RRT). These two algorithms
also have optimized versions - PRM* and RRT* and this paper discusses how that
optimization is achieved and is beneficial.
