A better example would be Embark self-driving semi-trucks that have a set target location and can also adapt to changing environments.
This ability to find an optimal path also plays an important role in other fields such as video games and gene sequencing.
[4] Global path planning refers to methods that require prior knowledge of the robot's environment.
[1][5] The rapidly exploring random tree method works by running through all possible translations from a specific configuration .
By running through all possible series of translations a path is created for the robot to reach the target from the starting configuration.
[10] The modified indicative routes and navigation method gives various weights to different paths the robot can take from its current position.
This creates a variety of weighted regions in the environment which allows the robot to decide on a path towards the target.
Humanoid robots on the other hand have a similar number of degrees of freedom to a human body which increases the complexity of path planning.
Self-driving vehicles are a form of mobile robots that utilizes real-time path planning.
Oftentimes a vehicle will first use global path planning to decide which roads to take to the target.
While the truck is on the road it will use its sensors alongside local path planning methods to navigate around obstacles to safely reach the target location.
This requires real-time path planning as the mob must avoid various obstacles while following the player.