Applications

Before we dive into the details - let’s look at where path planning can be applied!

Sitting in your home or office, some environment-specific examples come to mind right away - vacuum robots plan their paths around a house to ensure that every square inch of space gets cleaned. Self-driving cars are starting to appear around us. These vehicles can accept a destination as an input from a human and plan an efficient path that avoids collisions and obeys all traffic regulations.

More peculiar applications of path planning in robotics include assistive robotics. Whether working with the disabled or elderly, robots are starting to appear in care homes and hospitals to assist humans with their everyday tasks. Such robots must be mindful of their surroundings when planning paths - some obstacles stay put over time, such as walls and large pieces of furniture, while others may move around from day to day. Path planning in dynamic environments is undoubtedly more difficult.

Another robotic application of path planning is the planning of paths by exploratory rovers, such as Curiosity on Mars. The rover must safely navigate the surface of Mars (which is between 55 and 400 million kilometers away!). Accurate problem-free planning that avoids risks is incredibly important.

Path planning is not limited to robotics applications, in fact it is widely used in several other disciplines. Computer graphics and animation use path planning to generate the motion of characters. While computational biology applies path planning to the folding of protein chains.

With many different applications, there are naturally many different approaches. In the next few lessons you will gain the knowledge required to implement several different path planning algorithms.

Lesson Outcomes:

• Understand real-world applications of path planning.
• Select the appropriate path planning algorithm for a given application.
• Be able to successfully implement path planning algorithms in code.
• Interface a path planning package with ROS