This is the second post about robot motion planning. You can find the first post about sampling-based planners over here. Being able to plan a path quickly while avoiding collisions is crucial for our roadmap.
To use a robot, you need to be able to plan a path from point A to point B — bonus points for not hitting anything. This is the cornerstone of our roadmap for robotics. Here’s a quick overview of the different ways to achieve this.
As discussed in the OMPL primer, there are different families of planning algorithms. In this first post we’ll focus on sampling-based planning.
To deliver the best hands in the world, we’ve collaborated with the best researchers — but what does it take to reach out of a research oriented market? To solve real world problems using robots? We’re convinced that tailoring a custom solution for each problem is not the way forward. We want the people facing those problems to be able to use our solutions themselves. And we have a roadmap to get there.
Forget Robocop, Iron Man and Terminator. The most advanced android on the block is no clumsy Metal Mickey, but an astonishingly dextrous hand. Josh Sims gives it the thumbs up.
The Independent wrote an article about the work we do at Shadow. Happy to have my name in it! 😀