MIT Researchers Develop Algorithm to Help Robots Navigate Complex Environments
It isn’t easy for a robot to find its way out of a maze. Picture the machines trying to traverse a kid’s playroom to reach the kitchen, with miscellaneous toys scattered across the floor and furniture blocking some potential paths. This messy labyrinth requires the robot to calculate the most optimal journey to its destination, without crashing into any obstacles. What is the bot to do?
Introducing the Graphs of Convex Sets (GCS) Trajectory Optimization Algorithm
MIT Computer Science and Artificial Intelligence Laboratory (CSAIL) researchers have developed the “Graphs of Convex Sets (GCS) Trajectory Optimization” algorithm, a scalable, collision-free motion planning system for robotic navigational needs. The approach marries graph search (a method for finding discrete paths in a network) and convex optimization (an efficient method for optimizing continuous variables so that a given cost is minimized), and can quickly find paths through maze-like environments while simultaneously optimizing the trajectory of the robot.
Key Features of the Algorithm
The CSAIL-led project consistently finds shorter paths in less time than comparable planners, showing GCS’ capability to efficiently plan in complex environments. The algorithm can map out collision-free trajectories in as many as 14 dimensions (and potentially more), with the aim of improving how machines work in tandem in warehouses, libraries, and households.
Real-World Applications
The success of the algorithm was demonstrated in real-world tests, where two robotic arms holding a mug navigated around a shelf while optimizing for the shortest time and path. The duo’s synchronized motion resembled a partner dance routine, swaying around the bookcase’s edges without dropping objects. In subsequent setups, the researchers removed the shelves, and the robots swapped the positions of spray paints and handed each other a sugar box.
Conclusion
The GCS algorithm has the potential to dramatically enhance the speed and efficiency of robot motions and their ability to adapt to novel environments. The team is exploring applications of GCS trajectory optimization to robot task and motion planning, and is also looking into more involved problems where robots have to make contact with their environment, such as pushing or sliding objects out of the way.
FAQs
Q: What is the Graphs of Convex Sets (GCS) Trajectory Optimization algorithm?
A: The GCS algorithm is a scalable, collision-free motion planning system for robotic navigational needs, combining graph search and convex optimization to find paths through maze-like environments while optimizing the trajectory of the robot.
Q: What are the key features of the algorithm?
A: The algorithm consistently finds shorter paths in less time than comparable planners, and can map out collision-free trajectories in as many as 14 dimensions (and potentially more).
Q: What are the potential applications of the algorithm?
A: The algorithm has the potential to improve how machines work in tandem in warehouses, libraries, and households, and could be used in manufacturing, where two robotic arms working in tandem could bring down an item from a shelf.
Q: What is the future direction of the research?
A: The team is exploring applications of GCS trajectory optimization to robot task and motion planning, and is also looking into more involved problems where robots have to make contact with their environment, such as pushing or sliding objects out of the way.
