Herding by caging: a formation-based motion planning framework for guiding mobile agents

• Published in: Autonomous robots Vol. 45; no. 5; pp. 613 - 631
• Main Authors: Song, Haoran, Varava, Anastasiia, Kravchenko, Oleksandr, Kragic, Danica, Wang, Michael Yu, Pokorny, Florian T., Hang, Kaiyu
• Format: Journal Article
• Language: English
• Published: New York Springer US 01-06-2021; Springer Nature B.V
• Subjects: Agents (artificial intelligence); Agricultural robots; Algorithms; Artificial Intelligence; Computational geometry; Computer Imaging; Control; Engineering; Farmers; Mechatronics; Mobile agents; Mobile robots; Motion and path planning; Motion modeling; Motion planning; Path planning; Path planning for multiple mobile robots or agents; Path-planning algorithm; Pattern Recognition and Graphics; Repulsive forces; Robot dynamics; Robot programming; Robotic manipulation; Robotics; Robotics and Automation; Robots; Sheep; Special Issue on Topological Methods in Robotics; Target location; Topological representation and abstraction of configuration spaces; Vision; Topological representation and abstraction of configuration spaces; Computational geometry; Motion and path planning; Path planning for multiple mobile robots or agents
• ISSN: 0929-5593; 1573-7527; 1573-7527
• DOI: 10.1007/s10514-021-09975-8

We propose a solution to the problem of
herding by caging
: given a set of mobile robots (called herders) and a group of moving agents (called sheep), we guide the sheep to a target location without letting them escape from the herders along the way. We model the interaction between the herders and the sheep by defining virtual “repulsive forces” pushing the sheep away from the herders. This enables the herders to partially control the motion of the sheep. We formalize this behavior topologically by applying the notion of
caging
, a concept used in robotic manipulation. We demonstrate that our approach is provably correct in the sense that the sheep cannot escape from the robots under our assumed motion model. We propose an RRT-based path planning algorithm for herding by caging, demonstrate its probabilistic completeness, and evaluate it in simulations as well as on a group of real mobile robots.