Multi-robot formation control and object transport in dynamic environments via constrained optimization
By Javier Alonso-Mora, Stuart Baker, Daniela Rus. 2017
305 citations recorded by the source corpus.
We present a constrained optimization method for multi-robot formation control in dynamic environments, where the robots adjust the parameters of the formation, such as size and three-dimensional orientation, to avoid collisions with static and moving obstacles, and to make progress towards their goal. We describe two variants of the algorithm, one for local motion planning and one for global path planning. The local planner first computes a large obstacle-free convex region in a neighborhood of the robots, embedded in position-time space. Then, the parameters of the formation are optimized therein by solving a constrained optimization, via sequential convex programming. The robots navigate towards the optimized formation with individual controllers that account for their dynamics. The idea is extended to global path planning by sampling convex regions in free position space and connecting them if a transition in formation is possible - computed via the constrained optimization. The path of lowest cost to the goal is then found via graph search. The method applies to ground and aerial vehicles navigating in two- and three-dimensional environments among static and dynamic obstacles, allows for reconfiguration, and is efficient and scalable with the number of robots. In particular, we consider two applications, a team of aerial vehicles navigating in formation, and a small team of mobile manipulators that collaboratively carry an object. The approach is verified in experiments with a team of three mobile manipulators and in simulations with a team of up to sixteen Micro Air Vehicles (quadrotors).
Subfields: Computer Networks and Communications, Computer Vision and Pattern Recognition
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Citations in the corpus
- Multi-vehicle Flocking Control with Deep Deterministic Policy Gradient Method (2018, background): “Comparing to a single vehicle system, the MVS usually has higher efficiency and operational capability in accomplishing complex tasks such as transportation [2], search and rescue [3], mapping [4].”
- Swarm of micro flying robots in the wild (2022, background): “k d w ( t i ) = k d w ( u p( t i ) , k p( )) = ‖ E 1/2 ( u p( t i ) − k p( ))‖ (19)”
- Distributed multi-robot collision avoidance via deep reinforcement learning for navigation in complex scenarios (2020, background): “, 2009), and object transportation (Alonso-Mora et al., 2017; Hu and Sun, 2011; Michael et al., 2011; Turpin et al., 2014).”