FASTFUNNELS: Agile Multi Robot Navigation in Narrow Spaces via Adaptive Dynamic Funnels
Overview
This paper introduces FastFunnels, a method for high-speed multi-robot navigation in cluttered environments. Unlike rigid formation control that requires predefined shapes and slows down near bottlenecks, FastFunnels uses a reinforcement learning policy to generate a dynamic, blob-like elliptical patch that adapts its shape in real time. At the low level, distributed model predictive control (DMPC) keeps robots within this soft forcing function while avoiding collisions. Evaluated on simulated F1TENTH racing cars across 14 scenarios with 1 to 6 robots, FastFunnels achieves an 89% completion rate at an average speed of 7.73 m/s, significantly outperforming baselines that either fail or operate at much lower speeds (0.07-1.79 m/s) with only about 40% completion.
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Event evidence and corrections
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Environment: simulation
“nd orienting itself around obstacles and through tight spaces. At the low level, the robots are controlled via distributed model predictive control (DMPC) to stay inside the patch, which acts as a soft forcing function, while avoiding collisions with each other. We evaluate our approach, FastFunnels, on simulated F1TENTH racing cars across 14 scenarios with N=1 to N=6 robots, averaged over 20 trials each. FastFunnels”
Exact source · revision 1Source owner not reported
Reported quantity · Robot count: 6 robots · Basis not reported
“nd orienting itself around obstacles and through tight spaces. At the low level, the robots are controlled via distributed model predictive control (DMPC) to stay inside the patch, which acts as a soft forcing function, while avoiding collisions with each other. We evaluate our approach, FastFunnels, on simulated F1TENTH racing cars across 14 scenarios with N=1 to N=6 robots, averaged over 20 trials each. FastFunnels”
Exact source · revision 1Source owner not reported
Reported quantity · trials: 20 other · reported trials
“nd orienting itself around obstacles and through tight spaces. At the low level, the robots are controlled via distributed model predictive control (DMPC) to stay inside the patch, which acts as a soft forcing function, while avoiding collisions with each other. We evaluate our approach, FastFunnels, on simulated F1TENTH racing cars across 14 scenarios with N=1 to N=6 robots, averaged over 20 trials each. FastFunnels”
Exact source · revision 1Source owner not reported
Reported quantity · Reported completion rate: 89 percent · Basis not reported · Differing source assertions
“achieves an 89% completion rate at 7.73 m/s, while all baselines either fail to complete the track or succeed only at very low speeds (0.07-1.79 m/s, about 40% completion rate).”
Exact source · revision 1Source owner not reported
Reported quantity · speed: 7.73 other · Basis not reported · Differing source assertions
“achieves an 89% completion rate at 7.73 m/s, while all baselines either fail to complete the track or succeed only at very low speeds (0.07-1.79 m/s, about 40% completion rate).”
Exact source · revision 1Source owner not reported
Reported quantity · speed: 0.07 other · Basis not reported · Differing source assertions
“achieves an 89% completion rate at 7.73 m/s, while all baselines either fail to complete the track or succeed only at very low speeds (0.07-1.79 m/s, about 40% completion rate).”
Exact source · revision 1Source owner not reported
Reported quantity · Reported completion rate: 40 percent · Basis not reported · Differing source assertions
“achieves an 89% completion rate at 7.73 m/s, while all baselines either fail to complete the track or succeed only at very low speeds (0.07-1.79 m/s, about 40% completion rate).”
Exact source · revision 1Source owner not reported
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- arXiv Robotics — research abstractsSignalFASTFUNNELS: Agile Multi Robot Navigation in Narrow Spaces via Adaptive Dynamic Funnels
This paper introduces FastFunnels, a method for high-speed multi-robot navigation in cluttered environments. Unlike rigid formation control that requires predefined shapes and slows down near bottlenecks, FastFunnels uses a reinforcement learning policy to generate a dynamic, blob-like elliptical patch that adapts its shape in real time. At the low level, distributed model predictive control (DMPC) keeps robots within this soft forcing function while avoiding collisions. Evaluated on simulated F1TENTH racing cars across 14 scenarios with 1 to 6 robots, FastFunnels achieves an 89% completion rate at an average speed of 7.73 m/s, significantly outperforming baselines that either fail or operate at much lower speeds (0.07-1.79 m/s) with only about 40% completion.
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