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D4orm diffusion framework extended into a family of multi-robot trajectory planners
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Yuhao Zhang and other authors published a paper extending D4orm, a dynamics-aware diffusion denoising framework, into a family of multi-robot trajectory planners for different operational requirements, including a decoupled planner, an online receding-horizon planner with feedback control, and a distributed planner. The authors report that evaluations on differential-drive and omnidirectional robots in 2D and 3D environments show that D4orm-based methods find high-quality solutions faster and more reliably than sampling-based optimization methods such as MPPI and a method based on learned diffusion models. The paper also demonstrates zero-shot deployment of ten real quadrotors in an obstacle environment, large-scale deconfliction of 100 simulated robots, and fully onboard distributed "lifelong" operation of six ground robots.
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- D4orm diffusion-denoising framework extended into a family of multi-robot trajectory planners
Based on the dynamics-aware diffusion-denoising framework D4orm, this paper builds a family of multi-robot trajectory planners for different operational requirements, including a decoupled planner, an online receding-horizon planner with feedback control, and a distributed planner. Evaluations with differential-drive and holonomic robots in 2D and 3D environments show that D4orm-based methods find high-quality solutions faster and more reliably than sampling-based optimization methods such as MPPI and a learning-based diffusion model method.
arXiv cs.RO ·
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