Top Tweets for #ModelPredictiveController

about 1 month ago

We are excited to share our paper "Temporal Cascading of Planning and Control for Quadrotor MPC," published in the IEEE Transactions on Robotics, where, instead of separating planning and control, we unify them in a single #ModelPredictiveController: a high-fidelity model controls the near future, while a low-fidelity model plans the long horizon! PDF: https://t.co/eqJ7ld79zd Narrated video: https://t.co/Yip58934GH Traditionally, planning and control are treated separately: a simplified model plans a trajectory over a long horizon, and a high-fidelity controller then tries to track it. This is computationally convenient, but suboptimal: the controller is constrained by a plan that does not fully capture the drone dynamics. We introduce a different way of looking at this problem. Instead of separating planning and control, we put them into one MPC optimization, but use different models at different points in time: a high-fidelity quadrotor model over the near future for accurate control, followed by a computationally cheap point-mass model over the longer horizon for planning. In other words: control precisely nearby, plan efficiently far ahead. The key contribution is that the two horizons are not simply stacked: they are coupled through transition and feasibility constraints, so that what the simplified model plans remains compatible with what the real quadrotor can execute. We also use parallel low-fidelity planners and progressive obstacle smoothing to escape local minima in cluttered environments without slowing down the real-time control loop. The result is long-horizon planning with iteration times below 5 ms, and in simulation and real-world flights, up to 75% lower closed-loop cost than standard MPC and conventional hierarchical planner-controller approaches under comparable computational budgets. To recap: rather than choosing between an accurate short-horizon model and an efficient long-horizon model, use both at the time scales where each matters most! Kudos to Rudolf Reiter, Chao Qin, Leonard Bauersfeld! @uzh_ifi @UZH_Science @UZH_en @UZHspacehub @ERC_Research @UZH_ai @AUTOASSESS_EU

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