Graphs of convex sets with Bezier paths and a simplified bicycle model produce trajectories that closely match nonlinear optimal control results but with better speed and initialization robustness in CommonRoad driving scenarios.
Rapidly-exploring random trees: A new tool for path planning,
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Flow Motion Policy uses flow matching to model distributions over feasible manipulator paths, enabling best-of-N sampling with post-generation collision filtering to improve success and efficiency over prior neural and sampling-based planners.
BOW Planner applies constrained Bayesian optimization over reachable velocity windows to enable efficient, safe motion planning in complex environments with kinodynamic constraints.
A hybrid navigation system uses offline HJ reachability computations as heuristics and safety constraints within graph search to achieve faster and safer robot movement in complex indoor environments.
A hybrid search-plus-optimal-control framework that produces optimized, kinematically feasible trajectories for multiple agents by warm-starting an OCP from an initial feasible solution.
citing papers explorer
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Motion Planning for Autonomous Vehicles using Optimization over Graphs of Convex Sets
Graphs of convex sets with Bezier paths and a simplified bicycle model produce trajectories that closely match nonlinear optimal control results but with better speed and initialization robustness in CommonRoad driving scenarios.
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Flow Motion Policy: Manipulator Motion Planning with Flow Matching Models
Flow Motion Policy uses flow matching to model distributions over feasible manipulator paths, enabling best-of-N sampling with post-generation collision filtering to improve success and efficiency over prior neural and sampling-based planners.
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BOW: Bayesian Optimization over Windows for Motion Planning in Complex Environments
BOW Planner applies constrained Bayesian optimization over reachable velocity windows to enable efficient, safe motion planning in complex environments with kinodynamic constraints.
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A Hamilton-Jacobi Reachability-Guided Search Framework for Efficient and Safe Indoor Planar Robot Navigation
A hybrid navigation system uses offline HJ reachability computations as heuristics and safety constraints within graph search to achieve faster and safer robot movement in complex indoor environments.
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Optimized and kinematically feasible multi-agent motion planning
A hybrid search-plus-optimal-control framework that produces optimized, kinematically feasible trajectories for multiple agents by warm-starting an OCP from an initial feasible solution.