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
6 Pith papers cite this work. Polarity classification is still indexing.
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BOW Planner applies constrained Bayesian optimization over reachable velocity windows to enable efficient, safe motion planning in complex environments with kinodynamic constraints.
ELMP performs data-efficient self-supervised adaptation of neural motion planners via analytical policy gradients and point-cloud tool encoding, raising success from 57.3% zero-shot to 89.8% in unseen environments.
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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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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ELMP: Efficient Learning for Motion Planning via Analytical Policy Gradients
ELMP performs data-efficient self-supervised adaptation of neural motion planners via analytical policy gradients and point-cloud tool encoding, raising success from 57.3% zero-shot to 89.8% in unseen environments.
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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.
- Flow Motion Policy: Manipulator Motion Planning with Flow Matching Models