An SOS-certified adaptive Bug2 planner using second-order IK approximation and the S-procedure achieves zero joint-limit violations and 100% goal success across 94 adversarial test scenarios.
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7 Pith papers cite this work. Polarity classification is still indexing.
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A new alignment heuristic and star-shaped simplex chain construction for feedback motion planning reduces average path bending by 91.4% and LQR effort by 45.5% while remaining computationally efficient.
Backward time-reversed conflict-based search finds initial collision-free simultaneous-arrival trajectories for agents at rest, which are then refined via distributed nonlinear ADMM optimal control.
MRCPP is a new multi-robot coverage planning method that cuts energy use by 3-40% and planning time by an order of magnitude compared to existing approaches through optimized sweeping paths, safety buffers, and workload balancing.
A QP-designed C^∞-smooth vector field paired with an analytic nonlinear controller enables safe, input-constrained unicycle navigation to goals with faster convergence and lower turning effort than baselines.
BTIT* is the first anytime MEET-style kinodynamic planner that uses efficient-to-evaluate termination conditions for early on-the-fly stopping while preserving asymptotic optimality.
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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Verified Task-Space Motion Planning Under Joint-Space Constraints
An SOS-certified adaptive Bug2 planner using second-order IK approximation and the S-procedure achieves zero joint-limit violations and 100% goal success across 94 adversarial test scenarios.
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Smooth Feedback Motion Planning with Reduced Curvature
A new alignment heuristic and star-shaped simplex chain construction for feedback motion planning reduces average path bending by 91.4% and LQR effort by 45.5% while remaining computationally efficient.
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Multi-Agent Motion Planning for Simultaneous Arrival using Time-Reversed Search and Distributed Optimal Control
Backward time-reversed conflict-based search finds initial collision-free simultaneous-arrival trajectories for agents at rest, which are then refined via distributed nonlinear ADMM optimal control.
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Energy-Efficient Multi-Robot Coverage Path Planning of Non-Convex Regions of Interests
MRCPP is a new multi-robot coverage planning method that cuts energy use by 3-40% and planning time by an order of magnitude compared to existing approaches through optimized sweeping paths, safety buffers, and workload balancing.
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Planning Smooth and Safe Control Laws for a Unicycle Robot Among Obstacles
A QP-designed C^∞-smooth vector field paired with an analytic nonlinear controller enables safe, input-constrained unicycle navigation to goals with faster convergence and lower turning effort than baselines.
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Optimal Kinodynamic Motion Planning Through Anytime Bidirectional Heuristic Search with Tight Termination Condition
BTIT* is the first anytime MEET-style kinodynamic planner that uses efficient-to-evaluate termination conditions for early on-the-fly stopping while preserving asymptotic optimality.
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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.