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Motion Planning

In-depth coverage and expert perspectives

Lab focus

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High-DOF Configuration Spaces

Addressing the computational complexity of planning trajectories for robotic systems with many degrees of freedom in cluttered environments.

Asymptotic Optimality

Focusing on sampling-based algorithms that guarantee convergence to the optimal solution as computation time increases.

Physics-Aware Constraints

Integrating dynamic limitations and physical interactions directly into the planning loop to ensure executable and safe autonomous behaviors.

In practice, the cleanest way to think about “optimal” motion planning is as a chain of assumptions: what state you plan in, what costs you encode, and what physics you trust enough to constrain. Asymptotic optimality is valuable because it forces you to name those assumptions explicitly, then reason about what happens as computation grows—rather than treating a single run as a verdict.

Physics-aware planning raises the bar: feasibility is no longer “collision-free,” it is “executable under dynamics, contacts, and bounds.” That shift tends to expose the real bottleneck: model fidelity and numerical conditioning, not just algorithm choice. The strongest results still hinge on how well the modeled constraints match the interaction regime you actually operate in, especially when contact and friction dominate the task.

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