The technology
Motion planning for robots — the computation of collision-free, feasible paths from a start to a goal — often relies on sampling-based planners. These planners randomly sample points in the robot’s configuration (state) space and connect them to build feasible trajectories. While random sampling offers theoretical guarantees (e.g. probabilistic completeness or asymptotic optimality), such guarantees don’t say much about performance for a finite number of samples — i.e., realistic, bounded run-time scenarios.
This technology replaces random (or uniform) sampling with a deterministic, lattice-based sampling scheme, grounded in mathematical geometry.
By constructing sample sets according to this lattice (rather than at random), the method provides strong finite-time guarantees: the planner will reliably generate a usable path within a bounded time budget — not only asymptotically, but in real-world settings with limited computational resources.
The result is a motion planning method that retains the benefits of sampling-based planners (scalability, generality, global guarantees) while dramatically improving runtime performance, predictability, and reliability.
Key Advantages
• Significant speed-up: The lattice-based sampler yields up to two orders-of-magnitude speed improvement compared to existing methods for complex motion-planning problems.
• Finite-time guarantees: The method ensures reliable performance within a bounded number of samples — essential for real-time or resource-constrained systems.
• Deterministic / predictable behavior: No randomness means reproducibility and consistency — valuable for industrial robots and safety-critical applications.
• Scalable to higher-dimensional configuration spaces: The approach remains relevant even for complex, high-degree-of-freedom robots.
Applications & Opportunities
• Industrial robotics — deterministic, high-speed motion planning for manipulators in manufacturing lines, enabling faster cycle times, predictable robot behavior, and reduced downtime.
• Autonomous mobile robots and drones — efficient path planning in cluttered, dynamic environments.
• Real-time robotics & embedded systems — robots with limited computational power benefit from the lower computational overhead and deterministic guarantees.
• High-Degree-Of-Freedom robots — complex manipulators, humanoids, or robots with many joints, where configuration spaces are high-dimensional.
• Safety-critical / certification-sensitive applications — sectors like aerospace, medical robotics, or autonomous vehicles, where reproducibility, reliability, and bounded performance are required for regulatory compliance.
Show Less
Business Development Contacts
