#MotionPlanning

GetNews.me

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5 months ago

Efficient Probabilistic Planning with Robust Reachable Trees

Two multi‑query motion‑planning algorithms for linear Gaussian systems guarantee reachability to a Euclidean ball and an ellipsoid‑plus‑ball region, improving coverage. getnews.me/efficient-probabilistic-... #probabilisticplanning #motionplanning

GetNews.me

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5 months ago

Shape‑Space Graphs Enable Fast Collision‑Free Planning for Soft Robots

A graph‑based planner for a soft robotic arm with three artificial muscle fibers can produce collision‑free, energy‑aware paths in just a few milliseconds. Read more: getnews.me/shape-space-graphs-enabl... #softrobots #motionplanning

GetNews.me

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5 months ago

Fast Motion Planning Using Point Cloud Diffusion and Potential Fields

A new framework fuses diffusion models with artificial potential fields for real‑time motion planning from raw point clouds, succeeding in pursuit‑evasion tests. Read more: getnews.me/fast-motion-planning-usi... #motionplanning #pointcloud

GetNews.me

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5 months ago

New Motion Planning Approach Cuts Torque in Wearable Robotic Limbs

A new motion-planning layer for wearable robotic limbs enforces angular-acceleration and position-deviation limits, cutting peak torque in simulations. Read more: getnews.me/new-motion-planning-appr... #wearablerobotics #motionplanning

IEEE Transactions on Robotics (T-RO)

@ieeetro.bsky.social

9 months ago

There are 15 demonstration trajectories (red, green, and blue trajectories) that travel from the start to the goal, avoiding the obstacle. Middle and Right: MMP++ and IMMP++ learn 2-D manifolds in the curve parameter space and produce 2-D latent coordinate spaces. Latent values of the demonstration trajectories are visualized in the latent coordinate spaces, marked as ×. GMMs of three components are fitted in the latent spaces, and the sampled points are visualized as stars ∗. The corresponding generated trajectories are also visualized.

This T-RO paper presenting at ‪#ICRA2025 introduces a method integrating parametric curves into Motion Manifold Primitives. This approach ensures isometric latent spaces for better performance & online adaptation.
ieeexplore.ieee.org/document/106...

#Robotics #MotionPlanning #AI #IEEEras

AZoAI

@azoai.bsky.social

1 year ago

GPD-1: The Next Leap in Autonomous Driving Technology Explore GPD-1's transformative approach to motion planning and traffic simulation for smarter vehicles.

GPD-1: The Next Leap in Autonomous Driving Technology 🚗🤖✨ www.azoai.com/news/2025010... #AutonomousDriving #AI #MachineLearning #GenerativeModels #TrafficSimulation #MotionPlanning #SmartTechnology #FutureMobility #AI #Research @arxiv-stat-ml.bsky.social

Gerard Canal

@grarknal.bsky.social

5 years ago

Motion planning explanations Estimated time to complete: 25 minutes The goal of this questionnaire is to understand current issues with the use of motion planners, as well as the potential role that planner-generated explanations could have in dealing with these issues. The questionnaire has 5 parts: 1. Questions about yourself and your experience with planners 2. Questions about issues of failure to find feasible solutions 3. Questions about issues of unexpected motion 4. Your feedback 5. Evaluating examples of explanations Your input will be anonymized. The authors of this questionnaire are: Martim Brandao, Gerard Canal, Senka Krivic (King's College London) In case of concerns regarding the questionnaire and/or data protection, please contact martim.brandao@kcl.ac.uk

We are looking for #MotionPlanning users to answer a small survey on understanding and explanation of motion plans. Can you help? @MartimBrandao @senka_snow @rosorg @OpenRoboticsOrg @ROSIndustrial @PickNikRobotics @_TheConstruct_ #moveit #goros https://t.co/dR4kicHfC1