Tootfinder

Woa, just in time 😅
@… v0.18.0 just landed on pypi and conda-forge
For an overview of all new features and improvements, see
https://github.com/…

Release v0.18 · movingpandas/movingpandas
What's Changed New functionality Add TrajectoryCollection.get_trajectories(obj_id) in a7ef98e by @anitagraser Add obj weights to Aggregator by @Naarlack in #369 Additional trajectory splitter base...

Woohoo.
https://geeknews.chat/@theregister/112250715036548620

#Tesla #cybertruck

Tesla's Cybertruck may not be so stainless after all
Literally bulletproof' but needs constant cleaning to stave off corrosion

Several French municipal governments' services have been knocked offline following a "large-scale cyber attack" on their shared servers.
https://www.theregister.com/2024/04/12/french_municipalities_cyberattack/

Efficient and Guaranteed-Safe Non-Convex Trajectory Optimization with Constrained Diffusion Model
Anjian Li, Zihan Ding, Adji Bousso Dieng, Ryne Beeson
https://arxiv.org/abs/2403.05571

Efficient and Guaranteed-Safe Non-Convex Trajectory Optimization with Constrained Diffusion Model
Trajectory optimization in robotics poses a challenging non-convex problem due to complex dynamics and environmental settings. Traditional numerical optimization methods are time-consuming in finding feasible solutions, whereas data-driven approaches lack safety guarantees for the output trajectories. In this paper, we introduce a general and fully parallelizable framework that combines diffusion models and numerical solvers for non-convex trajectory optimization, ensuring both computational ef…

LeoLabs Raises $29 Million to Deliver Enhanced AI-powered Insights for Space Operations: #SpaceDebris with AI smarts: https://spacenews.com/leolabs-raises-29-million/ and https://www.theregister.com/2024/02/12/leolabs_investment/

#Tesla #cybertruck

Tesla's Cybertruck may not be so stainless after all
Literally bulletproof' but needs constant cleaning to stave off corrosion

Efficient and Guaranteed-Safe Non-Convex Trajectory Optimization with Constrained Diffusion Model
Anjian Li, Zihan Ding, Adji Bousso Dieng, Ryne Beeson
https://arxiv.org/abs/2403.05571

Efficient and Guaranteed-Safe Non-Convex Trajectory Optimization with Constrained Diffusion Model
Trajectory optimization in robotics poses a challenging non-convex problem due to complex dynamics and environmental settings. Traditional numerical optimization methods are time-consuming in finding feasible solutions, whereas data-driven approaches lack safety guarantees for the output trajectories. In this paper, we introduce a general and fully parallelizable framework that combines diffusion models and numerical solvers for non-convex trajectory optimization, ensuring both computational ef…

On the Performance of Jerk-Constrained Time-Optimal Trajectory Planning for Industrial Manipulators
Jee-eun Lee, Andrew Bylard, Robert Sun, Luis Sentis
https://arxiv.org/abs/2404.07889

On the Performance of Jerk-Constrained Time-Optimal Trajectory Planning for Industrial Manipulators
Jerk-constrained trajectories offer a wide range of advantages that collectively improve the performance of robotic systems, including increased energy efficiency, durability, and safety. In this paper, we present a novel approach to jerk-constrained time-optimal trajectory planning (TOTP), which follows a specified path while satisfying up to third-order constraints to ensure safety and smooth motion. One significant challenge in jerk-constrained TOTP is a non-convex formulation arising from t…

Model-Predictive Trajectory Generation for Autonomous Aerial Search and Coverage
Hugo Matias, Daniel Silvestre
https://arxiv.org/abs/2403.05944 https://

Model-Predictive Trajectory Generation for Autonomous Aerial Search and Coverage
This paper addresses the trajectory planning problem for search and coverage missions with an Unmanned Aerial Vehicle (UAV). The objective is to devise optimal coverage trajectories based on a utility map describing prior region information, assumed to be effectively approximated by a Gaussian Mixture Model (GMM). We introduce a Model Predictive Control (MPC) algorithm employing a relaxed formulation that promotes the exploration of the map by preventing the UAV from revisiting previously cover…