Pith. sign in

REVIEW 4 cited by

RotorPy: A Python-based Multirotor Simulator with Aerodynamics for Education and Research

Not yet reviewed by Pith; the record is open.

Re-run · record.json Download PDF Read on arXiv ↗

This paper has not been read by Pith yet. Machine review is queued; the pith claim, tier, and objections will appear here once it completes.

SPECIMEN: schema-true, not a live event

T0 review · schema-true

One-sentence machine reading of the paper's core claim.

pith:XXXXXXXX · record.json · timestamp

arxiv 2306.04485 v1 pith:5JTKTKTU submitted 2023-06-07 cs.RO

Spencer Folk , James Paulos , Vijay Kumar This is my paper · ORCID
classification cs.RO
keywords rotorpyaerialdynamicsmultirotorroboticssimulatorwindaccessible
verification ladder T0 review T1 audit T2 compute T3 formal
0 comments
read the original abstract

Simulators play a critical role in aerial robotics both in and out of the classroom. We present RotorPy, a simulation environment written entirely in Python intentionally designed to be a lightweight and accessible tool for robotics students and researchers alike to probe concepts in estimation, planning, and control for aerial robots. RotorPy simulates the 6-DoF dynamics of a multirotor robot including aerodynamic wrenches, obstacles, actuator dynamics and saturation, realistic sensors, and wind models. This work describes the modeling choices for RotorPy, benchmark testing against real data, and a case study using the simulator to design and evaluate a model-based wind estimator.

Discussion (0). Continue with ORCID to comment.

Forward citations

Cited by 4 Pith papers

Reviewed papers in the Pith corpus that reference this work. Sorted by Pith novelty score. Full citation record

  1. Learning Smooth SE(3) Trajectories under Left-Invariant Riemannian Metrics

    cs.RO 2026-08 conditional novelty 6.0 of 10

    A metric-conditioned network with analytic boundary completion generates smooth SE(3) trajectories in about a millisecond, approximating variational optima under left-invariant Riemannian metrics.

  2. Formal Semantics for Agentic Tool Protocols: A Process Calculus Approach

    cs.AI 2026-03 unverdicted novelty 6.0 of 10

    SGD and MCP are claimed structurally bisimilar; reverse mapping is lossy; MCP+ with four type extensions is claimed fully equivalent to SGD.

  3. Neurosim: A Fast Simulator for Neuromorphic Robot Perception

    cs.RO 2026-02 conditional novelty 6.0 of 10

    Neurosim simulates event cameras and other robot sensors at multi-kilohertz rates on a GPU and streams the data to training and control pipelines without touching disk.

  4. Sequence Modeling for Time-Optimal Quadrotor Trajectory Optimization with Sampling-based Robustness Analysis

    cs.RO 2025-06 conditional novelty 6.0 of 10

    An LSTM trained on TOPPQuad data predicts speed and yaw profiles that reconstruct near-time-optimal quadrotor trajectories with two orders of magnitude speedup and hardware-validated tracking.

Pith tools