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Nov 5Thu
  1. Guyuehome42

    In-Depth Analysis of ROS 2 tf2 Coordinate Transforms: Coordinate Trees, Static Extrinsics, and RViz Misalignment Troubleshooting

    For ROS 2 tf2 coordinate transforms, this content analyzes the coordinate tree structure, static extrinsic configuration, and troubleshooting of RViz display misalignment. The original text does not provide a specific robot model, version number, or experimental data; it only explains the use of tf2 and the localization of common issues from a conceptual and debugging perspective.

  3. Guyuehome35

    In-Depth Analysis of CAN Bus DBC Signal Encoding and Decoding: Start Bits, Byte Order, and Physical Value Conversion

    The article analyzes signal encoding and decoding in CAN bus DBC files, focusing on the correspondence among start bits, byte order, and physical value conversion. It is organized around DBC signal definitions to help readers understand signal positions within messages and how raw values are converted to physical values.

  4. Reddit r/robotics22

    SmartGrid Mesh Flow feature: How to use vector fields to generate adaptive spray trajectories on 3D mesh surfaces

    A developer is seeking mathematical algorithm advice for the Mesh Flow feature of SmartGrid, an offline robotic spray trajectory planner, with the goal of generating and maintaining consistent trajectory patterns across different 3D mesh geometries. Their concerns include using vector fields to determine travel direction, controlling path spacing with geodesic distance fields or surface offsets, and letting local normals, tangent vectors, and curvature influence trajectories while suppressing jitter caused by mesh irregularities. The current focus is on deriving a general geometric algorithm that can reproduce reference patterns, rather than on programming implementation.

  6. Guyuehome58

    OpenFleX: Omnidirectional Mobility and Dual-Arm Coordinated Manipulation Control for a Wheeled Humanoid Robot

    The OpenFleX wheeled humanoid robot reproduces omnidirectional chassis kinematics, damped least-squares inverse kinematics, and whole-body redundant coordinated control in MuJoCo. A single end-effector motion can be achieved through various combinations of chassis, lift, and dual-arm degrees of freedom; the experiments compare pseudoinverse and damped least-squares performance near singular configurations.

  7. Guyuehome58

    Dexterous Hand Practice 13: Using Polynomial Trajectory Planning to Eliminate Position Jumps and Achieve Smooth Motion

    The 13th post in Gu Yue Ju's "Dexterous Hand Practice" series explains how to use polynomial trajectory planning to turn position jumps in a dexterous hand into smooth motion. The material provides only the title and summary; it does not give specific polynomial orders, parameters, or experimental data.

Nov 4Wed
  1. 深蓝AI · 微信公众号60

    Understanding RRT and RRT*: How Random Sampling Helps Robots Find a Path, and What Makes the Path Shorter

    Deep Blue AI's "Algorithm Explained" column published an article explaining the two sampling-based planning algorithms RRT and RRT*: RRT uses random sampling, finding the nearest node, expanding toward the sampled direction, and collision detection to grow a tree from the start toward the goal, addressing the difficulty of enumerating high-dimensional configuration spaces in advance.

  4. Guyuehome22

    Zephyr System Tracing and Performance Analysis in Practice: Thread Statistics, CTF Tracing, and Overhead Measurement

    This practical guide to Zephyr system tracing and performance analysis covers three topics: thread statistics, CTF tracing, and overhead measurement. The article discusses Zephyr's thread runtime statistics, CTF-format trace collection, and methods for measuring the overhead of the tracing mechanism itself, aimed at developers who need to diagnose real-time and scheduling issues.

  5. Guyuehome30

    Advanced Zephyr Builds: sysbuild, Multi-Image, and Modular Engineering

    This advanced Zephyr build content focuses on three build mechanisms: sysbuild, multi-image, and modular engineering. sysbuild is used to manage multi-image builds in a unified way, while multi-image and modular engineering address the splitting and organization of complex firmware. The original text does not provide specific versions, example projects, or measured data.

  7. Guyuehome29

    crocoddyl: Feasibility-Driven Differential Dynamic Programming for Optimal Control

    Reproduced the core mathematical formulation of the crocoddyl solver isomorphically: Riccati backward sweep, LM regularization, and gap contraction expansion. On a large-amplitude cart-pole swing-up task, a three-way comparison was conducted; FeasShoot required fewer than 30% of the iterations of the shooting method, and regularization remained frozen.

  8. Guyuehome58

    How DiffTaichi's differentiable physics simulation uses gradients to drive control optimization

    The article analyzes the adjoint gradient mechanism of DiffTaichi's differentiable physics simulation, using loss gradients to directly optimize actuator control sequences in material point method soft body, mass-spring, and fluid scenarios. It shows that gradient methods can optimize soft-body motion forms that reinforcement-learning-style sampling methods struggle to reach.

  9. Guyuehome60

    Reproducing PPO Reinforcement Learning-Based Quadrotor Racing Flight Control

    The author loads an open-source pretrained policy in Isaac Sim to reproduce quadrotor racing on a figure-8 track, matching the official result with 25 gates and 3.12 laps. The content breaks down the three attitude phases of high-speed turns and uses a three-seed controlled experiment to test the real benefits of domain randomization.

  10. Guyuehome38

    Dexterous Hand Practice 12: Where the Fingertip Should Go and How to Solve Joint Angles via Inverse Kinematics

    The 12th installment of the Dexterous Hand Practice series focuses on solving inverse kinematics: first determine the target position of the fingertip, then solve backward for each joint angle. The content is tutorial in nature, centered on the correspondence between fingertip targets and joint angles, and does not involve specific robot models or experimental data.

  12. 锐曼REEMAN38

    Planning a multi-floor autonomous forklift project? REEMAN Ironhide elevator integration checklist

    From a supplier perspective, REEMAN provides a five-item checklist of mechanical prerequisites, communication architecture, and responsibility division for autonomous forklift and elevator integration. For a single elevator, a local LoRa direct connection to the elevator control module is used; for multiple elevators, MQTT is used with the fleet system queuing and dispatching. The elevator control module only coordinates calls and status and does not replace the elevator's native safety controls. In the Maoming factory case, Ironhide transports about 1,000 kg of wire rod per trip over a one-way distance of about 100 m, completing 65–85 cross-floor elevator tasks per day.

  13. Guyuehome38

    ROS Secondary Development and Navigation Deployment Guide Based on the Ruiman Wind Fire Wheel (WBOT11B) Chassis

    The Ruiman Wind Fire Wheel (WBOT11B) chassis has an open-source SDK and API, and can be used for mobile robot algorithm validation in university laboratories and MVP development of commercial service robots for enterprises, eliminating the work of building low-level drivers and motion control from scratch. This content takes ROS secondary development and navigation deployment as its thread and provides a deployment guide for the above scenarios.

  14. 锐曼REEMAN40

    REEMAN autonomous forklift and stretch wrapper integration: task signals, pallet handover, and docking conditions

    REEMAN autonomous forklifts perform a task-level handshake with turntable stretch wrappers via an external wireless module: the forklift places the pallet and, after retreating from the rotating area, sends a start signal; the wrapper completes rotary film wrapping and automatic film cutting, then returns a completion signal, after which the forklift re-identifies and aligns to pick up the load.

Nov 3Tue
  1. Guyuehome58

    Reproducing Deep Reinforcement Learning Control for In-Hand Object Reorientation with the Revo3 Dexterous Hand

    This article reproduces three official pretrained policies for the Revo3 dexterous hand in Isaac Lab for in-hand object reorientation control. The cube random-target reorientation runs 8 times per episode, while the cylinder uses a 22.5° incremental chain, advancing once every 3.4 steps.

  6. Guyuehome40

    Retrospective on Brownfield Line Retrofits: Analyzing Integration Challenges of AMRs Interfacing with Industrial Doors and Stretch Wrappers

    The Ruiman Ironhide3.0 autonomous forklift has validated the feasibility of interworking with heterogeneous equipment such as industrial doors and stretch wrappers in brownfield production line retrofits. The article reviews the challenges encountered during integration and commissioning, focusing on coordination issues between autonomous forklifts and existing production line equipment.

  7. Guyuehome40

    Deep Vision Reinforcement Learning for Autonomous Quadrotor Flight: AirGym Control Hierarchies and Depth Observation Design Overview

    For quadrotor obstacle avoidance, AirGym incorporates depth perception, low-level control, and sensor noise into a single training pipeline, and outlines five control hierarchies, depth observation designs, and reward structures. The article also records the engineering finding that Blackwell GPUs cannot run Isaac Gym.

  8. Guyuehome47

    [Flexible Simulation] Dynamic unfolding control based on MuJoCo cloth dynamics

    Based on MuJoCo cloth dynamics, twelve controlled experiments were completed using realistic shell-element simulation to study differences in cloth unfolding under the same gripping trajectory. Flicking unfolds the cloth through inertia; the slow setting spreads it most fully, while too fast a motion flings it off the table edge. Unfolding performance is insensitive to stiffness, and overdamping instead produces creep.

  9. Guyuehome58

    python-control: State-Space Design and the Physical Closed Loop of an Inverted Pendulum

    Using python-control as the vehicle, the article breaks down state-space control design, covering controllability and observability criteria, pole placement, the algebraic Riccati solution for LQR, the dual observer, and frequency-domain margins. It also deploys discrete LQR and an energy-based swing-up strategy into a MuJoCo cart-inverted-pendulum closed loop.

  10. Guyuehome58

    Analysis of GTSAM Factor Graph Smoothing and Incremental Pose Estimation

    The article analyzes GTSAM factor graph state estimation, introduces how pose graph and IMU preintegration factors are constructed, and conducts a comparison experiment between batch LM and iSAM2 incremental solving. It also analyzes the process by which loop closure detection triggers global correction, as well as numerical validation of incremental inference on the Bayes tree.

  11. Guyuehome58

    Dexterous Hand in Practice 07: Connecting CAN Bus to a Single Finger, Explaining Protocol, Wiring, and Transceiving

    Gu Yue Ju (Ancient Moon Residence) publishes the seventh installment of the Dexterous Hand in Practice series, themed on connecting the CAN bus to a single finger and covering three parts: protocol, wiring, and transceiving. The material provides only the title and summary, without specific protocol details, wiring schemes, or transceiving implementation.

  13. Guyuehome37

    Dexterous Hand in Practice 10: Coordinate Transforms and Collisions—First Put Every Finger on the Same Map

    The 10th installment of the Dexterous Hand in Practice series focuses on coordinate transforms and collision detection, with the goal of unifying each finger onto the same map. The content covers a unified representation of multi-finger coordinate frames and collision handling, and is one part of this hands-on tutorial series.

  14. Guyuehome45

    Dexterous Hand in Practice 08: Making a Single Joint Obedient — How to Implement Position PID Closed-Loop Control

    Dexterous Hand in Practice 08 focuses on single-joint control, explaining how to implement position PID closed-loop control so that a single joint moves stably according to commands. It is part of a hands-on tutorial series aimed at the implementation process of joint-level control for dexterous hands.

  15. Guyuehome40

    Dexterous Hand in Practice 06: STM32, ESP32, and Raspberry Pi — How the Control Boards Divide the Work

    Part 06 of the Dexterous Hand in Practice series discusses how STM32, ESP32, and Raspberry Pi divide responsibilities in dexterous hand control. The content centers on the roles each of these three types of control boards plays, and it is a tutorial-style article in this hands-on series.