A fast trajectory optimization library written in Julia
Principalele funcționalități ale roboticexplorationlab/trajectoryoptimization.jl sunt: Motion Planning.
Alternativele open-source pentru roboticexplorationlab/trajectoryoptimization.jl includ: ros-navigation/navigation2 — Navigation2 is a ROS 2 navigation framework for autonomous mobile robots. It provides the core identity of a path… pcrnjak/parol6-desktop-robot-arm — This project provides a complete set of design files and hardware specifications for a 3D-printable industrial-style… opendrivelab/uniad — UniAD is a unified deep learning framework for autonomous driving that integrates perception, prediction, and planning… ethz-adrl/towr — A light-weight, Eigen-based C++ library for trajectory optimization for legged robots. gtrll/gpmp2 — Gaussian Process Motion Planner 2. fields2cover/fields2cover — Robust and efficient coverage paths for autonomous agricultural vehicles. A modular and extensible Coverage Path…
This project provides a complete set of design files and hardware specifications for a 3D-printable industrial-style robot arm. The system includes a CAN bus robot controller for managing stepper motors and sensors, a kinematics engine for calculating joint angles and poses, and a UDP-based Python API for sending motion commands and monitoring telemetry. The system features a force-controlled robotic gripper that utilizes field-oriented control on stepper motors to enable compliant grasping and precise force sensing. It also includes a 3D position visualization tool for real-time telemetry tr
Navigation2 is a ROS 2 navigation framework for autonomous mobile robots. It provides the core identity of a path planner, costmap management system, kinematic motion controller, and behavior tree orchestrator to compute collision-free routes and execute movement commands. The framework is distinguished by its use of behavior trees to coordinate modular task servers, enabling complex navigation routines and autonomous recovery actions. It supports a plugin-based architecture that allows planners and controllers to be swapped at runtime to adapt to different environments. The system covers a
UniAD is a unified deep learning framework for autonomous driving that integrates perception, prediction, and planning into a single end-to-end model. It functions as a neural network architecture that maps raw sensor data directly to driving trajectories and motion plans. This project serves as a research implementation of a planning-oriented approach that jointly trains occupancy, mapping, and object tracking modules. It employs a multi-task perception framework to optimize overall driving performance. The system covers a broad capability surface including end-to-end driving pipelines, veh
A light-weight, Eigen-based C++ library for trajectory optimization for legged robots.