repository·master·Indexed 12 days ago
https://github.com/atsushisakai/pythonroboticsA comprehensive collection of Python implementations and an accompanying textbook for robotics algorithms. It covers localization (EKF, Particle Filter), mapping, SLAM (FastSLAM, ICP), path planning (A*, Dijkstra, RRT*, PRM), path tracking (MPC, LQR, Stanley), and specialized navigation for robotic arms, aerial vehicles, and bipedal robots. Built for Python 3.13.x using NumPy, SciPy, Matplotlib, and cvxpy.
Localization is the process of determining a robot's position and orientation using sensors like GNSS. This repository provides implementations of various Bayesian filters used for localization, including:
Path tracking enables a robot to follow a reference path generated by a path planner while stabilizing the robot. This process often accounts for modeling errors and uncertainty using feedback control or optimization-based control techniques.
PythonRobotics provides several implementations of path tracking controllers, including:
PythonRobotics focuses on algorithms rather than hardware, this section provides a conceptual overview of the external sensors used to feed data into robotic algorithms. External recognition (understanding surroundings, identifying safe zones, and detecting moving objects) depends on selecting the appropriate sensor based on the environment and required precision.The pythonrobotics repository provides several implementations of Simultaneous Localization and Mapping (SLAM). SLAM is the process of estimating a robot's pose and the environment's map simultaneously. Because mapping requires localization and localization requires a map, it is often treated as a 'chicken-and-egg' problem.
Available SLAM implementation modules in this repository include:
PythonRobotics focuses on algorithms rather than hardware, understanding these sensors is essential for implementing algorithms that process their feedback (e.g., orientation, acceleration, altitude, and temperature).The Path Planning module in PythonRobotics provides implementations for searching feasible and efficient paths to a goal while satisfying robot motion constraints and avoiding obstacles. The module includes two primary categories of algorithms:
Users can explore specific implementations by navigating to the individual sub-directories for each algorithm listed in the module's table of contents.
This module implements object shape recognition by fitting rectangles to range data (e.g., from laser scanners). The algorithm is based on the paper "Efficient L-Shape Fitting for Vehicle Detection Using Laser Scanners" from Carnegie Mellon University. It works in two primary stages:
Adaptive range segmentation: Range data points are clustered based on their proximity. To account for the sparser data distribution at greater distances, the distance threshold $r_{th}$ is calculated adaptively using the formula: $r_{th} = R_0 + R_d * r_{origin}$ where $R_0$ and $R_d$ are constant parameters and $r_{origin}$ is the distance from the sensor.
Rectangle search: For each cluster, the algorithm rotates the data at specific angle intervals and evaluates the fit using one of three criteria to find the best rectangle shape.
The PythonRobotics project is specifically focused on the Software & Algorithms component of robotics. It provides implementations for core robotic logic such as:
It is intended for developers interested in the algorithmic side of robotics rather than the hardware design of sensors or actuators.
PythonRobotics is an open-source collection of robotics algorithms implemented in Python. The project is designed for beginners to understand the core ideas behind practical algorithms used in both academia and industry.
Key features include:
Matplotlib to provide intuitive animations that demonstrate algorithm behavior over time.