For potential field methods, the strongest matches are ros-planning/navigation2 (Navigation2 is the industry-standard framework for ROS 2 autonomous), ai-winter/python_motion_planning (This library provides a comprehensive collection of motion planning) and ros-navigation/navigation2 (Navigation2 is the industry-standard framework for ROS 2 robotics). ai-winter/ros_motion_planning and zhm-real/pathplanning round out the shortlist. Each is ranked by relevance to your query, popularity and recent activity.
Explore the best open-source potential field method libraries. Compare top-rated tools by activity and features to find the best fit for your project.
Navigation2 is a navigation stack for ROS 2 designed for autonomous robot navigation. It provides a framework for computing optimal paths from a starting position to a goal while avoiding static and dynamic obstacles. The system utilizes a behavior tree orchestrator to coordinate complex navigation tasks and trigger recovery actions. Its architecture is plugin-based, allowing planners, controllers, and costmap layers to be swapped at runtime without recompiling the core system. The project covers path planning, motion control, and environmental mapping. It generates occupancy grids and costm
Navigation2 is the industry-standard framework for ROS 2 autonomous navigation that natively implements costmap-based obstacle avoidance and various path planning algorithms, making it a comprehensive solution for your robotics requirements.
This project is a comprehensive software framework for autonomous robot navigation, providing a collection of algorithms for path planning, motion control, and trajectory generation. It serves as a toolkit for implementing and researching navigation logic, enabling the calculation of collision-free routes and the execution of precise movement commands for autonomous mobile agents. The library distinguishes itself by integrating both global pathfinding and real-time reactive control strategies. It supports diverse planning methodologies, including graph-based heuristic searches and sampling-ba
This library provides a comprehensive collection of motion planning algorithms, including artificial potential fields, specifically designed for autonomous robot navigation and obstacle avoidance.
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
Navigation2 is the industry-standard framework for ROS 2 robotics navigation, providing the modular architecture and plugin system necessary to implement potential field algorithms for real-time path planning and obstacle avoidance.
This project is a comprehensive library and framework for autonomous mobile robot navigation, providing a suite of geometric, heuristic, and optimization-based algorithms. It enables robots to calculate collision-free global paths and generate smooth, kinematically feasible local trajectories within complex environments. The system is built on a modular plugin architecture that allows developers to integrate and configure custom motion planning algorithms directly into the Robot Operating System. By utilizing a layered spatial representation, the framework aggregates heterogeneous sensor data
This library provides a modular framework for autonomous robot navigation that explicitly includes artificial potential field algorithms alongside various other path planning and obstacle avoidance techniques within a ROS-integrated architecture.
PathPlanning is a library of animated path planning algorithms that includes implementations of A-star, Dijkstra, RRT, and spline-based trajectory generation for both 2D and 3D environments. The project provides a collection of motion planning algorithms that demonstrate how robots can find collision-free paths through continuous spaces, with each algorithm rendered as a step-by-step visual animation to show how the search or tree grows over time. The library covers three main categories of path planning: sampling-based methods like RRT, RRT-star, and BIT-star that grow trees by randomly samp
This library provides a comprehensive collection of path planning and motion planning algorithms for robotics, though it focuses on sampling-based and graph-search methods rather than the specific potential field algorithms requested.
PythonRobotics is a comprehensive collection of modular robotics algorithms and educational simulations designed for autonomous navigation, state estimation, and motion control. The project provides a library of standalone implementations for path planning, localization, mapping, and kinematics, serving as a resource for researchers and students to experiment with foundational and advanced robotic theories. The project distinguishes itself through an algorithm-centric design where each module functions as an isolated script, allowing for independent testing and clear pedagogical demonstration
This repository provides a comprehensive collection of modular path planning implementations, including specific modules for artificial potential field algorithms and obstacle avoidance, making it a highly relevant resource for robotics navigation research.
The Robotics Library (RL) is a self-contained C++ library for rigid body kinematics and dynamics, motion planning, and control.
This library provides a comprehensive suite of motion planning and collision detection tools for robotics, though it focuses on broader kinematics and dynamics rather than specifically implementing potential field algorithms as a primary feature.
This library is a comprehensive toolkit for autonomous robot navigation and multi-agent motion coordination. It provides a framework for calculating collision-free movement trajectories, enabling multiple robots to operate within shared environments while maintaining efficient and safe paths. The project distinguishes itself by supporting both global and decentralized control strategies. It offers global coordination techniques that resolve path conflicts across entire workspaces to ensure unified group movement, alongside decentralized methods that allow individual agents to react dynamicall
This repository provides a collection of multi-agent path-planning algorithms, including velocity obstacles, which directly addresses the core requirement for robotic navigation and obstacle avoidance frameworks.
MotionPlanning is a software library designed for autonomous navigation, providing a comprehensive suite of tools for path planning, trajectory generation, and vehicle control. It enables the calculation of collision-free routes and dynamic movement paths for autonomous vehicles operating in complex, changing environments. The project distinguishes itself by integrating hierarchical motion decomposition, which separates high-level route planning from low-level trajectory generation to manage computational complexity. It employs kinematic bicycle modeling and trailer dynamics simulation to ens
This library provides a collection of motion planning and tracking algorithms for autonomous vehicles, including state-lattice and hybrid-astar planners that are commonly used for obstacle-aware navigation, though it lacks a specific implementation of potential field algorithms.