M4 Robot Fleet Management System

M4 Technical Architecture

M4 Technical Key Point

The backend core is based on the JVM (Java), with some high-performance components developed in Rust and C++. It is compatible with mainstream operating systems and supports the ARM architecture.

Through a self-developed persistence-layer engine, the system supports various mainstream databases. No need to manually maintain table structures, as the system automatically updates them at startup. The self-developed fully automatic caching layer intercepts over 90% of database requests, significantly reducing system overhead. Automatic cache clearance eliminates manual management, resulting in nearly zero bugs.

The frontend core technology framework is React. No third-party UI component libraries are used—all components are self-developed. It is fully adapted for industrial scenarios such as mobile devices, barcode scanning, and focus operations. It supports global zooming in and out to accommodate lower-quality industrial displays.

The core product and project code can be completely separated. It supports extended customization of backend logic and frontend business processes. The backend extends system functionality through Python scripts, while the frontend extends page functionality through JavaScript. All users share the same version of the core product while still being able to maintain their own customized business logic. Long-term users can more easily access the latest product feature updates.

Key system components, such as Falcon Task and Dispatch, feature crash recovery capabilities, remember execution progress, and resume from the point of interruption.

Open Source Soon Multi-Agent Path Finding (MAPF) Test Platform

MAPF DEV is a multi-agent path finding (MAPF) test platform independently developed by SEER. It will soon be open-sourced. The platform supports testing of multiple MAPF algorithms, including CBS, PBS, A*, and SIPP. It features map construction, parameter configuration, solving, simulation, import/export, solution analysis, and 3D visualization. It can rapidly simulate tests for hundreds of smart-agents (robots).

MAPF DEV Basic Function Demonstration

MAPF DEV Basic Function Demonstration

MAPF DEV fully covers the core requirements of the entire scheduling process scenario. It supports flexible adjustment of map size, node size, and obstacle ratio, allowing for quick environment setup through random generation or load/save functions. Meanwhile, it can realize robot generation, random task creation, and intelligent solving, providing multiple scheduling strategy options. Additionally, users can view the real-time operation process of solving simulations and gain in-depth insights into the underlying and high-level solving details, which helps them clearly grasp the scheduling logic and execution principles.

MAPF DEV Large-Scale Operation Live Demo

MAPF DEV Large-Scale Operation Live Demo

In complex scenarios with large map scales and extremely high obstacle densities, MAPF DEV still demonstrates an extremely fast solving speed. At the same time, it clearly shows the efficient avoidance process of robots in dense obstacle environments, fully verifying the system's stability, practicality, and strong adaptability in large-scale and high-complexity scheduling scenarios.

Academic Research

The multi-robot path planning problem, or multi-agent path planning problem, has been studied for many years. There are a large number of academic papers exploring solutions from various perspectives. Although these papers are still far from product-level functionality, they can serve as a starting point for related explorations. Here are some classic literatures on robot path planning.

Sharon, Guni, et al. "Conflict-based search for optimal multi-agent pathfinding." Artificial intelligence 219 (2015): 40-66.

Bnaya, Zahy, and Ariel Felner. "Conflict-oriented windowed hierarchical cooperative A." 2014 IEEE International Conference on Robotics and Automation (ICRA). IEEE, 2014.

Li, Jiaoyang, et al. "Lifelong multi-agent path finding in large-scale warehouses." Proceedings of the AAAI Conference on Artificial Intelligence. Vol. 35. No. 13. 2021.

Here are some key technical terms in the field.

Complete (Completeness): Refers to the property that an algorithm is guaranteed to find a solution when one exists. If an algorithm lacks completeness, it may return "no solution" even when a valid solution exists (i.e., the problem is solvable).

Optimal (Optimality): Means that an algorithm not only finds a solution but also ensures it is the optimal one (e.g., the solution with minimal cost, shortest path, etc.).

Bounded Sub-optimal: Allows solutions that are sub-optimal within a specified bound. For example, a solution with a cost no more than 1.2 times the optimal solution is considered bounded sub-optimal.

Lifelong: Refers to scenarios where an agent receives new goals after completing a previous one or handles multiple goals in sequence (e.g., first going to point A, then to point B).

One-shot: Contrasts with "lifelong," referring to a single planning task without subsequent or sequential goals.

Anytime: Applies to scenarios with limited time, where the algorithm first returns a sufficiently good solution within a specified time frame. If more time is available, it iteratively refines the solution to achieve better optimality (e.g., generating improved results through multiple iterations).

Online: Generally refers to planning while executing, rather than pausing the robot during planning, executing the planned path, and then pausing again for subsequent planning.

Offline: Generally refers to executing a path only after complete planning is finished.

Roadmap: A topological graph of connected paths, distinct from grid or chessboard-style maps.

Dynamic: Note that in this context, it typically refers to "dynamics" rather than "dynamic environments." For example, "two-order dynamic" indicates that a robot cannot accelerate or decelerate infinitely (i.e., it cannot instantaneously move at a specified speed or stop abruptly).