Modular Blueprint for RLM Construction

• Modular RLM construction is a systematic approach using standardized mechanical, electronic, and software modules to enable rapid reconfiguration for diverse field tasks.
• It employs an automated discovery pipeline that updates kinematic models in real time, ensuring seamless assembly and deployment via EtherCAT and URDF/SRDF generation.
• The blueprint, exemplified by the CONCERT system, has been validated in tasks like drilling, spraying, sanding, and collaborative transport, highlighting its practical field performance.

A modular blueprint for RLM (Reconfigurable Construction Robot) construction elucidates a thoroughly decomposed, engineering-validated workflow for assembling, configuring, and operating construction robots with reconfigurable kinematic and task structures. Such blueprints, exemplified by the “CONCERT” system, formalize the modular mechanical, electronic, and software design principles enabling rapid task-driven morphology changes, instant software model adaptation, and field robustness suitable for real on-site deployment (Rossini et al., 7 Apr 2025).

1. Mechanical Architecture and Module Taxonomy

At the core of RLM construction lies a tightly constrained catalog of standardized mechanical/electromechanical modules. Each module type—active (motorized joint), passive (rigid link), hub (branch/interface), or end-effector (tool)—is defined precisely in terms of function, interface geometry, and real-time communication/power protocol. The electro-mechanical interface (EMI) is universal, providing a quick-lock, 48V DC power, and an EtherCAT bus for real-time module discovery and control.

Module Taxonomy

Module Type	Description / Key Specs	Connectivity
Active joint	Elbow (pitch, axis ⟂ EMI), Straight (yaw, axis ∥ EMI), Steering+Wheel	Input/Output EMI (linear chain/leg), torque+speed variant
Passive link	Rigid straight (0.3/0.4m), angle (45°, 90°), passive base (0.6m elev.)	Input/Output EMI
Hub	Mobile Base (5 ports/legs+arm/compute/sensor), Torso (3 ports, branch)	Multi-port, includes compute, batteries, sensors
End-effector	Drill (with depth camera), spray tool, sander, active/pas gripper	Single-port, swappable distal tool

Module selection and ordering determines workspace, kinematics, and capacity. Morphologies can be reconfigured in field conditions in under ten minutes, with mechanical operations per module taking ≈40 s for mounting and ≈30 s for unmounting. The “Anthropomorphic Arm” canonical assembly leverages a 6-DOF (e.g., ElbowA→StraightA→ElbowA→StraightB→[Passive]→ElbowB→Tool) sequence, while mobile platforms use steering+wheel at hub legs for omnidirectionality (Rossini et al., 7 Apr 2025).

2. Real-Time Kinematic and Dynamic Model Updating

A distinctive element of the RLM blueprint is software-driven, hardware-reflective auto-updating of the robot’s kinematic/dynamic model with each physical reconfiguration. Each module encodes in its descriptor the required transformation matrices, joint types, and inertia parameters.

• Every EMI defines an input and output frame; each joint’s coordinate is parameterized as a twist in SE(3), with transformations computed as

$$X_{\text{parent}\rightarrow k}(q_k) = T_{\text{in}}^{\text{parent}} \cdot \exp([s_k] q_k) \cdot T_{\text{out}}^k$$

where $[s_k]$ encodes the joint twist and the exponentiation is performed in SE(3).

• Spatial inertias, center-of-mass, and module-specific parameters are drawn from a database upon EtherCAT scan.
• The full robot URDF is constructed 1:1 from a graph expansion over the networked modules, with downstream update of SRDF (semantic groups, end-effectors), and can be parsed by kinematic/dynamic libraries such as Pinocchio or RBDL for real-time Jacobian and mass matrix computation (Rossini et al., 7 Apr 2025).

3. Automated Discovery and Deployment Pipeline

Deployment after reconfiguration is fully modularized, with an explicit algorithmic pipeline:

1. EtherCAT network topology scan: Discovers all connected modules and their linkage.
2. Module property retrieval: Extracts module ID and descriptor, including transform, mass, CoM, inertia, and port data.
3. Physical graph construction: Expands network graph into a physical assembly of bodies and joints; links modules by applying stored transforms.
4. URDF/SRDF generation: Encodes the chain into standard robot description formats, ensuring syntactic congruence for motion planning/control middleware.
5. Controller (XBot2) launch: Activates robot with fresh model, enabling immediate motion planning and task execution (Rossini et al., 7 Apr 2025).

This makes it possible to change the robot’s physical configuration and execute new task behaviors within minutes, with negligible software downtime (≈5 s for model scan/update, ≈30 s for user additions/homing, ≈25 s for control bring-up).

4. Morphology Flexibility, Validation, and Extension

The modular RLM blueprint is rigorously validated for field scenarios. Rapidly reconfigurable assemblies have been proven on:

• Autonomous drilling (multi-height, visual alignment, real-time RRT-Connect planning with posture redundancy)
• Insulation spraying (trajectory following, wall distance feedback)
• Dry-wall sanding (cartesian impedance control, force setpoint at 30 N)
• Collaborative transport (human-following by end-effector force sensing mapped to MPC, gravity-compensated teach mode)

Morphological flexibility is extendable: hub modules enable branching (dual arms, humanoid); modules scale up (hydraulic/power-class for high payloads), passive link variants yield curved/reachable workspace, and integrating advanced distal sensors (SLAM LiDAR) directly at the tool increases task precision (Rossini et al., 7 Apr 2025).

5. Lessons, Limitations, and Future Extension

Field deployments highlight core extension guidelines and critical engineering observations:

• Mechanical tolerances in EMI induce backlash at long chains; this is mitigated by empirical calibration per pose and chain length, with corrective offset mapping.
• User interfaces: Web-based GUIs for topology scan and manual sensor “addon” registration are essential for field usability.
• Scalability: The approach supports quick integration of novel modules (custom joint/link/end-effector types), and roadmap items include morphology optimization via genetic search for task metrics (e.g., joint torque, execution time), and standards for open modular interop across vendors (Rossini et al., 7 Apr 2025).
• Fully automated morphology designers (potentially VR-based), human–robot and multi-robot collaboration, and universal “open” standards for third-party module compatibility are active directions.

6. Comparative and Methodological Context

The modular blueprint for RLM shares theoretical themes with broader modular robotics and automation paradigms:

• Vision-based approaches allow for generic, AR-marker-driven model reconstruction, primarily where EMI/fieldbus self-ID is absent (Lin et al., 2017), but mechanical–electrical coded EMI as in CONCERT enables more robust, direct auto-ID and modeling.
• Modularization at the mechanical/electrical level complements recent trends in compositional deep learning and function block abstraction in cognitive robotics.
• Task-driven assembly can be algorithmically optimized using functional metrics and search strategies, allowing reconfigurable construction robots to be rapidly repurposed for new tasks and environments.

Through the modular blueprint, construction robots approach the flexibility and redeployability seen in software-defined automation, but with mechanical, electrical, and software layers co-designed for on-site reliability and minimal downtime (Rossini et al., 7 Apr 2025).