[Paper Review] SMORES-EP, a Modular Robot with Parallel Self-assembly

Chao Liu, Qian Lin+2 more|arXiv (Cornell University)|Apr 1, 2021

Modular Robots and Swarm Intelligence11 references4 citations

TL;DR

This paper presents a parallel self-assembly framework for SMORES-EP modular robots that enables efficient, robust construction of kinematic chain topologies through optimized module assignment and concurrent docking actions. The hybrid control architecture—distributed for low-level control and centralized for high-level planning—demonstrates successful hardware and simulation-based self-assembly with guaranteed docking success and significant speedup via parallel execution.

ABSTRACT

Self-assembly of modular robotic systems enables the construction of complex robotic configurations to adapt to different tasks. This paper presents a framework for SMORES types of modular robots to efficiently self-assemble into tree topologies. These modular robots form kinematic chains that have been shown to be capable of a large variety of manipulation and locomotion tasks, yet they can reconfigure using a mobile reconfiguration. A desired kinematic topology can be mapped onto a planar pattern with optimal module assignment based on the modules' locations, then the mobile reconfiguration assembly process can be executed in parallel. A docking controller is developed to guarantee the success of docking processes. A hybrid control architecture is designed to handle a large number of modules and complex behaviors of each individual, and achieve efficient and robust self-assembly actions. The framework is demonstrated in both hardware and simulation on the SMORES-EP platform.

Motivation & Objective

• To address the challenge of efficiently self-assembling modular robots into complex, task-specific kinematic topologies.
• To enable parallel execution of docking actions to reduce assembly time in large modular robot systems.
• To ensure reliable and accurate docking despite physical constraints and hardware limitations.
• To develop a scalable control architecture that balances distributed execution with centralized planning for complex reconfiguration tasks.
• To demonstrate the framework’s effectiveness through hardware and simulation experiments on the SMORES-EP platform.

Proposed method

• A task assignment problem is solved to optimally map existing modules to target configuration positions, minimizing total movement distance.
• The self-assembly process is formulated as a parallel algorithm that processes modules in order of depth in the target topology, starting from leaves.
• A hybrid control architecture is employed: each module handles low-level control and communication locally, while a central computer performs pose tracking and high-level planning.
• A dedicated docking controller ensures success by managing motion and clearance, especially when modules must reposition to enable connections.
• The framework reuses existing reconfiguration planners (e.g., Liu et al., 2019) by reformulating their sequential docking actions as a parallel self-assembly problem.
• Physical constraints such as actuator limits and module clearance are explicitly modeled in the motion and docking control phases.

Experimental results

Research questions

• RQ1 How can modular robot self-assembly be accelerated through parallel execution of docking actions?
• RQ2 What control architecture enables efficient coordination of many modules while maintaining docking reliability?
• RQ3 How can optimal module assignment be computed to minimize movement cost in self-assembly?
• RQ4 What strategies ensure successful docking when physical constraints and module proximity limit motion?
• RQ5 Can the framework be applied to reconfigure from arbitrary initial configurations to complex goal topologies?

Key findings

• The parallel self-assembly framework significantly reduces assembly time by executing multiple docking actions concurrently.
• The hybrid control architecture enables robust execution of complex self-assembly sequences on the SMORES-EP hardware platform.
• The docking controller successfully manages physical constraints, ensuring high success rates even in tight configurations.
• The framework demonstrated effective self-assembly in both simulation and hardware, including reconfiguration from a walker to a mobile vehicle with an arm.
• The algorithm scales well to larger structures, with simulations indicating potential for handling arbitrarily large 3D topologies with minor algorithmic adjustments.
• The approach enables faster self-reconfiguration by reformulating sequential reconfiguration plans into parallel self-assembly processes.