Multi-robot Construction Research Articles

Two-stage task allocation for multiple construction robots using an improved genetic algorithm

The construction industry is facing serious challenges, such as labor shortage and safety hazards. Construction robots can perform dangerous and repetitive tasks instead of workers. However, complex construction tasks require multi-robot collaboration, which needs efficient task allocation. This paper describes a two-stage task allocation method using an improved Genetic Algorithm (GA) for multiple construction robots. First, a construction task decomposition and grouping method under construction constraints is established, using a steel structure as example. Second, a Multi-Robot Construction Task Allocation (MRCTA) model is developed by modeling construction robots and tasks. Third, the MRCTA problem is divided into two stages, i.e., determining the optimal construction robot group for each task and the best task sequence for each robot group. Finally, a simulated experiment was designed and conducted. The results show that the method can efficiently find optimal solutions and the improved GA can enhance the diversity to achieve well-adapted solutions.

Material–Robot System for Assembly of Discrete Cellular Structures

We present a material–robot system consisting of mobile robots that can assemble discrete cellular structures. We detail the manufacturing of cuboctahedral unit cells, termed voxels, which passively connect to neighboring voxels with magnets. We then describe “relative” robots that can locomote on, transport, and place voxels. These robots are designed relative to and in coordination with the cellular structure—the geometry of the voxel informs the robot's global geometric configuration, local mechanisms, and end effectors, and robotic assembly features are designed into the voxels. We describe control strategies for determining build sequence, robot path planning, discrete motion control, and feedback, integrated within a custom software environment for simulating and executing a single or multi-robot construction. We use this material–robot system to build several types of structures, such as one-dimensional (1-D) beams, 2-D plates, and 3-D enclosures. The robots can navigate and assemble structures with minimal feedback, relying on voxel-sized resolution to achieve successful global positioning. We show a multi-robot assembly to increase the throughput and expand system capability using a deterministic centralized control strategy.

An open-source multi-robot construction system

Abstract We describe a completely open source system for performing experiments in multi-robot construction in laboratory settings. The system consists of robots that are capable of assembling cubic blocks into structures, which can be up to three blocks in height. The building material contains microcontrollers and multi-color light-emitting diodes (LEDs) that can be programmed by the robots using a near-field communication (NFC) interface. This mechanism is implemented to facilitate experiments where the intelligence that coordinates the construction can be embedded not only in the robots but also in the building material.

FIBERBOTS: an autonomous swarm-based robotic system for digital fabrication of fiber-based composites

Construction is a labor-intensive industry that relies on dependent processes being completed in series. Redesigning fabrication processes to allow for parallelization and replacing workers with mobile multi-robot construction systems are strategies to expedite construction, but they typically require extensive supporting infrastructure and strictly constrain fabricable designs. Here we present Fiberbots, a platform that represents a step toward autonomous, collaborative robotic fabrication. This system comprises a team of identical robots that work in parallel to build different parts of the same structure up to tens of times larger than themselves from raw, homogeneous materials. By winding fiber and resin around themselves, each robot creates an independent composite tube that it can climb and extend. The robots’ trajectories are controlled to construct intertwining tubes that result in a computationally derived woven architecture. This end-to-end system is scalable, allowing additional robots to join the system without substantially increasing design complexity or fabrication time. As an initial demonstration of system viability, a structural case study was performed. The robots constructed a 4.5 m-tall tubular composite structure in an outdoor environment in under 12 h. While further improvements must be made before this can be used in industry or in truly cooperative settings, this is the largest known demonstration of on-site construction with multiple, homogeneous mobile robots. This work offers a scalable step forward in autonomous, site-specific fabrication systems.

A cellular mechanism for multi-robot construction via evolutionary multi-objective optimization of a gene regulatory network

A major research challenge of multi-robot systems is to predict the emerging behaviors from the local interactions of the individual agents. Biological systems can generate robust and complex behaviors through relatively simple local interactions in a world characterized by rapid changes, high uncertainty, infinite richness, and limited availability of information. Gene Regulatory Networks (GRNs) play a central role in understanding natural evolution and development of biological organisms from cells. In this paper, inspired by biological organisms, we propose a distributed GRN-based algorithm for a multi-robot construction task. Through this algorithm, multiple robots can self-organize autonomously into different predefined shapes, and self-reorganize adaptively under dynamic environments. This developmental process is evolved using a multi-objective optimization algorithm to achieve a shorter travel distance and less convergence time. Furthermore, a theoretical proof of the system's convergence is also provided. Various case studies have been conducted in the simulation, and the results show the efficiency and convergence of the proposed method.