Abstract
Many species of termites build large, structurally complex mounds, and the mechanisms behind this coordinated construction have been a longstanding topic of investigation. Recent work has suggested that humidity may play a key role in the mound expansion of savannah-dwelling Macrotermes species: termites preferentially deposit soil on the mound surface at the boundary of the high-humidity region characteristic of the mound interior, implying a coordination mechanism through environmental feedback where addition of wet soil influences the humidity profile and vice versa. Here we test this potential mechanism physically using a robotic system. Local humidity measurements provide a cue for material deposition. As the analogue of the termite's deposition of wet soil and corresponding local increase in humidity, the robot drips water onto an absorbent substrate as it moves. Results show that the robot extends a semi-enclosed area outward when air is undisturbed, but closes it off when air is disturbed by an external fan, consistent with termite building activity in still vs. windy conditions. This result demonstrates an example of adaptive construction patterns arising from the proposed coordination mechanism, and supports the hypothesis that such a mechanism operates in termites.
1. Introduction
Mound-building termites of several different genera are known for their prowess in collective construction: colonies of millions of insects construct mounds that can be several meters tall, with elaborate outer features and complex networks of internal tunnels (Figures 1A,B; McFarlan and McWhirter, ; Turner, ; King et al., ). These examples from nature have long spurred interest in collective construction both from a scientific viewpoint, seeking to understand principles underlying the insects' activity (Grassé, ; Camazine et al., ), and from the engineering one, seeking to create artificial systems that operate under similar restrictions and with similar advantages (Werfel et al., ; Petersen et al., ). Mechanisms for coordinating the activity of independent agents acting with limited information are of interest to both communities. Understanding the principles behind the operation of such natural systems can provide a source of tools for designing artificial ones, as well as illuminating how these insect colonies function.
Figure 1
The classic explanation for how termites coordinate their building activity is based on a putative cement pheromone: a hypothetical chemical added to pellets of soil deposited by workers, which attracts other workers to the same site and triggers further deposition (Grassé,
We and others recently described a novel mechanism based on sensitivity to air humidity (Bardunias et al.,
While this humidity template mechanism is consistent with observed termite behavior, it is difficult to experimentally isolate humidity from other cues potentially available to termites (e.g., carbon dioxide level or air turbulence) and determine with certainty that their actions are solely due to humidity levels. To explore whether the humidity template mechanism on its own can produce the kinds of building patterns seen with the insects, in this work we test an artificial agent-based construction system in which humidity alone is the defining template characteristic. Simulations can indicate the feasibility of the basic mechanism (Supplementary Material), but simulations by necessity neglect factors that can prove to be of importance in the actual behavior of the simulated system (Zubair et al.,
Figure 2

Robophysical model. (A) Robot agent analogous to a termite, with gripper for manipulating blocks at left of image and dripper for water deposition on the right. (B) Overhead view of the arena after the robot has placed several blocks.
2. Materials and Methods
We constructed an autonomous mobile robot equipped with humidity sensors (Carey,
Initially, a pair of parallel walls provide a short tunnel; tiles between the walls are saturated, while those beyond the tunnel's end are left dry. A humidity bubble, like that observed during active building on the termite mounds (Bardunias et al.,
Figure 3

State and transition diagram used by the robot termite. Blue boxes indicate the robot states, and transition arrows between each state are labeled with the fulfillment conditions.
To make a deposition, a termite must affix its load of saturated clay soil to existing material on the mound (Figure 1B). In our 2D analogue, the robot deposits its load adjacent to an existing wall or previously deposited block. The robot hence needs to accurately identify when it is sufficiently close to a wall to “attach” its load, before returning to the back wall of the arena. Termites are blind and navigate via smell, vibration, and touch; however, these modalities can be difficult to implement cleanly with current sensors, so deposition is accomplished using a combination of touch and the on-board omnidirectional camera (Supplementary Material).
As the robot moves, it passively drips water from its reservoir onto the substrate. To simulate the effect of the 3D nature of termite building in our 2D arena, a transparent acrylic lid covers the initial tunnel, and is manually extended after each deposition cycle to the furthest extent of any contiguous wall. The deposition loop repeats until either no space wider than 5 cm remains between deposited blocks, or 14 blocks have been placed.
We performed experiments under three conditions, analogous to those experienced by M. michaelseni workers in Namibia: (1) The external air is still, as is typical for termites building on the surface of their mound at night, or in protected areas on calm days. (2) A fan blows air across the arena outside the tunnel, corresponding to wind, typical especially during the day. (3) The robot's water reservoir is left empty, corresponding to conditions experienced by termites in the dry season.
These methods are described in detail in the Supplementary Material, along with supporting video of depositions under wet and dry still conditions.
3. Results
Figure 4 summarizes the results from the three experimental conditions described above. Row A shows a representative sample of the end-state block placement under each trial scenario. Row B shows the points at which the robot sensed the humidity had dropped below the given threshold value (75% RH), thus triggering a deposition, on a digitized map of the arena (determined by synchronizing the video feed from an overhead camera with the robot's own recording of the environment). The point where the robot passed the threshold for each block deposition cycle is marked with a cross; the color scale (inset) identifies the cycle sequence (up to a maximum of 14 blocks). Row C plots the vertical distance between the edge of the initial arena walls (marked in green in Row B) and the block deposition trigger points for all trials in the three experimental conditions.
Figure 4

Results of robophysical experiments. (A) End state block placements for one example run of each of the three treatments [left: robot's water reservoir full, no external airflow; middle: external fan blowing across the arena from the right hand side (fan direction was alternated between experiments); right: no fan, water reservoir empty]. (B) Locations where measured humidity dropped below threshold (75% RH), triggering block deposition, superimposed for all trials for each condition. Cycle sequence is indicated by color of the location marker (inset), showing the change in trigger location over time. Fan position, shown by a red dot on the right in the middle panel, was alternated between trials. (C) Comparison of the distances of the block deposition trigger points from the initial wall length [see (B), center] for the three experimental conditions. Significance calculated via 2-tailed independent sample t-test; a minimum of n = 6 trials was conducted for each experimental condition.
In trials without external agitation of the air, the initial tunnel is extended outward as water deposition and new blocks trap humidity, allowing the robot to move increasingly further forward over time before deposition is triggered (Figure 4, left). In trials with a fan blowing air across the arena, the robot deposits blocks so as to close off the tunnel (Figure 4, center). Similarly, without additional water being added to the system, the zone of high humidity does not advance and the robot closes the tunnel (Figure 4, right). A two-tailed independent-sample t-test demonstrates a significant difference in deposition trigger location between the still high-humidity experiments vs. the disrupted and dry trials (Supplementary Material). The deposition distances of the blocks are not statistically different between the latter two experimental conditions, but a less orderly build structure in the fan case (Figure 4, Supplementary Figure 7) reflects the irregular evaporation pattern generated by the fan's disruption of the humidity bubble. End conditions for all trials in each experimental condition can be found in Supplementary Figures 8–10.
4. Discussion
The experiments above demonstrate that in the absence of disturbance to the humidity landscape, reinforcement of a humidity bubble through water associated with deposition can provide a feedback mechanism enabling mound expansion. Without such reinforcement, the expansion stops. In the robophysical experiments above, we observe that the humidity bubble formed in our arena is easily disrupted, with drier airflow reducing the humidity even close to the wet surface and behind the moderate protection provided by the block being carried. As a result of this disruption, the robot closes off the tunnel rather than continuing to extend it forward.
Theraulaz et al. (
The shape of the eventual construction thus depends on two factors. The first is the humidity template, which provides the cue directing agents to look for a place to deposit material; its shape is the result of moisture emanating (in the termite case) from the mound interior and freshly deposited material, or (in the robophysical experiments) from the wet tiles. The second is the evolving physical structure itself, which provides the substrate to which more material is attached; the locations of previous depositions shape the possibilities for further growth of the mound.
Macrotermes michaelseni building activity at the mound surface, expanding the mound, varies seasonally and with time of day. Almost all expansion occurs during or soon after the rainy season, when soil brought up by workers from below the nest is much wetter; in the dry season, when deep soils have much lower water content, little or no mound expansion occurs (Turner et al.,
These results support the feasibility of the hypothesized mechanism for coordination of termite construction. While termites sense a wide range of environmental stimuli and are doubtless influenced by a variety of factors in different situations, these experiments indicate that humidity alone provides a sufficient cue for when to deposit material in order to produce such building patterns.
While the animal experiments that motivated this work were performed specifically with M. michaelseni, similar principles may operate with other insects. Many other termite species are observed to respond strongly to different humidity levels (Emerson,
The challenges of swiftly navigating a largely symmetrical and homogeneous environment with limited visual fields became apparent through the experimental process, suggesting the advantages of incorporating biomimetic sensory modalities into robots that operate in visually constrained environments. Termites are blind and process the world largely through tactile and olfactory information. Coupling sensing of humidity (or analogous sensing of other chemicals or signals) with wide-field touch or force sensing may convey a richer, more salient landscape profile for such a construction algorithm. Nevertheless, the robot used here produced the observed building patterns despite its sensory limitations and imprecise navigation. Such robustness to unreliability of individual agents and actions is necessary to collaborative construction algorithms, for engineered systems no less than for social insect colonies, whose successful operation likewise cannot depend on unerring precision by each insect.
A template-based feedback process like the one considered here could potentially provide a novel mechanism to help coordinate environmentally-responsive building in collective robots. It requires no direct communication between agents, nor do agents respond directly to perceived configurations of material depositions, as they do in many insect-inspired models (Theraulaz and Bonabeau,
Statements
Data availability statement
The original contributions presented in the study are included in the article/Supplementary Materials; further inquiries can be directed to the corresponding author/s.
Author contributions
NC, PB, JW, and RN: conceptualization. NC, PB, and JW: methodology. NC: robot hardware, software development, robot experiments, and visualization. JW: simulations and project administration. NC and JW: analysis and writing. RN and JW: supervision and funding. All authors contributed to the article and approved the submitted version.
Funding
Research reported in this publication was supported by the National Institute of General Medical Sciences of the National Institutes of Health under award number R01GM112633. The content is solely the responsibility of the authors and does not necessarily represent the official views of the National Institutes of Health.
Conflict of interest
The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.
Supplementary material
The Supplementary Material for this article can be found online at: https://www.frontiersin.org/articles/10.3389/frobt.2021.645728/full#supplementary-material
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Summary
Keywords
biorobotics, humidity, stigmergy, collective construction, termite, template
Citation
Carey NE, Bardunias P, Nagpal R and Werfel J (2021) Validating a Termite-Inspired Construction Coordination Mechanism Using an Autonomous Robot. Front. Robot. AI 8:645728. doi: 10.3389/frobt.2021.645728
Received
04 January 2021
Accepted
19 March 2021
Published
21 April 2021
Volume
8 - 2021
Edited by
Andre Rosendo, ShanghaiTech University, China
Reviewed by
Takeshi Kano, Tohoku University, Japan; Christian W. W. Pirk, University of Pretoria, South Africa
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© 2021 Carey, Bardunias, Nagpal and Werfel.
This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.
*Correspondence: Nicole E. Carey nic.carey@autodesk.com
†Present address: Nicole E. Carey, Autodesk Robotic Lab, San Francisco, CA, United States
This article was submitted to Bio-Inspired Robotics, a section of the journal Frontiers in Robotics and AI
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