Swarm navigation of cyborg-insects in unknown obstructed soft terrain

TL;DR

This research proposes an algorithm that can navigate a swarm of cyborgs from the start to a predetermined goal in an unknown sandy terrain in the presence of obstacles and hills and verifies it under experiments.

Abstract

Cyborg insects refer to hybrid robots that integrate living insects with miniature electronic controllers to enable robotic-like programmable control. These creatures exhibit advantages over conventional robots in adaption to complex terrain and sustained energy efficiency. Nevertheless, there is a lack of literature on the control of multi-cyborg systems. This research gap is due to the difficulty in coordinating the movements of a cyborg system under the presence of insects' inherent individual variability in their reactions to control input. Regarding this issue, we propose a swarm navigation algorithm and verify it under experiments. This research advances swarm robotics by integrating biological organisms with control theory to develop intelligent autonomous systems for real-world applications.

Figures (5)

• Figure 1: Overview of the cyborg swarm navigation. (a) A cyborg insect. In this work, the Madagascar hissing cockroach (Gromphadorhina portentosa) was chosen for the cyborg system. A backpack circuit board attached to the insect was designed to house the necessary systems, including the locomotion control system and wireless communication module. It was powered by a rechargeable LiPo battery. How to prepare a cyborg insect is detailed in the supplementary materials. (b) An illustration of cyborg swarm navigation (top view). A decentralized algorithm was proposed to navigate the cyborg swarm to a designated goal through a sandy area in the presence of hills and obstacles. All the cyborgs have no information about obstacles and hills in the field. (c) The side view and (d) the back view of a cyborg swarm. A comprehensive presentation of this work can be found in Supplementary Video 1.
• Figure 2: Navigation algorithm design for a cyborg swarm. (a) The cyborg swarm consists of a leader and several followers. Each cyborg can detect neighbors within a limited sensing range, and distinguish the leader and followers. Only the leader is given the position of the goal. (b) An illustration of the proposed navigation algorithm. It consists of two parts: motion planning and trajectory tracking. The motion planning algorithm provides the desired positions of cyborgs for the next step based on their local information and passes it to the trajectory-tracking algorithm. The trajectory tracking algorithm computes the corresponding amplitude and types of stimulation (left, right, or acceleration) to be applied to the insects.
• Figure 3: Summary of swarm navigation experiments. (a) An illustration of experiments in an obstructed soft terrain (front view). (b) Path plot of Exp 1. (c) The top view of Exp 1. The video of Exp 1 is Supplementary Video 2. (d) Path plots of Exp 2 to 10. In Exp 2, the leader’s marker was untrackable for more than 5s, but the followers maintained group cohesion by moving to the leader's last known position. They resumed tracking the leader after it gained its trackability. (e) The degree of autonomy for the leader under conventional control (yellow) and the followers under our proposed control algorithm (blue) in 10 trials of experiments. The error bars denote the standard deviation. Our approach nearly doubles the cyborgs' free-motion time, effectively mitigating the likelihood of habituation of insects. Source data are provided as a Source Data file.
• Figure 4: Comparison between the experimental results under the conventional BOIDS and the proposed TGI algorithms. (a) The frequency of entanglements in 10 trials of experiments, respectively, under two control algorithms. The central line within the box indicates the median entanglement number, while the boundaries represent the 25th and 75th percentiles (lower and upper quartiles). The whiskers extend to the smallest and largest entanglement numbers within 1.5 times the interquartile range from the quartiles. The number of entanglements under the proposed TGI algorithm is notably lower than that under the BOIDS algorithm. (b) An illustration of entanglements: when parts of two or more cyborgs overlap with each other. Entanglement can lead to undesirable results, such as damage to the cyborgs. (c) A group of close but non-entangling cyborgs. (d) The minimum distance among all pairs of cyborgs circled out in experimental snapshots in one trial of experiments conducted with the BOIDS algorithm. Red points indicate instances where at least one of those cyborgs received electrical stimulation. The frequent stimulations on cyborgs that were too close to each other led to entanglements (distance less than 6 cm). (e) The minimum distance in one trial of experiments under TGI control. The TGI algorithm leverages insects' instincts to prevent entanglements. The corresponding snapshots reveal that cyborgs can move closely without entanglements under TGI control. The experiment results are shown by Supplementary Video 3. Source data are provided as a Source Data file.
• Figure 5: The process of an immobilized cyborg being saved by neighbors. (a) Entangle and detach from obstacles. A cyborg's "Y" shape marker became wedged on an obstacle ($t = t_2$), and the corresponding displacement almost remained the same during this period. Then the surrounding cyborgs navigated around the trapped one and the obstacle ($t = t_3$). Through our algorithm, the trapped cyborg gradually overcame the obstacle with the "attractive force" from other cyborgs, and the displacement started changing again. The red dots in the displacement plot represent electrical stimulations applied to the cyborgs. Finally, the cyborg detached from the obstacle ($t = t_4$). See Supplementary video 4. (b) Recover from an overturn. The grey bar in the bar chart denotes the time interval of a self-attempted period for recovery of an overturned cyborg. The bar's bottom and top indicate the start and ending moments. Similarly, the purple bar denotes the time taken for recovery with help from a neighbor. In Cases 1 and 3, the overturned cyborg first attempted and failed to recover by itself for a while, then successfully recovered by grabbing a passing neighbor. The corresponding recovery processes are illustrated by the snapshots. The experiment results are shown by Supplementary Video 5. Source data are provided as a Source Data file.