Regenerative patterning in Swarm Robots: mutual benefits of research in robotics and stem cell biology

Abstract

This paper presents a novel perspective of Robotic Stem Cells (RSCs), defined as the basic non-biological elements with stem cell like properties that can self-reorganize to repair damage to their swarming organization. Self here means that the elements can autonomously decide and execute their actions without requiring any preset triggers, commands, or help from external sources. We develop this concept for two purposes. One is to develop a new theory for self-organization and self-assembly of multi-robots systems that can detect and recover from unforeseen errors or attacks. This self-healing and self-regeneration is used to minimize the compromise of overall function for the robot team. The other is to decipher the basic algorithms of regenerative behaviors in multi-cellular animal models, so that we can understand the fundamental principles used in the regeneration of biological systems. RSCs are envisioned to be basic building elements for future systems that are capable of self-organization, self-assembly, self-healing and self-regeneration. We first discuss the essential features of biological stem cells for such a purpose, and then propose the functional requirements of robotic stem cells with properties equivalent to gene controller, program selector and executor. We show that RSCs are a novel robotic model for scalable self-organization and self-healing in computer simulations and physical implementation. As our understanding of stem cells advances, we expect that future robots will be more versatile, resilient and complex, and such new robotic systems may also demand and inspire new knowledge from stem cell biology and related fields, such as artificial intelligence and tissue engineering.

Fig. 1. Functional diagram of a robotic stem cell (RSC)

Each RSC has its own controllers, receptors, sensors, connectors and actuators. The controller activates and inhabits the receptors. The connectors support dynamic connection and disconnections with other cells. The actuators enable movement or other actions of the cell. RSCs communicate using signals similar to hormones or neurotransmitters among biological cells. Each RCS has a dynamic set of receptors that will react to incoming signals and trigger local actions. The local actions include activating and controlling local sensors and actuators, connecting and disconnecting with other RSCs, and generating and sending signals to other RSCs.

Fig. 2. The organizational diagram of robotic stem cells

It shows the organizational view of the robotic stem cells. Each cell is shown as a small cylinder and a set of connectors. In terms of their relationship, they can connect to each other using the connectors, or they disconnect from other cells. Similar to biological cells, RSCs do not have any names or globally unique identifiers and they are free to come and go in an organization. Unlike biological cells, RSCs may not die by themselves.

Fig. 3. The trivial and essential RSCs and structural equivalence

If the damaged RSC is trivial (e.g. RSC 3 in G1’), then its good neighbor RSC (e.g. 1) will disconnect from it and search and dock with the other good neighbor (e.g., 7). If the damaged RSC is essential (e.g. RSC 1 in G2’), then the good neighbors will select a replacement for the lost essential RSC. The selected RSC will conduct the healing process by forming the essential structure around itself with the collaboration of the good neighbors. For example, to replace a damaged RSC 1 in G2’, the good neighbors 2, 3, and 4 may select 3 as the new essential RSC, and RSC 3 will connect to 4 and 2 respectively to recover the lost structure. This process can be used to repair multiple damages. For example, if a starfish has lost three tentacles, then this approach will first redistribute the body RSCs to form three new short tentacles and then adjust the lengths of five tentacles by moving RSCs around. The repaired starfish will be smaller in size but equivalent in structure.

Fig. 4. Self-healing starfish shape formation and recovery after damage and separation

(A-E). A random swarm of elements self-organize into a coherent global position system and a starfish with individually colored limbs. (E-I) A self-formed starfish is cut and separated in two halves, and each half self-heals into a complete, new, but smaller starfish.

Fig. 5. Self-healing complex color patterns upon spatial patterns

The initial RSCs first self-form a picture of the earth, then a half of the RSCs are removed from the picture. The remaining elements self-heal the picture of earth into a smaller scale.

Fig. 6. Self-healing temporal patterns with a scrolling text over a circular swarm of elements

(A) Images of a circular swarm during increasing time, showing the text scrolling across the swarm. (B) Scrolling text swarms before damage (left), after damage once (center), and after damage twice (right).

Fig. 7. SuperBot as a candidate mechanical platform for Robotic Stem Cells

A single SuperBot module with three degrees of freedom, electronics, power, computer, sensors, actuators and connectors.

Fig. 8

Example configurations of robotic stem cells based on SuperBot modules.

Fig. 9. Multifunctional examples of robotic stem cells based on SuperBot modules

From top-left to bottom-right: (1) a rolling track composed of six modules, (2) a rope climber of three modules, (3) an “inchworm” formed by four modules, (4) a slope climber with four modules, (5) a “humanoid” formed by six modules, (6) a sidewinder “snake” configured by seven modules, (7) a “caterpillar” of three modules and (8) a “scorpion” with four modules.