Have you ever seen a sea star move?
To many of us, sea star seem motionless, like a rock on the ocean’s floor, but in actuality, they have hundreds of tube feet attached to their underbelly.
These feet stretch and contract to attach to rough terrain, hold on to prey and, of course, move.
Any one tube foot on a sea star can act autonomously in responding to stimuli, but coupled together, they can synchronize their motion to produce a bouncing motion — their version of running.
For years, researchers have wondered exactly how a sea star accomplishes this synchronization, given it has no brain and a completely decentralized nervous system.
The answer, from researchers at the USC Viterbi School of Engineering, was recently published in the Journal of the Royal Society Interface: sea star couple a global directionality command from a “dominant arm” with individual, localized responses to stimuli to achieve coordinated locomotion.
In other words, once the sea star provides an instruction on which way to move, the individual feet figure out how to achieve this on their own, without further communication.
The researchers, including Professor Eva Kanso in USC Viterbi’s Department of Aerospace and Mechanical Engineering and Sina Heydari, a USC Viterbi Ph.D. candidate, were joined by Matt McHenry, associate professor of ecology and evolutionary biology at the University of California, Irvine; Amy Johnson, professor of marine biology at Bowdoin College; and Olaf Ellers, research associate in biology and mathematics at Bowdoin College.
The work builds on an existing hierarchal model of behavior, but goes further in explaining how much of sea star locomotion happens locally versus globally.
“The nervous system does not process everything in the same place at the same time, but relies on the idea that the sea star is competent and will figure it out,” said Kanso, a Zohrab A. Kaprielian Fellow in Engineering.
“If one tube foot pushes against the ground, the others will feel the force. This mechanical coupling is the only way in which one tube foot shares information with another.”
The nervous system of a sea star is characterized by a nerve ring that surrounds its mouth and connects to each individual arm through a radial nerve.
The muscles of each tube foot are stimulated by neurons connected to the radial and ring nerves.
All feet step in the same direction while crawling, but their movement is not synchronized. However, when achieving the bouncing gait, sea star seem to coordinate tens of feet into two or three synchronized groups.
The research team, led by Kanso, looked at both modes of motion, and the transition between them. The result is a model that describes how much of a sea star’s locomotion is determined by local sensory-motor response at the tube feet level versus global sensory-motor commands.
In the animal world, behavior is often described by one of two prevailing models of locomotion; behavior such as insect flight is the result of sensory feedback traveling through a central processing system, which sends a message activating a response, or it is the result of completely decentralized, individual responses to sensory information such as in fish schools or ant colonies.
Credit: Kanso Bio-Inspired Motion Lab.
Neither of these models seem to describe the motion of a sea star.
“In the case of the sea star, the nervous system seems to rely on the physics of the interaction between the body and the environment to control locomotion. All of the tube feet are attached structurally to the sea star and thus, to each other.”
In this way, there is a mechanism for “information” to be communicated mechanically between tube feet.
An individual tube foot would only need to sense its own state (proprioception) and respond accordingly.
Because its state is coupled mechanically to other tube feet, they work together collectively. As the tube feet begin to move, each produces an individual force that becomes a part of the sensory environment.
In this way, each tube foot is also responding to the forces produced by other tube feet and eventually, they establish a rhythm with each other.
The nervous system of a sea star is characterized by a nerve ring that surrounds its mouth and connects to each individual arm through a radial nerve.
The muscles of each tube foot are stimulated by neurons connected to the radial and ring nerves.
This is similar to other mechanical models of coordination. For example, take a set of mechanical metronomes, devices used to help keep rhythm or time for a musician.
You can start a set of 10 at all different phases, resting them on the same flat surface. Over time, they will synchronize.
At play is the mechanical coupling effect seen with the sea star; each metronome is mechanically interacting with the phases created by the other metronomes and as such, is effectively “communicating” with the other metronomes until they begin to beat in complete rhythm and synchrony.
How sea star behavior can help us design more efficient robotics systems
Understanding how a distributed nervous system, like that of a sea star, achieves complex, coordinated motions could lead to advancements in areas such as robotics.
In robotics systems, it is relatively straightforward to program a robot to perform repetitive tasks.
However, in more complex situations where customization is required, robots face difficulties. How can robots be engineered to apply the same benefits to a more complex problem or environment?
The answer might lie in the sea star model, Kanso said. “Using the example of a sea star, we can design controllers so that learning can happen hierarchically.
There is a decentralized component for both decision-making and for communicating to a global authority. This could be useful for designing control algorithms for systems with multiple actuators, where we are delegating a lot of the control to the physics of the system — mechanical coupling — versus the input or intervention of a central controller.”
Next, Kanso and her team will look at how the global directionality command arises in the first place and what happens if there are competing stimuli.
Funding: The work is partially supported by a Basic Research Center Grant from the Office of Naval Research, ONR Award Number: N00014-17-1- 2062.
Modern mobile robots are required to perform adequately in harsh environments such as disaster areas, distant planets, and deep oceans (Murphy, 2004; Antonelli et al., 2008; Sanderson, 2010; Nagatani et al., 2013; Patané, 2019).
The challenge now is how to make the robots coordinate, in real-time, their numerous bodily degrees of freedom under unpredictable circumstances, including changes in the environment and unexpected physical damages to the robots’ structure. Previous studies tackled this problem by using learning techniques (Bongard et al., 2006; Mahdavi and Bentley, 2006; Mostafa et al., 2010; Koos et al., 2013; Christensen et al., 2014; Ren et al., 2014; Rubio et al., 2018, 2019; Yen et al., 2018) and trial-and-error methods (Cully et al., 2015), however, the performance level of robots using these techniques is not satisfactory. Specifically, the previous robots could only adapt to predictable circumstances or required a considerably long adaptation time.
Drawing inspiration from animals could be one solution to the aforementioned problem. Indeed, animals, even primitive living organisms, do not lose their functionality under unstructured and unpredictable real-world constraints, and they can adapt to various environments in real-time by coordinating their bodily degrees of freedom (Takamatsu et al., 2001; Schilling et al., 2013). This ability has been honed through evolutionary selection pressure, and it is likely that there is a sophisticated underlying mechanism. Owing to this, engineers have started implementing animal adaptation mechanisms in robots (Ijspeert, 2014).
Among the various animal species, in this paper, we focus on the locomotion of a brittle star; a variety in the phylum Echinodermata, which includes other varieties like starfish, sea cucumber, sea urchin etc. (Glaser, 1907; Arshavskii et al., 1976a,b; Wilkie, 1978; Cobb and Stubbs, 1981; Skold and Rosenberg, 1996; Carnevali, 2006; Astley, 2012; Kano et al., 2012, 2017; Watanabe et al., 2012; Matsuzaka et al., 2017; Clark et al., 2019).
A brittle star has a central disc and five functionally interchangeable flexible arms that diverge radially from a central disc (Figure 1A), and it can move adaptively on unpredictable and unstructured terrains (Arshavskii et al., 1976b). Moreover, it has an outstanding adaptability to bodily damage; it can move even after losing most of its arms (Arshavskii et al., 1976a; Kano et al., 2017). It achieves this highly adaptive locomotion by real-time coordination of different arms (i.e., inter-arm coordination) and the many bodily degrees of freedom within each arm (i.e., intra-arm coordination) (Arshavskii et al., 1976a,b; Astley, 2012; Kano et al., 2012, 2017; Watanabe et al., 2012; Matsuzaka et al., 2017; Clark et al., 2019).
Surprisingly, these coordinations are performed via an extremely simple decentralized nervous system along the arms, which join a circumoral nerve ring (Figure 1B, Supplementary Movie) (Cobb and Stubbs, 1981). Thus, brittle stars likely implement an ingenious autonomous decentralized control mechanism that enables adaptation to unexpected circumstances through the coordination of many body parts.
Thus far, the essential control mechanism underlying the brittle stars’ locomotion had stayed elusive for a long time, although several studies analyzed the locomotion patterns of brittle stars (Arshavskii et al., 1976a,b; Astley, 2012). Recently, we have addressed this issue by adopting a synthetic approach to infer essential mechanisms, by constructing phenomenological mathematical models and robots (Kano et al., 2012, 2017; Watanabe et al., 2012). Therein, we proposed a simple decentralized control model for the inter-arm coordination, based on the locomotion of brittle stars whose arms were trimmed or amputated in various ways (Kano et al., 2017).
We implemented this mechanism in a brittle star-like robot and demonstrated that it can immediately adapt to damages, in one or several arms, by automatically coordinating the still responsive arms, in a way similar to real brittle stars. However, as the trimmed-arm brittle stars were intensively analyzed, our previous works focused on the way different arms are coordinated (i.e., inter-arm coordination), but not on the ways multiple bodily degrees of freedom within each arm are coordinated (i.e., intra-arm coordination). Thus, it remained unclear how brittle stars move adaptively by coupling the inter- and intra-arm coordination.
Herein, we aim to elucidate the decentralized control mechanism that couples the inter- and intra-arm coordinations in brittle stars’ locomotion. Based on findings of the behavior of brittle stars, with various morphologies (various arm lengths, different numbers of arms etc.) and in different environments, we propose a decentralized control model that incorporates both inter- and intra-arm coordination mechanisms.
Given that we are motivated to capture the essential mechanism rather than to strictly mimic the locomotion of real brittle stars, the proposed mechanism is simple and describes the minimal requirement of the brittle stars’ locomotion. The validity of the proposed control mechanism was investigated via simulations, and with an experimental robot. The results show that the proposed mechanism, despite its simplicity, can reproduce the behavior of brittle stars to some extent.
The remainder of this paper is structured as follows. In section 2, we briefly summarize behavioral findings on brittle stars. In section 3, we propose a model of brittle star locomotion. Specifically, we present a model of the mechanical system and the decentralized control mechanism for the inter- and intra-arm coordination, which was deduced from the behavioral findings. In sections 4, 5, we demonstrate that a simulated brittle star (section 4) and an experimental brittle star-like robot (section 5) reproduce the locomotion of real brittle stars. Finally, we draw our conclusions and indicate the scope of future work in section 6.
Conclusion and Future Work
We focused on the locomotion of brittle stars that move by coordinating their five flexible arms. Based on behavioral findings of brittle stars with various morphologies in various environments, we proposed a simple decentralized control model that incorporates both inter- and intra-arm coordination mechanisms. We demonstrated, via simulations, that the proposed model reproduces the behavioral findings qualitatively. Moreover, we developed a brittle star-like robot and performed real-world experiments; the robot moved in a qualitatively similar manner as the real brittle stars.
Previous studies that used learning or trial-and-error techniques (Bongard et al., 2006; Mahdavi and Bentley, 2006; Mostafa et al., 2010; Koos et al., 2013; Christensen et al., 2014; Ren et al., 2014; Cully et al., 2015; Rubio et al., 2018, 2019; Yen et al., 2018) required a considerable amount of time (more than several tens of seconds) to respond to unexpected physical damage. Meanwhile, we have recently developed a brittle star-like robot that can immediately adapt to unexpected physical damage (Kano et al., 2017), yet the number of degrees of freedom within the body was still small. In contrast, this study succeeded in considerably increasing the number of bodily degrees of freedom since our previous work (Kano et al., 2017), thereby paving the way to developing robots that can coordinate a large number of bodily degrees of freedom adapting to unpredictable circumstances in real-time.
This study is also significant from a scientific viewpoint because we succeeded in capturing the essence of the inter- and intra-arm coordination mechanism in brittle stars. Moreover, we believe that our finding imparts novel insights into the essential mechanism of animals’ adaptive locomotion from a general perspective.
In fact, the proposed mechanism has things in common with other animals. For example, in insect locomotion, local positive feedback mechanism works depending on whether the leg supports locomotion or not (Schmitz et al., 2008), which is similar to the control mechanism proposed in this study.
However, there are limitations in this study. First, we had to fine-tune parameters for each body configuration as well as to carefully choose frictional property of the floor. Second, the robot did not move as effectively as real brittle stars. In particular, the locomotion of the robot with only one arm was extremely slow. Third, we could not reproduce locomotion on a terrain with several objects (Figure 2E) with the robot.
These limitations originate from mechanical and control issues. Concerning mechanical aspects, the reaction force was not properly measured by the current sensor system used. Additionally, the mass distribution of the robot and the friction between the body and the ground were not optimal. Regarding control, the proposed control scheme is not able to fully mimic the brittle stars’ locomotion owing to its simplicity, even though it likely captures the essence of the locomotion. More complex control schemes may improve performance. Solving these issues remain as future work.
Another future direction of this work is the realization of a fully autonomous brittle star-like robot. For this, from the viewpoint of mechanics, the robot must contain batteries. From the viewpoint of control, the moving direction needs to be automatically determined. In our previous works, we performed behavioral experiments wherein the nerve ring was partially damaged (Clark et al., 2019), and based on this, we proposed a mathematical model for the nerve ring and succeeded in determining the moving direction in a self-organized manner (Kano et al., 2019). We believe that the control scheme for the fully autonomous brittle star-like robot can be developed by combining the model proposed in this paper with that for the nerve ring (Kano et al., 2019).
Source:
USC
