Plant-inspired Robotics Research Articles

Fully autonomous water monitoring by plant-inspired robots

Developing countries struggle with water quality management owing to poor infrastructure, limited expertise, and financial constraints. Traditional water testing, relying on periodic site visits and manual sampling, is impractical for continuous wide-area monitoring and fails to detect sudden heavy metal contamination. To address this, plant-inspired robots capable of fully autonomous water quality monitoring are proposed. Constructed from paper, the robot absorbs surrounding water through its roots. This paper robot is controlled by paper-based microfluidic logic that sends absorbed water to petal-shaped actuators only when the water is polluted by heavy metals. This triggers the actuators to swell and bend like a blooming flower, visually signaling contamination to local residents. In tests with copper-contaminated water, the robotic flower’s diameter increased from 4.69 cm to 14.89 cm, a more than threefold expansion (217.25 %). This significant blooming movement serves as a highly visible and easily recognizable indicator of water pollution, even for the public. Furthermore, the paper robot can be mass-produced at a low cost (∼$0.2 per unit) and deployed over large areas. Once installed, the paper robot operates autonomously using surrounding water as a power source, eliminating the need for external electrical infrastructure and expert intervention. Therefore, this autonomous robot offers a new approach to water quality monitoring suitable for resource-limited environments, such as Sub-Saharan Africa.

A perspective on plant robotics: from bioinspiration to hybrid systems

As miscellaneous as the Plant Kingdom is, correspondingly diverse are the opportunities for taking inspiration from plants for innovations in science and engineering. Especially in robotics, properties like growth, adaptation to environments, ingenious materials, sustainability, and energy-effectiveness of plants provide an extremely rich source of inspiration to develop new technologies—and many of them are still in the beginning of being discovered. In the last decade, researchers have begun to reproduce complex plant functions leading to functionality that goes far beyond conventional robotics and this includes sustainability, resource saving, and eco-friendliness. This perspective drawn by specialists in different related disciplines provides a snapshot from the last decade of research in the field and draws conclusions on the current challenges, unanswered questions on plant functions, plant-inspired robots, bioinspired materials, and plant-hybrid systems looking ahead to the future of these research fields.

Novel Motion Sequences in Plant-Inspired Robotics: Combining Inspirations from Snap-Trapping in Two Plant Species into an Artificial Venus Flytrap Demonstrator.

The field of plant-inspired robotics is based on principles underlying the movements and attachment and adaptability strategies of plants, which together with their materials systems serve as concept generators. The transference of the functions and underlying structural principles of plants thus enables the development of novel life-like technical materials systems. For example, principles involved in the hinge-less movements of carnivorous snap-trap plants and climbing plants can be used in technical applications. A combination of the snap-trap motion of two plant species (Aldrovanda vesiculosa and Dionaea muscipula) has led to the creation of a novel motion sequence for plant-inspired robotics in an artificial Venus flytrap system, the Venus Flyflap. The novel motion pattern of Venus Flyflap lobes has been characterized by using four state-of-the-art actuation systems. A kinematic analysis of the individual phases of the new motion cycle has been performed by utilizing precise pneumatic actuation. Contactless magnetic actuation augments lobe motion into energy-efficient resonance-like oscillatory motion. The use of environmentally driven actuator materials has allowed autonomous motion generation via changes in environmental conditions. Measurement of the energy required for the differently actuated movements has shown that the Venus Flyflap is not only faster than the biological models in its closing movement, but also requires less energy in certain cases for the execution of this movement.

Enacting Plant-Inspired Robotics.

Plants offer a source of bioinspiration for soft robotics. Nevertheless, a gap remains in designing robots based on the fundamental principles of plant intelligence, rooted in a non-centralized, modular architecture and a highly plastic phenotype. We contend that a holistic approach to plant bioinspiration—one that draws more fully on the features of plant intelligence and behavior—evidences the value of an enactivist perspective. This is because enactivism emphasizes not only features of embodiment such as material composition and morphology, but also autonomy as an important aspect of plant intelligence and behavior. The enactivist sense of autonomy concerns the dynamics of self-producing systems (such as plants) that create a distinction between themselves and a domain of interactions that bear on the conditions of viability of the system. This contrasts with the widespread, but diluted notion of autonomy that merely indicates the independent operability of a system for an arbitrary period. Different notions of autonomy are relevant for soft roboticists, for instance, when evaluating limitations on existing growing robots (“growbots”) that take bioinspiration from plants, but depend on a fixed source of energy and material provided by an external agent. More generally, plant-inspired robots serve as a case study for an enactivist approach to intelligence, while, correspondingly, enactivism calls attention to the possibility of non-zoological forms of intelligence embodied in a self-organizing, autonomous system.

The Bio-Engineering Approach for Plant Investigations and Growing Robots. A Mini-Review.

It has been 10 years since the publication of the first article looking at plants as a biomechatronic system and as model for robotics. Now, roboticists have started to look at plants differently and consider them as a model in the field of bioinspired robotics. Despite plants have been seen traditionally as passive entities, in reality they are able to grow, move, sense, and communicate. These features make plants an exceptional example of morphological computation - with probably the highest level of adaptability among all living beings. They are a unique model to design robots that can act in- and adapt to- unstructured, extreme, and dynamically changing environments exposed to sudden or long-term events. Although plant-inspired robotics is still a relatively new field, it has triggered the concept of growing robotics: an emerging area in which systems are designed to create their own body, adapt their morphology, and explore different environments. There is a reciprocal interest between biology and robotics: plants represent an excellent source of inspiration for achieving new robotic abilities, and engineering tools can be used to reveal new biological information. This way, a bidirectional biology-robotics strategy provides mutual benefits for both disciplines. This mini-review offers a brief overview of the fundamental aspects related to a bioengineering approach in plant-inspired robotics. It analyses the works in which both biological and engineering aspects have been investigated, and highlights the key elements of plants that have been milestones in the pioneering field of growing robots.

Plant Bioinspired Ecological Robotics.

Plants are movers, but the nature of their movement differs dramatically from that of creatures that move their whole body from point A to point B. Plants grow to where they are going. Bio-inspired robotics sometimes emulates plants' growth-based movement; but growing is part of a broader system of movement guidance and control. We argue that ecological psychology's conception of “information” and “control” can simultaneously make sense of what it means for a plant to navigate its environment and provide a control scheme for the design of ecological plant-inspired robotics. In this effort, we will outline several control laws and give special consideration to the class of control laws identified by tau theory, such as time to contact.

Artificial Venus Flytraps: A Research Review and Outlook on Their Importance for Novel Bioinspired Materials Systems.

Bioinspired and biomimetic soft machines rely on functions and working principles that have been abstracted from biology but that have evolved over 3.5 billion years. So far, few examples from the huge pool of natural models have been examined and transferred to technical applications. Like living organisms, subsequent generations of soft machines will autonomously respond, sense, and adapt to the environment. Plants as concept generators remain relatively unexplored in biomimetic approaches to robotics and related technologies, despite being able to grow, and continuously adapt in response to environmental stimuli. In this research review, we highlight recent developments in plant-inspired soft machine systems based on movement principles. We focus on inspirations taken from fast active movements in the carnivorous Venus flytrap (Dionaea muscipula) and compare current developments in artificial Venus flytraps with their biological role model. The advantages and disadvantages of current systems are also analyzed and discussed, and a new state-of-the-art autonomous system is derived. Incorporation of the basic structural and functional principles of the Venus flytrap into novel autonomous applications in the field of robotics not only will inspire further plant-inspired biomimetic developments but might also advance contemporary plant-inspired robots, leading to fully autonomous systems utilizing bioinspired working concepts.

Nonlinear Model Predictive Growth Control of a Class of Plant-Inspired Soft Growing Robots

Recently, researchers have shown an increased interest in considering plants as a model of inspiration for designing new robot locomotions. Growing robots, that imitate the biological growth presented by plants, have proved irresistible in unpredictable and distal environments due to their morphological adaptation and tip-extension capabilities. However, as a result of the irreversible growing process exhibited by growing robots, classical control schemes could fail in obtaining feasible solutions that respect the permanent growth constraint. Thus, in this article, a Nonlinear Model Predictive Control (NMPC) scheme is proposed to guarantee the robot’s performance towards point stabilization while respecting the constraints imposed by the growing process and the control limits. The proposed NMPC-based growth control has applied to the kinematic model of the recently proposed plant-inspired robots in the literature, namely, vine-like growing robots. Numerical simulations have been performed to show the effectiveness of the proposed NMPC-based growth control in terms of point stabilization, disturbance rejection, and obstacle avoidance and encouraging results were obtained. Finally, the robustness of the proposed NMPC-based growth control is analyzed against various input disturbances using Monte-Carlo simulations that could guide the tuning process of the NMPC.

Toward Growing Robots: A Historical Evolution from Cellular to Plant-Inspired Robotics.

This paper provides the very first definition of “growing robots”: a category of robots that imitates biological growth by the incremental addition of material. Although this nomenclature is quite new, the concept of morphological evolution, which is behind growth, has been extensively addressed in engineering and robotics. In fact, the idea of reproducing processes that belong to living systems has always attracted scientists and engineers. The creation of systems that adapt reliably and effectively to the environment with their morphology and control would be beneficial for many different applications, including terrestrial and space exploration or the monitoring of disasters or dangerous environments. Different approaches have been proposed over the years for solving the morphological adaptation of artificial systems, e.g., self-assembly, self-reconfigurability, evolution of virtual creatures, plant inspiration. This work reviews the main milestones in relation to growing robots, starting from the original concept of a self-replicating automaton to the achievements obtained by plant inspiration, which provided an alternative solution to the challenges of creating robots with self-building capabilities. A selection of robots representative of growth functioning is also discussed, grouped by the natural element used as model: molecule, cell, or organism growth-inspired robots. Finally, the historical evolution of growing robots is outlined together with a discussion of the future challenges toward solutions that more faithfully can represent biological growth.

Toward Energy Autonomy in Heterogeneous Modular Plant-Inspired Robots through Artificial Evolution

Contemporary robots perform energy intensive tasks -- e.g. manipulation and locomotion -- making the development of energy autonomous robots challenging. Since plants are primary energy producers in natural ecosystems, we took plants as a source of inspiration for designing our robotics platform. This led us to investigate energy autonomy in robots through employing solar panels. As plants move slowly compared to other large terrestrial organisms, it is expected that plant-inspired robots can enable robotic applications such as long-term monitoring and exploration where energy consumption could be minimized. Since it is difficult to manually design robotic systems that adheres to full energy autonomy, we utilize evolutionary algorithms to automate the design and evaluation of energy harvesting robots. We demonstrate how artificial evolution can lead to the design and control of a modular plant-like robot. Robotic phenotypes were acquired through implementing an evolutionary algorithm, a generative encoding and modular building blocks in a simulation environment. The generative encoding is based on a context sensitive Lindemayer-System (L-System) and the evolutionary algorithm is used to optimize compositions of heterogeneous modular building blocks in the simulation environment. Phenotypes that evolved from the simulation environment are in turn transferred to a physical robot platform. The robotics platform consists of five different types of modules: (1) a base module; (2) a cube module; (3) servo modules; and (4,5) two types of solar panel modules that are used to harvest energy. The control system for the platform is initially evolved in the simulation environment and afterwards transferred to an actual physical robot. A few experiments were done showing the relationship between energy cost and the amount of light tracking that evolved in the simulation. The reconfigurable modular robots are eventually used to harvest light with the possibility to be reconfigured based on the needs of the designer, the type of usable modules and/or the optimal configuration derived from the simulation environment. Long term energy autonomy has not been tested in this robotics platform. However, we think our robotics platform can serve as a stepping stone towards full energy autonomy in modular robots.

Evolutionary Developmental Soft Robotics As a Framework to Study Intelligence and Adaptive Behavior in Animals and Plants

In this paper a comprehensive methodology and simulation framework will be reviewed, designed in order to study the emergence of adaptive and intelligent behavior in generic soft-bodied creatures. By incorporating artificial evolutionary and developmental processes, the system allows to evolve complete creatures (brain, body, developmental properties, sensory and control system, etc.) for different task environments. Whether the evolved creatures will resemble animals or plants is in general not known a priori, and depends on the specific task environment set up by the experimenter. In this regard, the system may offer a unique opportunity to explore differences and similarities between these two worlds. Different material properties can be simulated and optimized, from a continuum of soft/stiff materials, to the interconnection of heterogeneous structures, both found in animals and plants alike. The adopted genetic encoding and simulation environment are particularly suitable in order to evolve distributed sensory and control systems, which play a particularly important role in plants. After a general description of the system some case studies will be presented, focusing on the emergent properties of the evolved creatures. Particular emphasis will be on some unifying concepts that are thought to play an important role in the emergence of intelligent and adaptive behavior across both the animal and plant kingdoms, such as morphological computation and morphological developmental plasticity. Overall, with this paper we hope to draw attention on set of tools, methodologies, ideas and results which may be relevant to researchers interested in plant-inspired robotics and intelligence.