Capability by Stacking: The Current Design Heuristic for Soft Robots

Soft robots are a new class of systems being developed and studied by robotics scientists. These systems have a diverse range of applications including sub-sea manipulation and rehabilitative robotics. In their current state of development, the prevalent paradigm for the control architecture in these systems is a one-to-one mapping of controller outputs to actuators. In this work, we define functional blocks as the physical implementation of some discrete behaviors, which are presented as a decomposition of the behavior of the soft robot. We also use the term ‘stacking’ as the ability to combine functional blocks to create a system that is more complex and has greater capability than the sum of its parts. By stacking functional blocks a system designer can increase the range of behaviors and the overall capability of the system. As the community continues to increase the capabilities of soft systems—by stacking more and more functional blocks—we will encounter a practical limit with the number of parallelized control lines. In this paper, we review 20 soft systems reported in the literature and we observe this trend of one-to-one mapping of control outputs to functional blocks. We also observe that stacking functional blocks results in systems that are increasingly capable of a diverse range of complex motions and behaviors, leading ultimately to systems that are capable of performing useful tasks. The design heuristic that we observe is one of increased capability by stacking simple units—a classic engineering approach. As we move towards more capability in soft robotic systems, and begin to reach practical limits in control, we predict that we will require increased amounts of autonomy in the system. The field of soft robotics is in its infancy, and as we move towards realizing the potential of this technology, we will need to develop design tools and control paradigms that allow us to handle the complexity in these stacked, non-linear systems

Models for reinforcement learning and design of a soft robot inspired by Drosophila larvae

Designs for robots are often inspired by animals, as they are designed mimicking animals’ mechanics, motions, behaviours and learning. The Drosophila, known as the fruit fly, is a well-studied model animal. In this thesis, the Drosophila larva is studied and the results are applied to robots. More specifically: a part of the Drosophila larva’s neural circuit for operant learning is modelled, based on which a synaptic plasticity model and a neural circuit model for operant learning, as well as a dynamic neural network for robot reinforcement learning, are developed; then Drosophila larva’s motor system for locomotion is studied, and based on it a soft robot system is designed. Operant learning is a concept similar to reinforcement learning in computer science, i.e. learning by reward or punishment for behaviour. Experiments have shown that a wide range of animals is capable of operant learning, including animal with only a few neurons, such as Drosophila. The fact implies that operant learning can establish without a large number of neurons. With it as an assumption, the structure and dynamics of synapses are investigated, and a synaptic plasticity model is proposed. The model includes nonlinear dynamics of synapses, especially receptor trafficking which affects synaptic strength. Tests of this model show it can enable operant learning at the neuron level and apply to a broad range of NNs, including feedforward, recurrent and spiking NNs. The mushroom body is a learning centre of the insect brain known and modelled for associative learning, but not yet for operant learning. To investigate whether it participates in operant learning, Drosophila larvae are studied with a transgenic tool by my collaborators. Based on the experiment and the results, a mushroom body model capable of operant learning is modelled. The proposed neural circuit model can reproduce the operant learning of the turning behaviour of Drosophila larvae. Then the synaptic plasticity model is simplified for robot learning. With the simplified model, a recurrent neural network with internal neural dynamics can learn to control a planar bipedal robot in a benchmark reinforcement learning task which is called bipedal walker by OpenAI. Benefiting efficiency in parameter space exploration instead of action space exploration, it is the first known solution to the task with reinforcement learning approaches. Although existing pneumatic soft robots can have multiple muscles embedded in a component, it is far less than the muscles in the Drosophila larva, which are well-organised in a tiny space. A soft robot system is developed based on the muscle pattern of the Drosophila larva, to explore the possibility to embed a high density of muscles in a limited space. Three versions of the body wall with pneumatic muscles mimicking the muscle pattern are designed. A pneumatic control system and embedded control system are also developed for controlling the robot. With a bioinspired body wall will a large number of muscles, the robot performs lifelike motions in experiments

Limpet II: A Modular, Untethered Soft Robot

The ability to navigate complex unstructured environments and carry out inspection tasks requires robots to be capable of climbing inclined surfaces and to be equipped with a sensor payload. These features are desirable for robots that are used to inspect and monitor offshore energy platforms. Existing climbing robots mostly use rigid actuators, and robots that use soft actuators are not fully untethered yet. Another major problem with current climbing robots is that they are not built in a modular fashion, which makes it harder to adapt the system to new tasks, to repair the system, and to replace and reconfigure modules. This work presents a 450 g and a 250 × 250 × 140 mm modular, untethered hybrid hard/soft robot—Limpet II. The Limpet II uses a hybrid electromagnetic module as its core module to allow adhesion and locomotion capabilities. The adhesion capability is based on negative pressure adhesion utilizing suction cups. The locomotion capability is based on slip-stick locomotion. The Limpet II also has a sensor payload with nine different sensing modalities, which can be used to inspect and monitor offshore structures and the conditions surrounding them. Since the Limpet II is designed as a modular system, the modules can be reconfigured to achieve multiple tasks. To demonstrate its potential for inspection of offshore platforms, we show that the Limpet II is capable of responding to different sensory inputs, repositioning itself within its environment, adhering to structures made of different materials, and climbing inclined surfaces

Fluidic logic for the control and design of soft robotic systems

State-of-the-art in robotics are machines that do jobs. These jobs, for instance, can be automated routine procedures for delivering services with minimal human intervention or the task of building, maintaining or removing infrastructure in areas where it is too dangerous for humans to go. These assignments and tasks are open areas of research which can have a net-positive impact on society. We make robots from subsystems of hard components and rigid links in a physical system stacked in a hierarchy--building blocks of transistors on printed circuit boards in integrated approaches to control motors and end effectors. The hard characteristic of these systems means we can predict the motion and trajectory of robots. However, these constrained environments limit the places we can use robots. Suppose we would like to use a robot to interact with a human. In that case, the rigid materials and control systems may be incompatible with this unconstrained environment. Soft robots represent a change in thinking about robotic systems' dominant materials and control methods. Soft roboticists use soft materials, compliant joints with variable stiffness, and deformable systems in interaction with the environment. Rather than using motors, soft robots use air or other fluids to inflate and deflate chambers to make soft robots move and grasp. The design heuristic in soft robotics combines simple elements to create more complex systems. In this hierarchical architecture, there is a one-to-one mapping of control hardware to actuators resulting in systems that are increasingly capable of a diverse range of movements and actions. Nevertheless, as soft robots become even more capable, we will reach practical limits in size and control. This thesis explores the interdependence of architecture and control, moving beyond the current design heuristic to increase the capability of soft robots. An ideal control system in a soft robot has a low number of hardware outputs controlling a large number of actuators. Such an architecture could improve our ability to implement desired motions and behaviours to perform valuable tasks and move towards increased autonomy in soft robotics. This problem is reminiscent of the mechanical analogue systems developed in the \nth{20} century for numerical ballistic calculations. A solution using an abstract system of logic and philosophy ultimately led to the invention of the transistor and the electronics hierarchy of transistors on printed circuit boards and integrated systems in computers and robotics today. In this study, I use a fluidic transistor primitive to build memory elements based on logic gates and combinational logic to control arrays of actuators. The contributions of this thesis include the following: (i) a perspective on the current paradigm in soft robotic architecture and the scaling problem of control schemes in soft robots; (ii) the uses of stacking and hierarchy as a design principle in soft robots; (iii) the applications of sequential logic and memory for multi-state automata soft robots; (iv) a description of design dependencies for fluidic systems for medium-to-large scale integration. In summary, I address the significant challenge in soft robotic control and design, moving beyond the limitation of the control architecture toward autonomy using a fluidic architecture. I move through the levels of automata theory from combinational logic to sequential circuits and finite-state machines using fluidic transistors. My studies may help lay the foundations of a fluidic hardware description language for building large-scale integrated fluidic circuits in soft robotics design

Linking neural circuits to the mechanics of animal behavior in Drosophila larval locomotion

The motions that make up animal behavior arise from the interplay between neural circuits and the mechanical parts of the body. Therefore, in order to comprehend the operational mechanisms governing behavior, it is essential to examine not only the underlying neural network but also the mechanical characteristics of the animal’s body. The locomotor system of fly larvae serves as an ideal model for pursuing this integrative approach. By virtue of diverse investigation methods encompassing connectomics analysis and quantification of locomotion kinematics, research on larval locomotion has shed light on the underlying mechanisms of animal behavior. These studies have elucidated the roles of interneurons in coordinating muscle activities within and between segments, as well as the neural circuits responsible for exploration. This review aims to provide an overview of recent research on the neuromechanics of animal locomotion in fly larvae. We also briefly review interspecific diversity in fly larval locomotion and explore the latest advancements in soft robots inspired by larval locomotion. The integrative analysis of animal behavior using fly larvae could establish a practical framework for scrutinizing the behavior of other animal species

An economic analysis of a robotic harvest technology in New Zealand fresh apple industry : a dissertation presented in partial fulfilment of the requirements for the degree of Doctor of Philosophy in Agribusiness, Massey University School of Agriculture and Environment, Manawatu, New Zealand

The New Zealand apple industry is predominately an export-oriented industry relying on manual labour throughout the year. In recent years, however, labour shortages for harvesting have been jeopardising its competitiveness and profitability. Temporary immigration labour programs, such as the Recognised Seasonal Employer (RSE) program have not been able to solve the labour shortages, urging the industry to consider use of harvesting automation, i.e. robotic technology, as a solution. Harvesting robots are still in commercial trial stage and no studies have assessed the economic feasibility of such technology. The present study for the first time develops a bio-economic model to analyse the investment decision for adopting harvesting robots compared to available alternatives, i.e. platform and manual harvesting systems, using net present value (NPV) as the method of analysis; for newly established single-, bi-, and multi-varietal orchards across different orchard sizes, and three apple varieties (Envy, Jazz, and Royal Gala); and implications of orchard canopy transition and associated sensitivities are considered. The results of the analysis identified fruit value and yield as the key drivers for the adoption of harvesting automation. For relatively low value and or yielding varieties such as Jazz or Royal Gala, robots are less profitable in single-varietal orchard compared to bi-varietal orchard planted with relatively low value and yielding varieties. In a multi-varietal orchard, a relatively high value and high yield variety, such as Envy, is crucial to compensate for the costs incurred for harvesting other varieties using robots or platforms. The greatest potential benefit of utilising harvesting robots was reducing pickers required by an average of 54% for Envy and 48% for each of Jazz and Royal Gala across all orchard sizes compared to manual harvesting; and 7% in average for each of Envy, Jazz, and Royal Gala across all orchard sizes compared to platform harvesting system. This study also identified the break-even price for a robotic harvester in a single-varietal orchard, showed that the break-even prices exceeded the assumed price of the robot, and are highly variable depending on the varietal value and yield, where Envy as a relatively higher value and yielding variety returns a break-even price of $2.92 million compared to relatively lower value and yielding varieties, Jazz with$674,895, and Royal Gala with $689,608. Sensitivity analyses showed that both harvesting speed and efficiency are key parameters in the modelled orchard and positively affected the net returns of the investment and must be considered by researchers and manufacturers. However, for developers and potential adopters of robots, it should be more important that robots operate faster, but not necessarily as more efficient in order to generate a high return while substituting the highest number of pickers and leaving less unharvested fruit on trees in the limited harvesting window. Reducing robot price by 12% and 42% can generate an equivalent level of profit similar to manual or platform harvesting, respectively. Increases in labour wages, and decreases in labour availability and efficiency adversely affected the NPV and profitability outlook of the investment, but NPV was more affected by the decreases in labour efficiency and availability than wage increases. This research has important science and policy implications for policy makers, academics, growers, engineers, and manufacturers. From an economic perspective, for late adopters or those growers who may not be financially able to invest in robots or may be uncertain about their performance, platform harvesting system can be utilised as an alternative solution that is commercially available until robotic harvesting technology improves or becomes more affordable, and commercially available. Alternatively, it may be possible for these orchardists to benefit from utilising the robotic harvester in the form of a co-operative or contract-harvesting business model to avoid the capital costs associated with purchasing and operating the robots. Besides the economic factors, robotic harvesters have the potential to be considered as a solution for non-economic factors such as food safety problems. This is more apparent in the post-Covid-19 pandemic era, which has not only made it more difficult for growers to source their required workers due to border closures, but also has led consumers to be more cautious about food safety when they make purchase decisions and prefer to have their fresh fruit touchless from farm to plate. This may not be a problem for packhouses as most are automated, but it may be an issue for harvesting operations, because pickers have to pick apples by hand. Even though robots cannot be the only option for growers to rely on for the foreseeable future as they are not commercially available, in the current situation robot harvesting may be the most ideal solution