Controlling and Simulating Soft Robotic Systems: Insights from a Thermodynamic Perspective

Soft robots are machines, and like all machines their function is to convert energy from one form into another to perform tasks. One key figure of merit for machines is their efficiency, which is defined as the ratio of task- oriented work out to total energy in. All soft robots convert stored energy (from e.g., batteries, pressurized gas, chemicals) into task-oriented work (picking up objects, locomoting, jumping). These systems are complex hybrids of chemical, mechanical, pneumatic, hydraulic, and electrical components. This complexity makes it difficult to analyze and measure their total efficiency and to identify the sources of energy loss between chemical, electrical, and mechanical domains. As the field of soft robotics matures, the design-flow process will shift from one in which building is central to one in which simulation takes precedence. That is, there is a shift from an empirical experimental methodology toward a well-characterized engineering workflow. At this point, questions such as ‘‘For how long will this robot run on a 2000 mAh battery?’’ will need to be answered, and predictive capabilities will become paramount as designers need to understand: (1) the large-scale deformations inherent to soft robotic systems; and (2) the transduction of energy in these complex, dissipative, systems to enable them to design an efficient and a well-controlled system. In this perspective piece, we discuss one possible predictive approach: a framework that uses port-based modeling. This approach uses bond-graphs and the recently developed port-Hamiltonian theory to provide a step-by-step system for analyzing hybrid, multi- domain, soft robotic systems. We discuss how this framework could be applied to controlling and optimizing soft robotic systems for energy efficiency, thereby increasing their utility. An energy-based approach is useful as a domain-free linker in analyzing complex systems; the use of ports promotes a clear distinction between energy conservation and dissipation and facilitates the analysis of efficiency. In addition, the parallels with hardware description languages and object-oriented programming will make it easier for engineers to design, for soft robots, control systems that optimize for efficiency.