Annual Review of Control Robotics and Autonomous Systems · 2022 · 238 citations · 140 references
EngineeringMaterials ScienceRobotics EngineeringRobot ControlRoboticsSoft RoboticsSoft ActuatorsSoft MaterialsSoft Robot ControlSoft Robot DesignSoft Robot ModelingSoft SensorsIntelligent ControlManipulationSoft ActuationSoft Actuation ModelingSoft Robot LocomotionSoft Robot MorphologySoft Robotic LocomotionSoft Robotics Materials
Soft robots’ flexibility and compliance give them the potential to outperform rigid-bodied robots in varied tasks, yet they have not yet reached their full potential, with nature still ahead in locomotion and manipulation. This review seeks to identify the limits to soft‑robot performance and to guide future research toward understanding design principles, addressing challenges in materials, modeling, control, and intelligent behavior, and outlining key challenges for real‑world deployment. The authors survey advanced actuation, sensing, modeling, control, and learning architectures and explain how these approaches can be combined to build sophisticated soft robots for diverse application areas.
Soft robots’ flexibility and compliance give them the potential to outperform traditional rigid-bodied robots while performing multiple tasks in unexpectedly changing environments and conditions. However, soft robots are yet to reveal their full potential; nature is still far more advanced in several areas, such as locomotion and manipulation. To understand what limits their performance and hinders their transition from laboratory to real-world conditions, future studies should focus on understanding the principles behind the design and operation of soft robots. Such studies should also consider the major challenges with regard to complex materials, accurate modeling, advanced control, and intelligent behaviors. As a starting point for such studies, this review provides a current overview of the field by examining the working mechanisms of advanced actuation and sensing modalities, modeling techniques, control strategies, and learning architectures for soft robots. Next, we summarize how these approaches can be applied to create sophisticated soft robots and examine their application areas. Finally, we provide future perspectives on what key challenges should be tackled first to advance soft robotics to truly add value to our society.
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Small-scale soft-bodied robot with multimodal locomotion
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