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1. Robust navigation of a soft growing robot by exploiting contact with the environment

   https://doi.org/10.1177/0278364920903774  

   Greer, Joseph D ; Blumenschein, Laura H ; Alterovitz, Ron ; Hawkes, Elliot W ; Okamura, Allison M ( December 2020 , The International Journal of Robotics Research)

   null (Ed.)

   Navigation and motion control of a robot to a destination are tasks that have historically been performed with the assumption that contact with the environment is harmful. This makes sense for rigid-bodied robots, where obstacle collisions are fundamentally dangerous. However, because many soft robots have bodies that are low-inertia and compliant, obstacle contact is inherently safe. As a result, constraining paths of the robot to not interact with the environment is not necessary and may be limiting. In this article, we mathematically formalize interactions of a soft growing robot with a planar environment in an empirical kinematic model. Using this interaction model, we develop a method to plan paths for the robot to a destination. Rather than avoiding contact with the environment, the planner exploits obstacle contact when beneficial for navigation. We find that a planner that takes into account and capitalizes on environmental contact produces paths that are more robust to uncertainty than a planner that avoids all obstacle contact. 

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2. Rule-Based Safe Probabilistic Movement Primitive Control via Control Barrier Functions

   https://doi.org/10.1109/TASE.2022.3217468  

   Davoodi, Mohammadreza ; Iqbal, Asif ; Cloud, Joseph M. ; Beksi, William J. ; Gans, Nicholas R. ( January 2022 , IEEE Transactions on Automation Science and Engineering)

   In this paper, we develop a novel and safe control design approach that takes demonstrations provided by a human teacher to enable a robot to accomplish complex manipulation scenarios in dynamic environments. First, an overall task is divided into multiple simpler subtasks that are more appropriate for learning and control objectives. Then, by collecting human demonstrations, the subtasks that require robot movement are modeled by probabilistic movement primitives (ProMPs). We also study two strategies for modifying the ProMPs to avoid collisions with environmental obstacles. Finally, we introduce a rule-base control technique by utilizing a finite-state machine along with a unique means of control design for ProMPs. For the ProMP controller, we propose control barrier and Lyapunov functions to guide the system along a trajectory within the distribution defined by a ProMP while guaranteeing that the system state never leaves more than a desired distance from the distribution mean. This allows for better performance on nonlinear systems and offers solid stability and known bounds on the system state. A series of simulations and experimental studies demonstrate the efficacy of our approach and show that it can run in real time. Note to Practitioners —This paper is motivated by the need to create a teach-by-demonstration framework that captures the strengths of movement primitives and verifiable, safe control. We provide a framework that learns safe control laws from a probability distribution of robot trajectories through the use of advanced nonlinear control that incorporates safety constraints. Typically, such distributions are stochastic, making it difficult to offer any guarantees on safe operation. Our approach ensures that the distribution of allowed robot trajectories is within an envelope of safety and allows for robust operation of a robot. Furthermore, using our framework various probability distributions can be combined to represent complex scenarios in the environment. It will benefit practitioners by making it substantially easier to test and deploy accurate, efficient, and safe robots in complex real-world scenarios. The approach is currently limited to scenarios involving static obstacles, with dynamic obstacle avoidance an avenue of future effort. 

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3. Characterizing Environmental Interactions for Soft Growing Robots

   https://doi.org/10.1109/IROS40897.2019.8968137  

   Haggerty, David A. ; Naclerio, Nicholas D. ; Hawkes, Elliot W. ( November 2019 , IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS))

   null (Ed.)

   Soft, tip-extending devices, or “vine robots,” are a promising new paradigm for navigating cluttered and confined environments. Because they lengthen from their tips, there is little relative movement of the body with the environment, and the compressible nature of the device allows it to pass through orifices smaller than its diameter. However, the interaction between these devices and the environment is not well characterized. Here we present a comprehensive mathematical model that describes vine robot behavior during environmental interaction that provides a basis from which informed designs can be generated in future works. The model incorporates transverse and axial buckling modes that result from growing into obstacles with varying surface normals, as well as internal path-dependent and independent resistances to growth. Accordingly, the model is able to predict the pressure required to grow through a given environment due to the interaction forces it experiences. We experimentally validate both the individual components and the full model. Finally, we present three design insights from the model and demonstrate how they each improve performance in confined space navigation. Our work helps advance the understanding of tip-extending, vine robots through quantifying their interactions with the environment, opening the door for new designs and impactful applications in the realms of healthcare, research, search and rescue, and space exploration. 

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4. Twisting for soft intelligent autonomous robot in unstructured environments

   https://doi.org/10.1073/pnas.2200265119  

   Zhao, Yao ; Chi, Yinding ; Hong, Yaoye ; Li, Yanbin ; Yang, Shu ; Yin, Jie ( May 2022 , Proceedings of the National Academy of Sciences)

   Soft robots that can harvest energy from environmental resources for autonomous locomotion is highly desired; however, few are capable of adaptive navigation without human interventions. Here, we report twisting soft robots with embodied physical intelligence for adaptive, intelligent autonomous locomotion in various unstructured environments, without on-board or external controls and human interventions. The soft robots are constructed of twisted thermal-responsive liquid crystal elastomer ribbons with a straight centerline. They can harvest thermal energy from environments to roll on outdoor hard surfaces and challenging granular substrates without slip, including ascending loose sandy slopes, crossing sand ripples, escaping from burying sand, and crossing rocks with additional camouflaging features. The twisting body provides anchoring functionality by burrowing into loose sand. When encountering obstacles, they can either self-turn or self-snap for obstacle negotiation and avoidance. Theoretical models and finite element simulation reveal that such physical intelligence is achieved by spontaneously snapping-through its soft body upon active and adaptive soft body-obstacle interactions. Utilizing this strategy, they can intelligently escape from confined spaces and maze-like obstacle courses without any human intervention. This work presents a de novo design of embodied physical intelligence by harnessing the twisting geometry and snap-through instability for adaptive soft robot-environment interactions. 

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5. An obstacle disturbance selection framework: emergent robot steady states under repeated collisions

   https://doi.org/10.1177/0278364920935514  

   Qian, Feifei ; Koditschek, Daniel E ( July 2020 , The International Journal of Robotics Research)

   Natural environments are often filled with obstacles and disturbances. Traditional navigation and planning approaches normally depend on finding a traversable “free space” for robots to avoid unexpected contact or collision. We hypothesize that with a better understanding of the robot–obstacle interactions, these collisions and disturbances can be exploited as opportunities to improve robot locomotion in complex environments. In this article, we propose a novel obstacle disturbance selection (ODS) framework with the aim of allowing robots to actively select disturbances to achieve environment-aided locomotion. Using an empirically characterized relationship between leg–obstacle contact position and robot trajectory deviation, we simplify the representation of the obstacle-filled physical environment to a horizontal-plane disturbance force field. We then treat each robot leg as a “disturbance force selector” for prediction of obstacle-modulated robot dynamics. Combining the two representations provides analytical insights into the effects of gaits on legged traversal in cluttered environments. We illustrate the predictive power of the ODS framework by studying the horizontal-plane dynamics of a quadrupedal robot traversing an array of evenly-spaced cylindrical obstacles with both bounding and trotting gaits. Experiments corroborate numerical simulations that reveal the emergence of a stable equilibrium orientation in the face of repeated obstacle disturbances. The ODS reduction yields closed-form analytical predictions of the equilibrium position for different robot body aspect ratios, gait patterns, and obstacle spacings. We conclude with speculative remarks bearing on the prospects for novel ODS-based gait control schemes for shaping robot navigation in perturbation-rich environments. 

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