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1. Rapidly Exploring Random Tree Algorithm-Based Path Planning for Worm-Like Robot

   https://doi.org/10.3390/biomimetics5020026  

   Wang, Yifan; Pandit, Prathamesh; Kandhari, Akhil; Liu, Zehao; Daltorio, Kathryn A. (June 2020, Biomimetics)

   null (Ed.)

   Inspired by earthworms, worm-like robots use peristaltic waves to locomote. While there has been research on generating and optimizing the peristalsis wave, path planning for such worm-like robots has not been well explored. In this paper, we evaluate rapidly exploring random tree (RRT) algorithms for path planning in worm-like robots. The kinematics of peristaltic locomotion constrain the potential for turning in a non-holonomic way if slip is avoided. Here we show that adding an elliptical path generating algorithm, especially a two-step enhanced algorithm that searches path both forward and backward simultaneously, can make planning such waves feasible and efficient by reducing required iterations by up around 2 orders of magnitude. With this path planner, it is possible to calculate the number of waves to get to arbitrary combinations of position and orientation in a space. This reveals boundaries in configuration space that can be used to determine whether to continue forward or back-up before maneuvering, as in the worm-like equivalent of parallel parking. The high number of waves required to shift the body laterally by even a single body width suggests that strategies for lateral motion, planning around obstacles and responsive behaviors will be important for future worm-like robots. 

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2. Informed Sampling-Based Planning to Enable Legged Robots to Safely Negotiate Permeable Obstacles

   https://doi.org/10.1115/1.4055625  

   Chen, Yiyu; Lian, Lingchen; Hsieh, Yu-Hsiu; Nguyen, Quan; Gupta, Satyandra K. (October 2023, Journal of Mechanisms and Robotics)

   Abstract Legged robots have a unique capability of traversing rough terrains and negotiating cluttered environments. Recent control development of legged robots has enabled robust locomotion on rough terrains. However, such approaches mainly focus on maintaining balance for the robot body. In this work, we are interested in leveraging the whole body of the robot to pass through a permeable obstacle (e.g., a small confined opening) with height, width, and terrain constraints. This paper presents a planning framework for legged robots manipulating their body and legs to perform collision-free locomotion through a permeable obstacle. The planner incorporates quadrupedal gait constraint, biasing scheme, and safety margin for the simultaneous body and foothold motion planning. We perform informed sampling for the body poses and swing foot position based on the gait constraint while ensuring stability and collision avoidance. The footholds are planned based on the terrain and the contact constraint. We also integrate the planner with robot control to execute the planned trajectory successfully. We validated our approach in high-fidelity simulation and hardware experiments on the Unitree A1 robot navigating through different representative permeable obstacles. 

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3. Modular Design of a Polymer‐Bilayer‐Based Mechanically Compliant Worm‐Like Robot

   https://doi.org/10.1002/adma.202210409  

   Muff, Livius F; Mills, Austin S; Riddle, Shane; Buclin, Véronique; Roulin, Anita; Chiel, Hillel J; Quinn, Roger D; Weder, Christoph; Daltorio, Kathryn A (May 2023, Advanced Materials)

   Abstract Soft earthworm‐like robots that exhibit mechanical compliance can, in principle, navigate through uneven terrains and constricted spaces that are inaccessible to traditional legged and wheeled robots. However, unlike the biological originals that they mimic, most of the worm‐like robots reported to date contain rigid components that limit their compliance, such as electromotors or pressure‐driven actuation systems. Here, a mechanically compliant worm‐like robot with a fully modular body that is based on soft polymers is reported. The robot is composed of strategically assembled, electrothermally activated polymer bilayer actuators, which are based on a semicrystalline polyurethane with an exceptionally large nonlinear thermal expansion coefficient. The segments are designed on the basis of a modified Timoshenko model, and finite element analysis simulation is used to describe their performance. Upon electrical activation of the segments with basic waveform patterns, the robot can move through repeatable peristaltic locomotion on exceptionally slippery or sticky surfaces and it can be oriented in any direction. The soft body enables the robot to wriggle through openings and tunnels that are much smaller than its cross‐section. 

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4. Reactive Locomotion Decision-Making and Robust Motion Planning for Real-Time Perturbation Recovery

   https://doi.org/10.1109/ICRA46639.2022.9812068  

   Gu, Zhaoyuan; Boyd, Nathan; Zhao, Ye (May 2022, International Conference on Robotics and Automation)

   In this paper, we examine the problem of push recovery for bipedal robot locomotion and present a reactive decision-making and robust planning framework for locomotion resilient to external perturbations. Rejecting perturbations is an essential capability of bipedal robots and has been widely studied in the locomotion literature. However, adversarial disturbances and aggressive turning can lead to negative lateral step width (i.e., crossed-leg scenarios) with unstable motions and self-collision risks. These motion planning problems are computationally difficult and have not been explored under a hierarchically integrated task and motion planning method. We explore a planning and decision-making framework that closely ties linear-temporal-logic-based reactive synthesis with trajectory optimization incorporating the robot’s full-body dynamics, kinematics, and leg collision avoidance constraints. Between the high-level discrete symbolic decision-making and the low-level continuous motion planning, behavior trees serve as a reactive interface to handle perturbations occurring at any time of the locomotion process. Our experimental results show the efficacy of our method in generating resilient recovery behaviors in response to diverse perturbations from any direction with bounded magnitudes. 

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5. Obstacle-Aided Navigation of a Soft Growing Robot

   Greer, J. D.; Blumenschein, L. H.; Okamura, A. M.; Hawkes, E. W. (May 2018, IEEE International Conference on Robotics and Automation)

   For many types of robots, avoiding obstacles is necessary to prevent damage to the robot and environment. As a result, obstacle avoidance has historically been an im- portant problem in robot path planning and control. Soft robots represent a paradigm shift with respect to obstacle avoidance because their low mass and compliant bodies can make collisions with obstacles inherently safe. Here we consider the benefits of intentional obstacle collisions for soft robot navigation. We develop and experimentally verify a model of robot-obstacle interaction for a tip-extending soft robot. Building on the obstacle interaction model, we develop an algorithm to determine the path of a growing robot that takes into account obstacle collisions. We find that obstacle collisions can be beneficial for open-loop navigation of growing robots because the obstacles passively steer the robot, both reducing the uncertainty of the location of the robot and directing the robot to targets that do not lie on a straight path from the starting point. Our work shows that for a robot with predictable and safe interactions with obstacles, target locations in a cluttered, mapped environment can be reached reliably by simply setting the initial trajectory. This has implications for the control and design of robots with minimal active steering. 

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