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1. Design of an Underactuated Legged Robot with Prismatic Legs for Passive Adaptability to Terrain

   Noh, Seonghoon; Dollar, Aaron M. (August 2019, Proceedings of the ASME International Design Engineering Technical Conferences and Computers and Information in Engineering Conferences)

   Legged robots have the advantage of being able to maneuver rough, unstructured terrains unlike their wheeled counterparts. However, many legged robots require multiple sensors and online computations to specify the gait, trajectory or contact forces in real-time for a given terrain, and these methods can break down when sensory information is unreliable or not available. Over the years, underactuated mechanisms have demonstrated great success in object grasping and manipulation tasks due to their ability to passively adapt to the geometry of the objects without sensors. In this paper, we present an application of underactuation in the design of a legged robot with prismatic legs that maneuvers unstructured terrains under open-loop control using only four actuators – one for stance for each half of the robot, one for forward translation, and one for steering. Through experimental results, we show that prismatic legs can support a statically stable stance and can facilitate locomotion over unstructured terrain while maintaining its body posture. 

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2. Central pattern generator with inertial feedback for stable locomotion and climbing in unstructured terrain.

   Guillaume Sartoretti, Samuel Shaw (May 2018, IEEE International Conference on Robotics and Automation)

   : Inspired by the locomotor nervous system of vertebrates, central pattern generator (CPG) models can be used to design gaits for articulated robots, such as crawling, swimming or legged robots. Incorporating sensory feedback for gait adaptation in these models can improve the locomotive performance of such robots in challenging terrain. However, many CPG models to date have been developed exclusively for open-loop gait generation for traversing level terrain. In this paper, we present a novel approach for incorporating inertial feedback into the CPG framework for the control of body posture during legged locomotion on steep, unstructured terrain. That is, we adapt the limit cycle of each leg of the robot with time to simultaneously produce locomotion and body posture control. We experimentally validate our approach on a hexapod robot, locomoting in a variety of steep, challenging terrains (grass, rocky slide, stairs). We show how our approach can be used to level the robot's body, allowing it to locomote at a relatively constant speed, even as terrain steepness and complexity prevents the use of an open-loop control strategy. 

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3. Quadrupedal Robotic Walking on Sloped Terrains via Exact Decomposition into Coupled Bipedal Robots

   https://doi.org/10.1109/IROS45743.2020.9341181  

   Ma, Wen-Loong; Csomay-Shanklin, Noel; Ames, Aaron D. (October 2020, IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS))

   null (Ed.)

   Can we design motion primitives for complex legged systems uniformly for different terrain types without neglecting modeling details? This paper presents a method for rapidly generating quadrupedal locomotion on sloped terrains-from modeling to gait generation, to hardware demonstration. At the core of this approach is the observation that a quadrupedal robot can be exactly decomposed into coupled bipedal robots. Formally, this is represented through the framework of coupled control systems, wherein isolated subsystems interact through coupling constraints. We demonstrate this concept in the context of quadrupeds and use it to reduce the gait planning problem for uneven terrains to bipedal walking generation via hybrid zero dynamics. This reduction method allows for the formulation of a nonlinear optimization problem that leverages low-dimensional bipedal representations to generate dynamic walking gaits on slopes for the full-order quadrupedal robot dynamics. The result is the ability to rapidly generate quadrupedal walking gaits on a variety of slopes. We demonstrate these walking behaviors on the Vision 60 quadrupedal robot; in simulation, via walking on a range of sloped terrains of 13°, 15°, 20°, 25°, and, experimentally, through the successful locomotion of 13° and 20° ~ 25° sloped outdoor grasslands. 

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4. 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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5. Multi-robot connection towards collective obstacle field traversal

   Hu, Haodi; Liao, Xingjue; Du, Wuhao; Qian, Feifei (September 2024, IEEE International Conference on Robotics and Automation (ICRA))

   Environments with large terrain height variations present great challenges for legged robot locomotion. Drawing inspiration from fire ants’ collective assembly behavior, we study strategies that can enable two “connectable” robots to collectively navigate over bumpy terrains with height variations larger than robot leg length. Each robot was designed to be extremely simple, with a cubical body and one rotary motor actuating four vertical peg legs that move in pairs. Two or more robots could physically connect to one another to enhance collective mobility. We performed locomotion experiments with a two-robot group, across an obstacle field filled with uniformlydistributed semi-spherical “boulders”. Experimentally-measured robot speed suggested that the connection length between the robots has a significant effect on collective mobility: connection length C ∈ [0.86, 0.9] robot unit body length (UBL) were able to produce sustainable movements across the obstacle field, whereas connection length C ∈ [0.63, 0.84] and [0.92, 1.1] UBL resulted in low traversability. An energy landscape based model revealed the underlying mechanism of how connection length modulated collective mobility through the system’s potential energy landscape, and informed adaptation strategies for the two-robot system to adapt their connection length for traversing obstacle fields with varying spatial frequencies. Our results demonstrated that by varying the connection configuration between the robots, the tworobot system could leverage mechanical intelligence to better utilize obstacle interaction forces and produce improved locomotion. Going forward, we envision that generalized principles of robotenvironment coupling can inform design and control strategies for a large group of small robots to achieve ant-like collective environment negotiation. 

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