An official website of the United States government
Living systems can use a single periphery to perform a variety of tasks and adapt to a dynamic environment. This multifunctionality is achieved through the use of neural circuitry that adaptively controls the reconfigurable musculature. Current robotic systems struggle to flexibly adapt to unstructured environments. Through mimicry of the neuromechanical coupling seen in living organisms, robotic systems could potentially achieve greater autonomy. The tractable neuromechanics of the sea slug Aplysia californica’s feeding apparatus, or buccal mass, make it an ideal candidate for applying neuromechanical principles to the control of a soft robot. In this work, a robotic grasper was designed to mimic specific morphology of the Aplysia feeding apparatus. These include the use of soft actuators akin to biological muscle, a deformable grasping surface, and a similar muscular architecture. A previously developed Boolean neural controller was then adapted for the control of this soft robotic system. The robot was capable of qualitatively replicating swallowing behavior by cyclically ingesting a plastic tube. The robot’s normalized translational and rotational kinematics of the odontophore followed profiles observed in vivo despite morphological differences. This brings Aplysia-inspired control in roboto one step closer to multifunctional neural control schema in vivo and in silico. Future additions may improve SLUGBOT’s viability as a neuromechanical research platform.
more »
« less
Model-based dynamic feedback control of a planar soft robot: trajectory tracking and interaction with the environment
Leveraging the elastic bodies of soft robots promises to enable the execution of dynamic motions as well as compliant and safe interaction with an unstructured environment. However, the exploitation of these abilities is constrained by the lack of appropriate control strategies. This work tackles for the first time the development of closed-loop dynamic controllers for a continuous soft robot. We present two architectures designed for dynamic trajectory tracking and surface following, respectively. Both controllers are designed to preserve the natural softness of the robot and adapt to interactions with an unstructured environment. The validity of the controllers is proven analytically within the hypotheses of the model. The controllers are evaluated through an extensive series of simulations, and through experiments on a physical soft robot capable of planar motions.
more »
« less
- Award ID(s):
- 1830901
- PAR ID:
- 10548990
- Publisher / Repository:
- SAGE Publications
- Date Published:
- Journal Name:
- The International Journal of Robotics Research
- Volume:
- 39
- Issue:
- 4
- ISSN:
- 0278-3649
- Format(s):
- Medium: X Size: p. 490-513
- Size(s):
- p. 490-513
- Sponsoring Org:
- National Science Foundation
More Like this
-
-
Abstract Soft robots have attracted increasing attention due to their excellent versatility and broad applications. In this article, we present a minimally designed soft crawling robot (SCR) capable of robust locomotion in unstructured pipes with various geometric/material properties and surface topology. In particular, the SCR can squeeze through narrow pipes smaller than its cross section and propel robustly in spiked pipes. The gait pattern and locomotion mechanism of this robot are experimentally investigated and analysed by the finite element analysis, revealing that the resultant forward frictional force is generated due to the asymmetric mechanical properties along the length direction of the robot. The proposed simple yet working SCR could inspire novel designs and applications of soft robots in unstructured narrow canals such as large intestines or industrial pipelines.more » « less
-
In this work, we investigate a form of dynamic contact-rich locomotion in which a robot pushes off from obstacles in order to move through its environment. We present a reflex-based approach that switches between optimized hand- crafted reflex controllers and produces smooth and predictable motions. In contrast to previous work, our approach does not rely on periodic movements, complex models of robot and contact dynamics, or extensive hand tuning. We demonstrate the effectiveness of our approach and evaluate its performance compared to a standard model-free RL algorithm. We identify continuous clusters of similar behaviours, which allows us to successfully transfer different push-off motions directly from simulation to a physical robot without further retraining.more » « less
-
When robots operate in real-world off-road environments with unstructured terrains, the ability to adapt their navigational policy is critical for effective and safe navigation. However, off-road terrains introduce several challenges to robot navigation, including dynamic obstacles and terrain uncertainty, leading to inefficient traversal or navigation failures. To address these challenges, we introduce a novel approach for adaptation by negotiation that enables a ground robot to adjust its navigational behaviors through a negotiation process. Our approach first learns prediction models for various navigational policies to function as a terrain-aware joint local controller and planner. Then, through a new negotiation process, our approach learns from various policies' interactions with the environment to agree on the optimal combination of policies in an online fashion to adapt robot navigation to unstructured off-road terrains on the fly. Additionally, we implement a new optimization algorithm that offers the optimal solution for robot negotiation in real-time during execution. Experimental results have validated that our method for adaptation by negotiation outperforms previous methods for robot navigation, especially over unseen and uncertain dynamic terrains.more » « less
-
null (Ed.)Abstract In this study, we developed an offline, hierarchical intent recognition system for inferring the timing and direction of motion intent of a human operator when operating in an unstructured environment. There has been an increasing demand for robot agents to assist in these dynamic, rapid motions that are constantly evolving and require quick, accurate estimation of a user’s direction of travel. An experiment was conducted in a motion capture space with six subjects performing threat evasion in eight directions, and their mechanical and neuromuscular signals were recorded for use in our intent recognition system (XGBoost). Investigated against current, analytical methods, our system demonstrated superior performance with quicker direction of travel estimation occurring 140 ms earlier in the movement and a 11.6 deg reduction of error. The results showed that we could also predict the start of the movement 100 ms prior to the actual, thus allowing any physical systems to start up. Our direction estimation had an optimal performance of 8.8 deg, or 2.4% of the 360 deg range of travel, using three-axis kinetic data. The performance of other sensors and their combinations indicate that there are additional possibilities to obtain low estimation error. These findings are promising as they can be used to inform the design of a wearable robot aimed at assisting users in dynamic motions, while in environments with oncoming threats.more » « less
