章鱼臂感觉运动控制的神经模型与算法
Neural Models and Algorithms for Sensorimotor Control of an Octopus Arm
AI总结：
本文提出章鱼臂的生物物理模型及感觉运动控制算法，包括反馈神经运动控制律和共识算法，并通过数值模拟验证其有效性。
摘要：
本文提出了一种具有内部肌肉组织的软体章鱼臂的生物物理逼真模型。该建模的动机来自对感觉运动控制的实验观察，其中一条臂定位并到达目标。本文的主要贡献包括：(i) 开发模型以捕捉臂肌肉组织的机械特性、臂外周神经系统（PNS）的电特性以及PNS与肌肉收缩的耦合；(ii) 对臂感觉系统进行建模，包括化学感知和本体感觉；(iii) 提出感觉运动控制算法，其中包括一种新颖的反馈神经运动控制律，用于模拟面向目标的臂到达运动，以及一种新颖的共识算法，用于解决传感问题，例如从局部化学感觉信息（外源性）和臂变形信息（内源性）中定位食物源。本文提供了若干分析结果，包括所提出的传感和运动控制算法的静息状态表征和稳定性性质。数值模拟证明了我们方法的有效性。还报告了与观察到的臂静息形状和面向目标的到达运动的定性比较。
英文摘要：
In this article, a biophysically realistic model of a soft octopus arm with internal musculature is presented. The modeling is motivated by experimental observations of sensorimotor control where an arm localizes and reaches a target. Major contributions of this article are: (i) development of models to capture the mechanical properties of arm musculature, the electrical properties of the arm peripheral nervous system (PNS), and the coupling of PNS with muscular contractions; (ii) modeling the arm sensory system, including chemosensing and proprioception; and (iii) algorithms for sensorimotor control, which include a novel feedback neural motor control law for mimicking target-oriented arm reaching motions, and a novel consensus algorithm for solving sensing problems such as locating a food source from local chemical sensory information (exogenous) and arm deformation information (endogenous). Several analytical results, including rest-state characterization and stability properties of the proposed sensing and motor control algorithms, are provided. Numerical simulations demonstrate the efficacy of our approach. Qualitative comparisons against observed arm rest shapes and target-oriented reaching motions are also reported.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
