Enhanced locomotion control for a planetary rover

This article presents an approach to improve and monitor the behavior of a skid-steering rover on rough terrains. An adaptive locomotion control generates speeds references to avoid slipping situations. An enhanced odometry provides a better estimation of the distance travelled. A probabilistic classification procedure provides an evaluation of the locomotion efficiency on-line, with a detection of locomotion faults. Results obtained with a Marsokhod rover are presented throughout the paper

The role of vision in Parkinson's disease locomotion control: Free walking task

Fundação de Amparo à Pesquisa do Estado de São Paulo (FAPESP)

Adaptive compliance control of a multi‐legged stair‐climbing robot based on proprioceptive data

Purpose – The purpose of this paper is to describe an innovative compliance control architecture for hybrid multi‐legged robots. The approach was verified on the hybrid legged‐wheeled robot ASGUARD, which was inspired by quadruped animals. The adaptive compliance controller allows the system to cope with a variety of stairs, very rough terrain, and is also able to move with high velocity on flat ground without changing the control parameters. Design/methodology/approach – The paper shows how this adaptivity results in a versatile controller for hybrid legged‐wheeled robots. For the locomotion control we use an adaptive model of motion pattern generators. The control approach takes into account the proprioceptive information of the torques, which are applied on the legs. The controller itself is embedded on a FPGA‐based, custom designed motor control board. An additional proprioceptive inclination feedback is used to make the same controller more robust in terms of stair‐climbing capabilities. Findings – The robot is well suited for disaster mitigation as well as for urban search and rescue missions, where it is often necessary to place sensors or cameras into dangerous or inaccessible areas to get a better situation awareness for the rescue personnel, before they enter a possibly dangerous area. A rugged, waterproof and dust‐proof corpus and the ability to swim are additional features of the robot. Originality/value – Contrary to existing approaches, a pre‐defined walking pattern for stair‐climbing was not used, but an adaptive approach based only on internal sensor information. In contrast to many other walking pattern based robots, the direct proprioceptive feedback was used in order to modify the internal control loop, thus adapting the compliance of each leg on‐line.

Robust Agent Control of an Autonomous Robot with Many Sensors and Actuators

This thesis presents methods for implementing robust hexpod locomotion on an autonomous robot with many sensors and actuators. The controller is based on the Subsumption Architecture and is fully distributed over approximately 1500 simple, concurrent processes. The robot, Hannibal, weighs approximately 6 pounds and is equipped with over 100 physical sensors, 19 degrees of freedom, and 8 on board computers. We investigate the following topics in depth: distributed control of a complex robot, insect-inspired locomotion control for gait generation and rough terrain mobility, and fault tolerance. The controller was implemented, debugged, and tested on Hannibal. Through a series of experiments, we examined Hannibal's gait generation, rough terrain locomotion, and fault tolerance performance. These results demonstrate that Hannibal exhibits robust, flexible, real-time locomotion over a variety of terrain and tolerates a multitude of hardware failures.

Energy-Economical Heuristically Based Control of Compass Gait Walking on Stochastically Varying Terrain

Investigation uses simulation to explore the inherent tradeoffs ofcontrolling high-speed and highly robust walking robots while minimizing energy consumption. Using a novel controller which optimizes robustness, energy economy, and speed of a simulated robot on rough terrain, the user can adjust their priorities between these three outcome measures and systematically generate a performance curveassessing the tradeoffs associated with these metrics.

Exploiting body dynamics for controlling a running quadruped robot

Exploiting the body dynamics to control the behavior of robots is one of the most challenging issues, because the use of body dynamics has a significant potential in order to enhance both complexity of the robot design and the speed of movement. In this paper, we explore the control strategy of rapid four-legged locomotion by exploiting the intrinsic body dynamics. Based on the fact that a simple model of four-legged robot is known to exhibit interesting locomotion behavior, this paper analyzes the characteristics of the dynamic locomotion for the purpose of the locomotion control. The results from a series of running experiments with a robot show that, by exploiting the unique characteristics induced by the body dynamics, the forward velocity can be controlled by using a very simple method, in which only one control parameter is required. Furthermore it is also shown that a few of such different control parameters exist, each of them can control the forward velocity. Interestingly, with these parameters, the robot exhibits qualitatively different behavior during the locomotion, which could lead to our comprehensive understanding toward the behavioral diversity of adaptive robotic systems. © 2005 IEEE.

具有万向机构的蛇形机器人运动控制

设计了一种能够使蛇形机器人运动更灵巧、奇异点更少和运动能力更强的机构 ,对具有三个自由度的新型蛇形机器人单元进行了改进 ,在单元上增加被动轮机构 ,使其具有万向机构的特点。该单元不仅能够用被动轮驱动机器人运动 ,而且增加了类似于主动轮的驱动机构 ,克服了被动轮驱动能力弱的缺点 ,增强了机器人的运动能力。在分析非完整约束的基础上 ,对蛇形机器人的运动学和冗余度进行分析 ,提出了控制该类蛇形机器人运动的分解矩阵方法和分组交替运动法。

基于循环抑制CPG的蛇形机器人运动控制方法

蛇具有细长无肢的身体、独特的半球形关节，使其可在神经系统控制下完成与环境相适应的多种节律运动。模仿蛇的运动机理和行为方式而设计的蛇形机器人克服了轮腿式机器人的缺点，增加了机器人的运动方式，扩大了机器人的应用范围。但应用传统的控制策略实现蛇形机器人运动控制遇到了很难克服的问题。随着社会经济与科技的发展，研究人员把从蛇运动神经系统研究中得到的启示应用到蛇形机器人上，希望不仅可以解决其运动控制问题，更能在构型、步态及控制机制上皆可展示蛇的特征。 生物学家已经证明动物的节律运动是其低级神经中枢的自激行为，是由中枢模式发生器(Central Pattern Generator，CPG)控制的。中枢模式发生器是一种能够在缺乏有规律的感知和中枢控制输入的情况下，产生有节奏模式输出的神经网络。 本文以国家自然科学基金课题《基于CPG的蛇形机器人控制方法研究》和国家“863”高技术计划资助项目《具有环境适应能力的蛇形机器人的研究》为依托，突破以相互抑制机理研究CPG的传统观点，首次创新性地提出应用循环抑制(Cyclic Inhibition, CI)机理来研究蛇形机器人的CPG建模与实现问题。本研究涵概了神经元模型的特性分析、蛇形机器人关节循环抑制CPG建模理论、蛇形机器人循环抑制CPG神经网络稳定性分析以及典型步态的生成方法、循环抑制CPG神经网络控制蛇形机器人蜿蜒运动参数设定策略、应用动力学仿真和实验对该CPG控制方法有效性的验证。 首先，本文介绍了两个用于CPG建模研究的蛇形机器人“勘查者”和“勘查者-I”。给出各自机械系统、控制系统的构成和动力学仿真平台。 其次，详细分析了神经元以及传统的相互抑制(Mutual Inhibition, MI)CPG的特性。从工程角度首次创新性地应用循环抑制建模理论构建了蛇形机器人CPG模型，并对其稳定性进行了深入的分析。首次证明持续型神经元构成的单向循环抑制(Unilateral Cyclic Inhibition, UCI) CPG是能产生振荡输出CPG中微分方程数量最少的，而且其产生振荡输出的机理完全不同于传统的相互抑制CPG。其不需要具备调整功能，只需要神经元之间强的单向循环抑制连接。 第三，首次应用单向激励连接循环抑制CPG构成蛇形机器人神经网络系统。分析了其稳定性，给出其产生振荡输出的条件。通过仿真和实验验证了循环抑制CPG神经网络实现典型步态(蜿蜒运动、伸缩运动和侧向运动)的有效性。首次应用双向循环抑制(Bidirectional Cyclic Inhibition, BCI)CPG神经网络在不同高级控制神经元命令激活下的输出实现蛇形机器人典型运动步态之间的转换。为蛇节律运动生成机制建模提供了新方法。 最后，从实时性、控制方便性等工程应用的角度，对单向循环抑制CPG神经网络实现蛇形机器人蜿蜒运动控制进行了深入的分析。给出了S-波形、幅值、运动速度和运动轨迹曲率的参数设定策略。该系统应用首CPG自激励权重调解成功解决了传统CPG控制系统中CPG的个数比蛇形机器人关节数多一个的问题，并用其实现了一种独特的转弯控制策略。 综上，为蛇形机器人运动控制提供了全新的方法。

Effects of postural threat on walking features of Parkinson's disease patients

Fundação de Amparo à Pesquisa do Estado de São Paulo (FAPESP)

Task-level control of the lateral leg spring model of cockroach locomotion

The Lateral Leg Spring model (LLS) was developed by Schmitt and Holmes to model the horizontal-plane dynamics of a running cockroach. The model captures several salient features of real insect locomotion, and demonstrates that horizontal plane locomotion can be passively stabilized by a well-tuned mechanical system, thus requiring minimal neural reflexes. We propose two enhancements to the LLS model. First, we derive the dynamical equations for a more flexible placement of the center of pressure (COP), which enables the model to capture the phase relationship between the body orientation and center-of-mass (COM) heading in a simpler manner than previously possible. Second, we propose a reduced LLS "plant model" and biologically inspired control law that enables the model to follow along a virtual wall, much like antenna-based wall following in cockroaches. © 2006 Springer.

Motor control optimization of compliant one-legged locomotion in rough terrain

While underactuated robotic systems are capable of energy efficient and rapid dynamic behavior, we still do not fully understand how body dynamics can be actively used for adaptive behavior in complex unstructured environment. In particular, we can expect that the robotic systems could achieve high maneuverability by flexibly storing and releasing energy through the motor control of the physical interaction between the body and the environment. This paper presents a minimalistic optimization strategy of motor control policy for underactuated legged robotic systems. Based on a reinforcement learning algorithm, we propose an optimization scheme, with which the robot can exploit passive elasticity for hopping forward while maintaining the stability of locomotion process in the environment with a series of large changes of ground surface. We show a case study of a simple one-legged robot which consists of a servomotor and a passive elastic joint. The dynamics and learning performance of the robot model are tested in simulation, and then transferred the results to the real-world robot. ©2007 IEEE.

Testing the role of expansion in the prospective control of locomotion

The constant bearing angle (CBA) strategy is a prospective strategy that permits the interception of moving objects. The purpose of the present study is to test this strategy. Participants were asked to walk through a virtual environment and to change, if necessary, their walking speed so as to intercept approaching targets. The targets followed either a rectilinear or a curvilinear trajectory and target size was manipulated both within trials (target size was gradually changed during the trial in order to bias expansion) and between trials (targets of different sizes were used). The curvature manipulation had a large effect on the kinematics of walking, which is in agreement with the CBA strategy. The target size manipulations also affected the kinematics of walking. Although these effects of target size are not predicted by the CBA strategy, quantitative comparisons of observed kinematics and the kinematics predicted by the CBA strategy showed good fits. Furthermore, predictions based on the CBA strategy were deemed superior to predictions based on a required velocity (V-REQ) model. The role of target size and expansion in the prospective control of walking is discussed.

The role of visual and nonvisual information in the control of locomotion

During locomotion, retinal flow, gaze angle, and vestibular information can contribute to one's perception of self-motion. Their respective roles were investigated during active steering: Retinal flow and gaze angle were biased by altering the visual information during computer-simulated locomotion, and vestibular information was controlled through use of a motorized chair that rotated the participant around his or her vertical axis. Chair rotation was made appropriate for the steering response of the participant or made inappropriate by rotating a proportion of the veridical amount. Large steering errors resulted from selective manipulation of retinal flow and gaze angle, and the pattern of errors provided strong evidence for an additive model of combination. Vestibular information had little or no effect on steering performance, suggesting that vestibular signals are not integrated with visual information for the control of steering at these speeds.

Eye-movements aid the control of locomotion

Eye-movements have long been considered a problem when trying to understand the visual control of locomotion. They transform the retinal image from a simple expanding pattern of moving texture elements (pure optic flow), into a complex combination of translation and rotation components (retinal flow). In this article we investigate whether there are measurable advantages to having an active free gaze, over a static gaze or tracking gaze, when steering along a winding path. We also examine patterns of free gaze behavior to determine preferred gaze strategies during active locomotion. Participants were asked to steer along a computer-simulated textured roadway with free gaze, fixed gaze, or gaze tracking the center of the roadway. Deviation of position from the center of the road was recorded along with their point of gaze. It was found that visually tracking the middle of the road produced smaller steering errors than for fixed gaze. Participants performed best at the steering task when allowed to sample naturally from the road ahead with free gaze. There was some variation in the gaze strategies used, but sampling was predominantly of areas proximal to the center of the road. These results diverge from traditional models of flow analysis.