Biological Jumping Mechanism Analysis and Modeling for Frog Robot

This paper presents a mechanical model of jumping robot based on the biological mechanism analysis of frog. By biological observation and kinematic analysis the frog jump is divided into take-offphase, aerial phase and landing phase. We find the similar trajectories of hindlimb joints during jump, the important effect of foot during take-off and the role of forelimb in supporting the body. Based on the observation, the frog jump is simplified and a mechanical model is put forward. The robot leg is represented by a 4-bar spring/linkage mechanism model, which has three Degrees of Freedom （DOF） at hip joint and one DOF （passive） at tarsometatarsal joint on the foot. The shoulder and elbow joints each has one DOF for the balancing function of arm. The ground reaction force of the model is analyzed and compared with that of frog during take-off. The results show that the model has the same advantages of low likelihood of premature lift-off and high efficiency as the frog. Analysis results and the model can be employed to develop and control a robot capable of mimicking the jumping behavior of frog.

四足机器人腾空跳跃的轨迹优化与控制方法

针对四足机器人难以跨越大尺寸障碍物的问题,提出了一种仿生腾空跳跃的轨迹优化控制方法 .首先,对马里努阿犬的越障运动进行测试和仿生学分析,得到腾空跳跃的参考轨迹.其次,根据摩擦锥、轨迹平滑、运动学和动力学等约束条件,基于轨迹优化的方法对仿生参考轨迹进行离线优化,生成四足机器人的期望跳跃轨迹,得到跳跃所需的足端位置、关节力矩和关节角度序列.再次,设计分阶段的跳跃控制器,采用关节和质心PD(Proportional Derivative)的组合控制策略,实现对期望跳跃轨迹的跟踪,采用变刚度虚拟模型控制实现对落地姿态的调整和足底力的缓冲.最后,在Webots仿真环境中完成了1 m高度的自由落地和跳跃0.75 m高度桌子的仿真实验.研究结果表明:仿生轨迹优化与控制算法能够使四足机器人跳跃大尺寸障碍物并且具有平稳落地的效果,对于提高四足机器人的高性能运动能力以及拓展其应用环境具有参考价值.

基于改进蚁群算法的移动机器人路径规划

针对传统蚁群算法在移动机器人路径规划中存在搜索盲目性、收敛速度慢及路径转折点多等问题,提出了一种基于改进蚁群算法的移动机器人路径规划算法。首先,利用跳点搜索(Jump Point Search,JPS)算法不均匀分配初始信息素,降低蚁群前期盲目搜索的概率;然后,引入切比雪夫距离加权因子和转弯代价改进启发函数,提高算法的收敛速度、全局路径寻优能力和搜索路径的平滑程度;最后,提出一种新的信息素更新策略,引入自适应奖惩因子,自适应调整迭代前、后期的信息素奖惩因子,保证了算法全局最优收敛。实验仿真结果表明,在不同地图环境下,与现有文献结果对比,该算法可以有效地缩短路径搜索的迭代次数和最优路径长度,并提高路径的平滑程度。

Kinematic synthesis method for the one-degree-of-freedom jumping leg mechanism of a locust-inspired robot

The one-degree-of-freedom(DOF) mechanism has a simple structure, convenient control, and high stiffness, and it has been applied in many micro jumping robots. Meanwhile, the six-and eight-bar mechanisms can satisfy more complex motion requirements than the four-bar jumping leg mechanism and they have good application prospects. However, the lack of effective design methods limits the application range of these mechanisms. In this work, a type and dimensional integration synthesis method was proposed with the one-DOF six-bar leg mechanism as the research object. The initial tibia and femur were determined based on the kinematic chain atlas, and configuration design was implemented through the superposition of links.When a closed chain was formed in the superposition process, the feasible range of the link length was analyzed by considering the constraint conditions. The proposed method innovatively establishes the relationship between the kinematic chain atlas and the configuration, and the feasible length ranges of the links can be quickly obtained simultaneously. Several examples were provided to prove the feasibility of the kinematic synthesis method. This method provides a useful reference for the design of one-DOF mechanisms.

改进双向动态JPS算法的移动机器人全局路径规划

针对跳点搜索(jump point search,JPS)算法在寻路过程中所存在的路径拐点多、中间搜索跳点数多、寻找跳点的过程中扩展节点数多和寻路时间较长等问题,提出改进双向动态JPS算法。改进算法动态定义正、反扩展方向上的目标点,动态定义启发函数,并利用动态约束椭圆对算法的扩展区域加以限制,以区分椭圆内、外区域的扩展优先级。在算法从起点和目标点两个方向上分别向对方进行扩展的过程中,以寻找到的新的代价最小点为新椭圆的焦点,椭圆的方位和约束区域也随之动态调整。仿真结果表明,经过优化改进的双向动态JPS算法在一般地图中有一定的表现,在障碍物较少且目标点距离起点较近的室内环境地图中表现尤为良好。

改进D^(*)算法的未知场景机器人运动规划

随着机器人技术不断发展,自主移动机器人的应用已拓展到复杂未知环境中。针对传统运动规划算法在复杂未知环境中存在搜索盲目、计算效率低、难获得安全轨迹等问题,本文提出了一种基于改进D^(*)算法的运动规划方法。其中,前端路径规划使用融合跳跃点搜索(JPS)思想的D^(*)算法,后端轨迹优化基于B样条构建二次规划问题。利用矩阵实验室(Matlab)与机器人操作系统(ROS)的仿真平台进行实验,结果表明,改进D^(*)算法在30×30的栅格地图中,相比传统D^(*)算法、FocussedD^(*)、有向D^(*)算法搜索耗时减少0.297、0.269、0.191s;动态障碍物存在时,可使移动机器人快速、安全运动至目标点。

基于模糊PID控制的机器人腿部运动控制系统研究

文章针对跳跃机器人腿部运动控制稳定性较低的缺陷,设计了一种基于模糊PID控制的腿部运动控制系统,并对其开展了ADAMS和MATLAB联合仿真.实验结果表明,文章所设计的基于模糊PID控制的跳跃机器人腿部运动控制系统具有良好的控制效果,提升了跳跃机器人的运动稳定性.

Research on Bionic Jumping and Soft Landing of Single Leg System in Quadruped Robot

This paper proposed a novel multi-motion wheel-leg-separated quadruped robot that can adapt to both the structured and unstructured grounds.The models of the positive/inverse position,velocity,acceleration,and workspace of the single leg mechanism in the quadruped robot were established.A single leg complex dynamic model of the quadruped robot is derived,considering the mass and inertial force of all the components in the mechanical leg.Combined with the human jumping law in situ,the jumping trajectory of the single leg was planned.To reduce landing impact,a soft landing strategy based on motion planning was proposed by simulating human knee bending and buffering action.The change law of the kinetic energy and momentum of all the links in the single leg mechanism during the jump process was studied,and the influencing factors of jump height were analyzed to realize the height control of the jump.Single leg jumping dynamics model was established,and a dynamic control strategy for trajectory tracking with foot force compensation was proposed.In Adams and MATLAB/Simulink software,the jump simulation of single leg mechanism was carried out.The prototype of quadruped robot was developed,and the jumping experiment of the single leg mechanism was tested.The robot's single leg bionic jumping and soft landing control are realized.

融合向量叉积与跳点搜索策略的改进A^(*)算法研究

为解决传统A^(*)寻路算法在搜索过程中会产生大量冗余节点,导致算法整体搜索效率低,运算内存消耗大等问题,从A^(*)算法的两个重要决策点出发,改进算法的代价评估函数与邻节点搜索策略,提出一种改进融合算法。首先,采用向量叉积与尺度平衡因子相结合的方法优化传统A^(*)算法的启发函数,减少A^(*)算法寻路过程中在最优路径周围产生的具有相同代价值的冗余节点,减少了对称路径的搜索;其次,融合跳点搜索(Jump point search, JPS)策略,通过逻辑判断实现路径的变步长跳跃搜索,避免了A^(*)算法逐层搜索效率低的弊端。在不同尺寸的栅格地图中进行仿真分析,发现改进融合算法相比于传统A^(*)算法,在路径长度基本相等的情况下,节点搜索数量约减少95%,且与传统JPS寻路算法相比,有效过滤了路径周围复杂形状障碍物产生的大量冗余跳点。最后,将改进融合算法应用于ROS移动机器人并进行对比实验以验证算法的可行性。实验结果表明:改进融合算法在获得高效安全的路径基础上,搜索效率相比于A^(*)算法可提高约94%。

An Untethered Miniature Soft Jumping Robot Inspired by Quadrupeds

In recent years,designing a soft robot that can jump continuously and quickly explore in a narrow space has been a hot research topic.With the continuous efforts of researchers,many types of actuators have been developed and successfully employed to actuate the rapid locomotion of soft robots.Although these mechanisms have enabled soft robots with excellent movement capabilities,they largely rely on external energy supply cables,which greatly limits their applications.Therefore,it is still a big challenge to realize the unconstrained movement of the soft robot and the flexible adjustment of the movement direction in a narrow space.Here,a wireless magnetically controlled soft jumping robot with single-leg is proposed,which can achieve continuous and rapid jumping motion.What's more interesting is that by changing the frequency and waveform of the control signal,this soft robot can easily switch between forward and backward motions.This motion direction switching function enables the magnetically controlled soft robot to return to the initial position without adjusting the direction when it completes the operation in a narrow pipe or takes the wrong path,which greatly improves the motion efficiency of the soft jumping robot and broadens its application field.

Obstacle detection and autonomous stair climbing of a miniature jumping robot

Miniature jumping robots(MJRs)have difficulty executing autonomous movements in unstructured environments with obstacles because of their limited perception and computing resources.This study investigates the obstacle detection and autonomous stair climbing methods for MJRs.We propose an obstacle detection method based on a combination of attitude and distance detections,as well as MJRs’motion.A MEMS inertial sensor collects the yaw angle of the robot,and a ranging sensor senses the distance between the robot and the obstacle to estimate the size of the obstacle.We also propose an autonomous stair climbing algorithm based on the obstacle detection method.The robot can detect the height and width of stairs and its position relative to the stairs and then repeatedly jump to climb them step by step.Moreover,the height,width,and position are sent to a control terminal through a wireless sensor network to update the information regarding the MJR and stairs in a control interface.Furthermore,we conduct stair detection,modeling,and stair climbing experiments on the MJR and obtain acceptable precisions for autonomous obstacle negotiation.Thus,the proposed obstacle detection and stair climbing methods can enhance the locomotion capability of the MJR in environmental monitoring,search and rescue,etc.

基于ADAMS和MATLAB联合仿真的仿蚱蜢机器人腿部运动控制系统

针对跳跃机器人腿部运动控制的稳定性较低的问题,基于模糊PID设计了机器人腿部运动控制系统。以蚱蜢的腿部机构为生物原型,设计了仿蚱蜢机器人腿部弹跳机构模型,分析其运动学和动力学特性,开展ADAMS和MATLAB联合仿真,采用模糊PID对联合仿真系统进行控制,确定了仿蚱蜢机器人控制系统的设计可靠性。结果显示,基于模糊PID的控制方法在仿蚱蜢机器人的跳跃运动中有较好的控制效果,既提高了稳定性,也符合蚱蜢的跳跃机理,同时控制系统的操作也简便。

新型轮足跳跃机器人的结构设计及实现

为提高机器人续航能力和增强敏捷度跳跃运动,使机器人能够适应复杂环境的需求,设计了具备轮式移动和跳跃两种运动方式的间歇性跳跃机器人。该机器人可根据不同的地形环境采用不同的运动方式,在较平坦地形下采用轮式移动方式;遇到障碍物或沟渠时,经过轮足转换,变为跳跃方式,从而扩大了运动范围,并分析了机器人的运动过程。详细介绍了弹跳机构的结构设计和工作原理,并根据总体的设计要求选定了机器人的一些关键参数。

基于改进跳点搜索法的移动机器人路径规划

为解决跳点搜索算法在路径规划过程中存储大量不必要跳点的问题,提高寻路效率,提出一种融合人工势场法与跳点搜索法的机器人路径规划算法。该算法通过人工势场法为跳点搜索法提供搜索参考方向,直至生成最终路径,能够有效降低扩展过程中搜索到的无用跳点数量,从而减少计算量。在搜索到的路径基础上,引入3次均匀B样条曲线,以进一步优化最优解,得到更加平滑的机器人移动路径。仿真与实验结果表明,改进算法可很大程度减少跳点的数量,有效提高路径规划效率。

微小型跳跃机器人:仿生原理,设计方法与驱动技术

高爆发性的跳跃是生物亿万年进化演变中赖以生存的关键之一,帮助生物实现在各种非结构化环境下的灵活运动功能.通过对生物跳跃机制的深入理解,微小型跳跃机器人在功能及性能上取得长足进步.本文以生物跳跃运动四个阶段(准备、起跳、腾空和着陆)为主线,剖析了生物的行为原理,介绍了对应的微小型跳跃机器人的动力学特征与技术,归纳了现有研究的挑战,最后讨论了跳跃机器人的未来发展趋势和潜在研究价值.

基于改进JPS的移动机器人路径规划算法

在移动机器人的路径规划技术中,跳点搜索算法(JPS)因具备简单、快速、易实现的特性而被广泛使用。然而,传统的JPS算法由于启发式函数搜寻效率低导致其搜索的节点数量冗余,而且难以有效兼顾规划路径的安全性。针对该问题,本文提出了一种改进的JPS算法。该方法设计了一种由对角线距离和方向信息结合的启发式函数用于提高寻路效率,并且进一步对规划路径进行平滑处理以有效兼顾规划路径的安全可靠性。移动机器人在复杂障碍物环境下的路径规划仿真实验表明,相较于JPS算法,本文改进后的JPS算法平均规划时间减少了13.4%,平均路径长度减少了3.1%,平均危险点数量降低了83.3%。

仿婴猴单腿弹跳机器人结构设计与实现

针对弹跳仿生机器人存在的主要问题,结合婴猴体型小且具有平稳暴发弹跳的特点,提出了一种仿婴猴单腿弹跳机器人,对其主要结构和控制系统进行了设计。通过分析婴猴的腿部结构,基于Stephenson六杆机构原理,设计了一种仿婴猴机器人腿部的平面八连杆机构,并对其轨迹进行优化,获得了具有最佳弹跳能力的杆长参数;对机器人弹跳的3个方向进行控制调整,利用双闭环反馈控制系统实现了机器人在起跳、腾空、落地时姿态的稳定;最后,建立了机器人的实物验证模型,整个机械装置质量为395.5 g,整体高度为250 mm。实验结果表明,该仿生单腿机器人可实现连续稳定的跳跃,最高弹跳速度可达20 m/s,弹跳高度为100 mm时的响应时间为0.1 s。

滑雪跳台修整机械臂轨迹控制方法

针对滑雪跳台修整工作人工成本高、效率低、精度差等问题,提出自由式滑雪跳台修整机械臂轨迹控制方法。针对标准跳台修整要求和六自由度工业机械臂本体特点,研究基于D-H法机械臂运动学建模、滑雪跳台标准曲线拟合、跳台修整轨迹规划算法。在白清寨滑雪场基地开展实际测试,测试结束后测量滑雪跳台关键点角度与规定角度值之间的误差均小于规定误差值正负0.3°,验证了滑雪跳台修整机械臂轨迹控制方法的有效性。

Analysis and Comparison of Two Jumping Leg Models for Bioinspired Locust Robot

Bionic jumping robot can cross the obstacles by jumping, and it has a good application prospect in the unstructured com- plex environment. The less Degree of Freedom （DOF） jumping leg, which has the characteristics of simple control and high rigidity, and is very important in research. Based on the experimental observation of leg physiological structure and take-off process of locust, two 1 DOF jumping leg models, which includes four-bar jumping leg model and slider-crank jumping leg model, are established, and multi objective optimization is conducted to deduce the motion law of two 1 DOF jumping leg models and jumping leg of locust is closer. Then the jumping performance evaluation indices are proposed, which include the mechanical property, body attitude, jumping distance and jumping performances of the two jumping leg models environmental effect. According to these evaluation indices, the are analyzed and compared, and the simulation is conducted for further explanations. The analysis results show that the four-bar jumping leg has smaller structural size and its motion law is closer to the hindleg of locust. The slider-crank jumping leg has better mechanical property, stronger energy storage capacity and the rough ground has less effect on it. This study offers a quantitative analysis and comparison for different jumping leg models of bionic locust jumping robot. Furthermore, a theoretical basis for future research and engineering application is established.

仿青蛙变形轮式跳跃机器人的机构优化

能量储存和跳跃性能是制约跳跃机器人应用的关键因素。通过深入分析青蛙的跳跃运动,设计了一种变形轮式跳跃机器人机构,实现了能量的高效储存。在此基础上建立了轮式跳跃机构的运动学和动力学数学模型,对跳跃过程进行了详细分析。利用MATLAB对轮式跳跃机构进行了优化设计,确定了杆件的尺寸与弹簧连接点的最佳位置。建立虚拟样机和制作实物样机进行实验,实验结果与理论分析结果相吻合,验证了优化方法和结果的可靠性。