腿式弹跳机器人的搭载回收系统设计与实现

发布时间：2018-07-16 09:39

【摘要】：随着机器人技术的发展和应用领域的不断拓展,对机器人环境适应能力提出了新的挑战。凭借出色的越障性能,小型弹跳机器人非常适用于狭窄空间和多障碍环境,但也因体型和续航能力限制,无法长时间远距离工作；此外,考虑到环境的复杂性,单一的弹跳机器人可能无法完成任务,本文基于实验室研制的腿式弹跳机器人,设计了搭载检测平台,组建了子母机器人系统,重点研究了子母机器人的相对定位方法及子机器人搭载回收策略。首先,规划了搭载回收系统的两大研究对象——弹跳机器人和搭载机器人的设计方案。针对早期研制的弹跳机器人的不足,提出两种弹跳机器人自复位和航向调节方法,有效提升了弹跳机器人的运动性能。设计了安装在弹跳机器人头部用于位姿检测的双面圆形色标及驱动机构。完成了弹跳机器人传感与控制电路设计,操作者或搭载机器人可无线控制弹跳机器人运动。设计了搭载机器人硬件系统,包括车体子系统、RGB-D信息反馈子系统、无线通信子系统和监控终端。其次,在硬件基础上,设计了弹跳机器人检测方法及回收策略,对基于RGB-D信息的位姿检测方法进行了研究,提出基于颜色识别的搭载机器人和弹跳机器人相对航向角检测方法,提出基于彩色和深度图像匹配原理的子母机器人间距离检测方法。特别地,提出一种基于子母机器人动态协作的抗背景干扰检测方法,弥补了面积过滤法的不足,能在众多的干扰物中准确定位弹跳机器人。最后,对子母机器人的基本运动性能及搭载回收相关方法进行了实验研究。在增加了色标、调节机构等负重后,弹跳机器人可弹跳75cm高,30cm远,自复位和航向调节方法测试结果验证了机构设计的有效性：在设定的速度倍率下,搭载机器人直行2米的横向和纵向偏差均在1.5cm以内,转弯误差在5°以内；搭载机器人和弹跳机器人航向角检测的最大误差分别为2.9°和7.4°,距离检测精度在2cm内；弹跳机器人搭载回收综合实验结果说明了搭载机器人在2米范围内能准确计算弹跳机器人航向角和距离信息,成功定位并追踪弹跳机器人,50次回收实验的成功率为92%,验证了算法的有效性和系统的可行性。
[Abstract]:With the development of robot technology and the continuous expansion of application field, new challenges to the adaptability of robot environment are put forward. Small bouncing robots are well suited to narrow spaces and multi-obstacle environments with excellent barrier performance, but they are also unable to work long hours long distances due to their size and ability to survive; in addition, given the complexity of the environment, A single bouncing robot may not be able to complete the task. Based on the leg hopping robot developed in the laboratory, this paper designs a platform for carrying on the detection, and constructs a sub-mother robot system. The relative positioning method of the sub-mother robot and the recovery strategy of the sub-robot are studied. Firstly, the design scheme of bouncing robot and carrying robot is planned. In view of the shortcomings of the bouncing robot developed in the early stage, two kinds of self-reset and course adjusting methods are proposed, which can effectively improve the kinematic performance of the bouncing robot. A double-sided circular color standard and driving mechanism are designed for position and pose detection in the head of a bouncing robot. The sensor and control circuit of bouncing robot is designed. The robot can be wirelessly controlled by the operator or the robot. The hardware system of carrying robot is designed, which includes vehicle body subsystem, RGB-D information feedback subsystem, wireless communication subsystem and monitoring terminal. Secondly, on the basis of hardware, the detection method and recovery strategy of bouncing robot are designed, the position and pose detection method based on RGB-D information is studied, and the relative heading angle detection method of carrying robot and bouncing robot based on color recognition is proposed. Based on the matching principle of color and depth images, a distance detection method for submother robots is proposed. In particular, an anti-background interference detection method based on the dynamic cooperation of the sub-mother robot is proposed, which makes up for the deficiency of the area filtering method and can accurately locate the bouncing robot in a large number of interference objects. Finally, the basic motion performance of the sub-mother robot and the related methods of loading and recovering are studied experimentally. After adding the weight of color standard and adjusting mechanism, the bouncing robot can bounce 75cm 30 cm high. The test results of self-reset and course adjusting method verify the validity of the mechanism design: under the set speed ratio, The lateral and longitudinal deviations are within 1.5cm and the turning error is less than 5 掳, the maximum errors of heading angle detection of robot and hopping robot are 2.9 掳and 7.4 掳, respectively, and the precision of distance detection is within 2cm. The experimental results show that the hopping robot can accurately calculate the heading angle and distance information of the hopping robot in a range of 2 meters. The success rate of successfully locating and tracking the bouncing robot for 50 times is 92, which verifies the validity of the algorithm and the feasibility of the system.
【学位授予单位】：东南大学
【学位级别】：硕士
【学位授予年份】：2016
【分类号】：TP242

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