To solve dynamic obstacle avoidance problems, a novel algorithm was put forward with the advantages of wireless sensor network (WSN). In view of moving velocity and direction of both the obstacles and robots, a mathem...To solve dynamic obstacle avoidance problems, a novel algorithm was put forward with the advantages of wireless sensor network (WSN). In view of moving velocity and direction of both the obstacles and robots, a mathematic model was built based on the exposure model, exposure direction and critical speeds of sensors. Ant colony optimization (ACO) algorithm based on bionic swarm intelligence was used for solution of the multi-objective optimization. Energy consumption and topology of the WSN were also discussed. A practical implementation with real WSN and real mobile robots were carried out. In environment with multiple obstacles, the convergence curve of the shortest path length shows that as iterative generation grows, the length of the shortest path decreases and finally reaches a stable and optimal value. Comparisons show that using sensor information fusion can greatly improve the accuracy in comparison with single sensor. The successful path of robots without collision validates the efficiency, stability and accuracy of the proposed algorithm, which is proved to be better than tradition genetic algorithm (GA) for dynamic obstacle avoidance in real time.展开更多
A dead reckoning system for a wheeled mobile robot was designed, and the method for robot’s pose estimation in the 3D environments was presented on the basis of its rigid-body kinematic equations. After analyzing the...A dead reckoning system for a wheeled mobile robot was designed, and the method for robot’s pose estimation in the 3D environments was presented on the basis of its rigid-body kinematic equations. After analyzing the locomotion architecture of mobile robot and the principle of proprioceptive sensors, the kinematics model of mobile robot was built to realize the relative localization. Considering that the research on dead reckoning of mobile robot was confined to the 2 dimensional planes, the locomotion of mobile robot in the 3 coordinate axis direction was thought over in order to estimate its pose on uneven terrain. Because the computing method in a plane is rather mature, the calculation in height direction is emphatically represented as a key issue. With experimental results obtained by simulation program and robot platform, the position of mobile robot can be reliably estimated and the localization precision can be effectively improved, so the effectiveness of this dead reckoning system is demonstrated.展开更多
为有效解决单一传感器同时定位与地图构建(simultaneous localization and mapping, SLAM)定位精度低、障碍物识别不全问题,提出一种多传感器融合的SLAM方法。通过将RGB-D相机采集的点云进行降采样、滤波处理,极大降低算法的计算量。利...为有效解决单一传感器同时定位与地图构建(simultaneous localization and mapping, SLAM)定位精度低、障碍物识别不全问题,提出一种多传感器融合的SLAM方法。通过将RGB-D相机采集的点云进行降采样、滤波处理,极大降低算法的计算量。利用点云库对激光点云和降采样RGB-D相机点云进行融合,融合的点云利用PL-ICP完成点云配准,提高对外部环境的准确识别。利用扩展卡尔曼滤波融合IMU和轮式里程计与点云进行位姿匹配,保证定位的精度。实验结果表明,该方法可以有效提高对室内建图和导航的精度。展开更多
针对移动机器人噪声模型不确定性导致定位算法鲁棒性弱、精度低的问题,提出一种基于奇异值分解(Singular Value Decomposition,SVD)的自适应无迹H_(∞)滤波定位算法。该算法利用无迹H_(∞)滤波融合多传感器数据估计移动机器人位姿,并通...针对移动机器人噪声模型不确定性导致定位算法鲁棒性弱、精度低的问题,提出一种基于奇异值分解(Singular Value Decomposition,SVD)的自适应无迹H_(∞)滤波定位算法。该算法利用无迹H_(∞)滤波融合多传感器数据估计移动机器人位姿,并通过自适应调节滤波器参数γ,提高了移动机器人的定位精度。同时为了提高算法的鲁棒性,采用SVD分解代替常规Cholesky分解,避免了误差协方差矩阵在数值迭代过程中出现负定的情况。实验结果表明:相较于扩展H_(∞)滤波和粒子滤波算法,基于SVD分解的自适应无迹H_(∞)滤波定位算法具有精度高、鲁棒性强的优势。展开更多
基金Project(60475035) supported by the National Natural Science Foundation of China
文摘To solve dynamic obstacle avoidance problems, a novel algorithm was put forward with the advantages of wireless sensor network (WSN). In view of moving velocity and direction of both the obstacles and robots, a mathematic model was built based on the exposure model, exposure direction and critical speeds of sensors. Ant colony optimization (ACO) algorithm based on bionic swarm intelligence was used for solution of the multi-objective optimization. Energy consumption and topology of the WSN were also discussed. A practical implementation with real WSN and real mobile robots were carried out. In environment with multiple obstacles, the convergence curve of the shortest path length shows that as iterative generation grows, the length of the shortest path decreases and finally reaches a stable and optimal value. Comparisons show that using sensor information fusion can greatly improve the accuracy in comparison with single sensor. The successful path of robots without collision validates the efficiency, stability and accuracy of the proposed algorithm, which is proved to be better than tradition genetic algorithm (GA) for dynamic obstacle avoidance in real time.
基金Project(60234030) supported by the National Natural Science Foundation of China
文摘A dead reckoning system for a wheeled mobile robot was designed, and the method for robot’s pose estimation in the 3D environments was presented on the basis of its rigid-body kinematic equations. After analyzing the locomotion architecture of mobile robot and the principle of proprioceptive sensors, the kinematics model of mobile robot was built to realize the relative localization. Considering that the research on dead reckoning of mobile robot was confined to the 2 dimensional planes, the locomotion of mobile robot in the 3 coordinate axis direction was thought over in order to estimate its pose on uneven terrain. Because the computing method in a plane is rather mature, the calculation in height direction is emphatically represented as a key issue. With experimental results obtained by simulation program and robot platform, the position of mobile robot can be reliably estimated and the localization precision can be effectively improved, so the effectiveness of this dead reckoning system is demonstrated.
文摘为有效解决单一传感器同时定位与地图构建(simultaneous localization and mapping, SLAM)定位精度低、障碍物识别不全问题,提出一种多传感器融合的SLAM方法。通过将RGB-D相机采集的点云进行降采样、滤波处理,极大降低算法的计算量。利用点云库对激光点云和降采样RGB-D相机点云进行融合,融合的点云利用PL-ICP完成点云配准,提高对外部环境的准确识别。利用扩展卡尔曼滤波融合IMU和轮式里程计与点云进行位姿匹配,保证定位的精度。实验结果表明,该方法可以有效提高对室内建图和导航的精度。
文摘针对移动机器人噪声模型不确定性导致定位算法鲁棒性弱、精度低的问题,提出一种基于奇异值分解(Singular Value Decomposition,SVD)的自适应无迹H_(∞)滤波定位算法。该算法利用无迹H_(∞)滤波融合多传感器数据估计移动机器人位姿,并通过自适应调节滤波器参数γ,提高了移动机器人的定位精度。同时为了提高算法的鲁棒性,采用SVD分解代替常规Cholesky分解,避免了误差协方差矩阵在数值迭代过程中出现负定的情况。实验结果表明:相较于扩展H_(∞)滤波和粒子滤波算法,基于SVD分解的自适应无迹H_(∞)滤波定位算法具有精度高、鲁棒性强的优势。
