Zhang Feng, Li Leijun, Xu Peiquan, Zhang Pengyu
School of Materials Science and Engineering, Shanghai University of Engineering Science, Shanghai 201620, China.
Department of Chemical and Materials Engineering, University of Alberta, Edmonton, AB T6G 1H9, Canada.
Sensors (Basel). 2024 May 13;24(10):3100. doi: 10.3390/s24103100.
High-precision positioning and multi-target detection have been proposed as key technologies for robotic path planning and obstacle avoidance. First, the Cartographer algorithm was used to generate high-quality maps. Then, the iterative nearest point (ICP) and the occupation probability algorithms were combined to scan and match the local point cloud, and the positions and attitudes of the robot were obtained. Furthermore, Sparse Matrix Pose Optimization was carried out to improve the positioning accuracy. The positioning accuracy of the robot in x and y directions was kept within 5 cm, the angle error was controlled within 2°, and the positioning time was reduced by 40%. An improved timing elastic band (TEB) algorithm was proposed to guide the robot to move safely and smoothly. A critical factor was introduced to adjust the distance between the waypoints and the obstacle, generating a safer trajectory, and increasing the constraint of acceleration and end speed; thus, smooth navigation of the robot to the target point was achieved. The experimental results showed that, in the case of multiple obstacles being present, the robot could choose the path with fewer obstacles, and the robot moved smoothly when facing turns and approaching the target point by reducing its overshoot. The proposed mapping, positioning, and improved TEB algorithms were effective for high-precision positioning and efficient multi-target detection.
高精度定位和多目标检测已被提出作为机器人路径规划和避障的关键技术。首先，使用Cartographer算法生成高质量地图。然后，将迭代最近点（ICP）算法和占用概率算法相结合，对局部点云进行扫描和匹配，从而获得机器人的位置和姿态。此外，还进行了稀疏矩阵姿态优化以提高定位精度。机器人在x和y方向上的定位精度保持在5厘米以内，角度误差控制在2°以内，定位时间减少了40%。提出了一种改进的定时弹性带（TEB）算法，以引导机器人安全平稳地移动。引入了一个关键因素来调整路径点与障碍物之间的距离，生成更安全的轨迹，并增加对加速度和末端速度的约束；从而实现了机器人向目标点的平稳导航。实验结果表明，在存在多个障碍物的情况下，机器人能够选择障碍物较少的路径，并且在转弯和接近目标点时通过减少过冲实现了平稳移动。所提出的建图、定位和改进的TEB算法对于高精度定位和高效多目标检测是有效的。
