Zhang Xiaolong, Huang Yu, Rong Youmin, Li Gen, Wang Hui, Liu Chao
School of Mechanical Science and Engineering, Huazhong University of Science and Technology, Wuhan 430074, China.
Guangzhou Institute of Advanced Technology, Chinese Academy of Sciences, Guangzhou 511400, China.
Sensors (Basel). 2021 Jan 6;21(2):335. doi: 10.3390/s21020335.
With the rapid development of robotics, wheeled mobile robots are widely used in smart factories to perform navigation tasks. In this paper, an optimal trajectory planning method based on an improved dolphin swarm algorithm is proposed to balance localization uncertainty and energy efficiency, such that a minimum total cost trajectory is obtained for wheeled mobile robots. Since environmental information has different effects on the robot localization process at different positions, a novel localizability measure method based on the likelihood function is presented to explicitly quantify the localization ability of the robot over a prior map. To generate the robot trajectory, we incorporate localizability and energy efficiency criteria into the parameterized trajectory as the cost function. In terms of trajectory optimization issues, an improved dolphin swarm algorithm is then proposed to generate better localization performance and more energy efficiency trajectories. It utilizes the proposed adaptive step strategy and learning strategy to minimize the cost function during the robot motions. Simulations are carried out in various autonomous navigation scenarios to validate the efficiency of the proposed trajectory planning method. Experiments are performed on the prototype "Forbot" four-wheel independently driven-steered mobile robot; the results demonstrate that the proposed method effectively improves energy efficiency while reducing localization errors along the generated trajectory.
随着机器人技术的快速发展，轮式移动机器人在智能工厂中被广泛用于执行导航任务。本文提出了一种基于改进海豚群算法的最优轨迹规划方法，以平衡定位不确定性和能源效率，从而为轮式移动机器人获得总代价最小的轨迹。由于环境信息在不同位置对机器人定位过程有不同影响，提出了一种基于似然函数的新型可定位性度量方法，以明确量化机器人在已知地图上的定位能力。为了生成机器人轨迹，我们将可定位性和能源效率标准纳入参数化轨迹作为代价函数。针对轨迹优化问题，提出了一种改进的海豚群算法，以生成具有更好定位性能和更高能源效率的轨迹。它利用所提出的自适应步长策略和学习策略，在机器人运动过程中最小化代价函数。在各种自主导航场景中进行了仿真，以验证所提出轨迹规划方法的有效性。在原型“Forbot”四轮独立驱动转向移动机器人上进行了实验；结果表明，该方法在降低沿生成轨迹的定位误差的同时，有效地提高了能源效率。
