A large number of logistics operations are needed to transport fabric rolls and dye barrels to different positions in printing and dyeing plants, and increasing labor cost is making it difficult for plants to recruit ...A large number of logistics operations are needed to transport fabric rolls and dye barrels to different positions in printing and dyeing plants, and increasing labor cost is making it difficult for plants to recruit workers to complete manual operations. Artificial intelligence and robotics, which are rapidly evolving, offer potential solutions to this problem. In this paper, a navigation method dedicated to solving the issues of the inability to pass smoothly at corners in practice and local obstacle avoidance is presented. In the system, a Gaussian fitting smoothing rapid exploration random tree star-smart(GFS RRT^(*)-Smart) algorithm is proposed for global path planning and enhances the performance when the robot makes a sharp turn around corners. In local obstacle avoidance, a deep reinforcement learning determiner mixed actor critic(MAC) algorithm is used for obstacle avoidance decisions. The navigation system is implemented in a scaled-down simulation factory.展开更多
为解决传统方法在铁路变配电所敷设施工时完全依靠二维设计布线图纸,易发生扭绞、交叉以及浪费物料等问题,基于建筑信息模型(BIM,Building Information Modeling)技术对铁路变配电所的线缆敷设进行优化。利用改进的快速扩展随机树(RRT*,...为解决传统方法在铁路变配电所敷设施工时完全依靠二维设计布线图纸,易发生扭绞、交叉以及浪费物料等问题,基于建筑信息模型(BIM,Building Information Modeling)技术对铁路变配电所的线缆敷设进行优化。利用改进的快速扩展随机树(RRT*,Rapidly Exploring Random Tree*)算法,在三维视图下进行智能布线,解决了线缆布放规划复杂,工艺要求高,施工工艺难以掌握等问题,避免了施工过程中扭绞等问题的发生,同时实现了布线路径最优化。此外,还可以三维动画的形式对整个线缆敷设过程进行模拟和演示,并生成包含路由、长度、规格型号的线缆清单,显著提高了施工效率和工艺质量。展开更多
Mobile robots have been used for many industrial scenarios which can realize automated manufacturing process instead of human workers. To improve the quality of the optimal rapidly-exploring random tree(RRT^(*)) for p...Mobile robots have been used for many industrial scenarios which can realize automated manufacturing process instead of human workers. To improve the quality of the optimal rapidly-exploring random tree(RRT^(*)) for planning path in dynamic environment, a high-quality dynamic rapidly-exploring random tree(HQD-RRT^(*)) algorithm is proposed in this paper, which generates a high-quality solution with optimal path length in dynamic environment. This method proceeds in two stages: initial path generation and path re-planning. Firstly, the initial path is generated by an improved smart rapidly-exploring random tree(RRT^(*)-SMART) algorithm, and the state tree information is stored as prior knowledge. During the process of path execution, a strategy of obstacle avoidance is proposed to avoid moving obstacles. The cost and smoothness of path are considered to re-plan the initial path to improve the path quality in this strategy. Compared with related work, a higher-quality path in dynamic environment can be achieved in this paper. HQD-RRT^(*) algorithm can obtain an optimal path with better stability. Simulations on the static and dynamic environment are conducted to clarify the efficiency of HQD-RRT^(*) in avoiding unknown obstacles.展开更多
基金National Natural Science Foundation of China (No.61903078)。
文摘A large number of logistics operations are needed to transport fabric rolls and dye barrels to different positions in printing and dyeing plants, and increasing labor cost is making it difficult for plants to recruit workers to complete manual operations. Artificial intelligence and robotics, which are rapidly evolving, offer potential solutions to this problem. In this paper, a navigation method dedicated to solving the issues of the inability to pass smoothly at corners in practice and local obstacle avoidance is presented. In the system, a Gaussian fitting smoothing rapid exploration random tree star-smart(GFS RRT^(*)-Smart) algorithm is proposed for global path planning and enhances the performance when the robot makes a sharp turn around corners. In local obstacle avoidance, a deep reinforcement learning determiner mixed actor critic(MAC) algorithm is used for obstacle avoidance decisions. The navigation system is implemented in a scaled-down simulation factory.
文摘为解决传统方法在铁路变配电所敷设施工时完全依靠二维设计布线图纸,易发生扭绞、交叉以及浪费物料等问题,基于建筑信息模型(BIM,Building Information Modeling)技术对铁路变配电所的线缆敷设进行优化。利用改进的快速扩展随机树(RRT*,Rapidly Exploring Random Tree*)算法,在三维视图下进行智能布线,解决了线缆布放规划复杂,工艺要求高,施工工艺难以掌握等问题,避免了施工过程中扭绞等问题的发生,同时实现了布线路径最优化。此外,还可以三维动画的形式对整个线缆敷设过程进行模拟和演示,并生成包含路由、长度、规格型号的线缆清单,显著提高了施工效率和工艺质量。
基金supported by the Program for Youth Innovative Research Team in the University of Shandong Province in China(2019KJN010)。
文摘Mobile robots have been used for many industrial scenarios which can realize automated manufacturing process instead of human workers. To improve the quality of the optimal rapidly-exploring random tree(RRT^(*)) for planning path in dynamic environment, a high-quality dynamic rapidly-exploring random tree(HQD-RRT^(*)) algorithm is proposed in this paper, which generates a high-quality solution with optimal path length in dynamic environment. This method proceeds in two stages: initial path generation and path re-planning. Firstly, the initial path is generated by an improved smart rapidly-exploring random tree(RRT^(*)-SMART) algorithm, and the state tree information is stored as prior knowledge. During the process of path execution, a strategy of obstacle avoidance is proposed to avoid moving obstacles. The cost and smoothness of path are considered to re-plan the initial path to improve the path quality in this strategy. Compared with related work, a higher-quality path in dynamic environment can be achieved in this paper. HQD-RRT^(*) algorithm can obtain an optimal path with better stability. Simulations on the static and dynamic environment are conducted to clarify the efficiency of HQD-RRT^(*) in avoiding unknown obstacles.
