As a large-scale mining excavator,the electric shovel(ES)has been extensively employed in open-pit mines for overburden removal and mineral loading.In the development of unmanned operations for ES,dynamic excavation trajectory planning is essential,as it directly influences operational efficiency and energy consumption by guiding the dipper during excavation.However,conventional optimization-based methods for excavation trajectory planning typically start from scratch,resulting in a time-consuming process that fails to meet real-time requirements.To address this challenge,we propose an innovative online trajectory planning framework based on physics-informed neural networks(PINNOTP)that utilizes advanced data-driven techniques.The input to PINNOTP consists of onsite working conditions,including the initial state of the ES and the material surface being excavated.The output is a smooth,polynomial-based curve that serves as the reference trajectory for the dipper.To ensure smooth execution of the generated trajectory,prior domain knowledge-such as physics-based target-oriented constraints,essential system dynamics,and mechanical constraints-is explicitly incorporated into the loss function during training.A case study is presented to validate the proposed method,demonstrating that PINNOTP effectively addresses the challenges of online excavation trajectory planning.
Tao FuZhengguo HuTianci ZhangQiushi BiXueguan Song
探讨基于机器人操作系统(Robot Operating System,ROS)的智能挖掘机控制系统设计方案。构建一个模块化的系统架构,包含感知模块、决策模块、路径规划模块、控制模块以及仿真模块。通过深入比较快速扩展随机树(Rapidly-exploring Random Tree,RRT)算法和RRT-connect算法,发现RRT-connect算法更适合挖掘机运动规划,所设计的系统在复杂动态环境下展现出良好的规划效率和运行稳定性,为智能挖掘机的实际应用提供了可靠的技术支持。
