Baidu USA LLC

Liyang Wang

Liangjun Zhang

Jinxin Zhao

Zhixian Ye

Qiangqiang Guo

Autonomous excavator has developed rapidly in recent years because of a shortage of labor and hazardous working environments for operating excavators. Presented herein are embodiments of a novel hierarchical planning system for autonomous machines, such as excavators. In one or more embodiments, the overall planning system comprises a high-level task planner for task division and base movement planning, and general sub-task planners with motion primitives, which include both arm and base movement in the case of an excavator. Using embodiments of the system architecture, experiments were performed for the trench and pile removal tasks in the real world and for the large-scale material loading tasks in a simulation environment. The results show that the system architecture embodiments and planner method embodiments generate effective task and motion plans that perform well in autonomous excavation.

Hierarchical planning for autonomous machinery on various tasks

Presented herein are embodiments of a two-stage methodology that integrates data-driven imitation learning and model-based trajectory optimization to generate optimal trajectories for autonomous excavators. In one or more embodiments, a deep neural network using demonstration data to mimic the operation patterns of human experts under various terrain states, including their geometry shape and material type. A stochastic trajectory optimization methodology is used to improve the trajectory generated by the neural network to ensure kinematics feasibility, improve smoothness, satisfy hard constraints, and achieve desired excavation volumes. Embodiments were tested on a Franka robot arm equipped with a bucket end-effector. Embodiments were also evaluated on different material types, such as sand and rigid blocks. Experimental results showed that embodiments of the two-stage methodology that comprises combining expert knowledge and model optimization increased the excavation weights by up to 24.77% with low variance.

Imitation learning and model integrated trajectory planning

The present disclosure discloses an engineering machinery equipment, and a method, system, and storage medium for safety control thereof, and relates to the field of artificial intelligence, automatic control, and engineering machinery technologies. A method can include: acquiring spatial sensing data of a work area; performing obstacle detection based on the spatial sensing data to determine a position of an obstacle within the work area; determining a safe working range of a mechanical structural component of the engineering machinery equipment based on the position of the obstacle within the work area; and controlling a working range of the mechanical structural component of the engineering machinery equipment based on the safe working range.

Engineering machinery equipment, and method, system, and storage medium for safety control thereof

The present disclosure discloses an engineering machinery equipment, and a method, system, and storage medium for operation trajectory planning thereof, and relates to the field of artificial intelligence, automatic control, and engineering machinery technologies. A method can include: acquiring three-dimensional sensing data of a material pile, to construct a three-dimensional model of the material pile based on the three-dimensional sensing data; determining a loading operation position of the engineering machinery equipment on the material pile based on the three-dimensional model of the material pile and structural design information of the engineering machinery equipment; and acquiring position information of a mechanical structural component of the engineering machinery equipment, and performing operation trajectory planning based on the position information of the mechanical structural component and the loading operation position, to generate an operation trajectory of the mechanical structural component executing a material loading operation.

Engineering machinery equipment, and method, system, and storage medium for operation trajectory planning thereof

Embodiments of the present disclosure disclose a method, apparatus, device, and storage medium for controlling a guide robot, and relate to the field of artificial intelligence, robots, and multi-sensor fusion technologies. A specific embodiment of the method includes: acquiring a state of the guide robot, a state of a user, and a position of an obstacle; generating a state update equation for a combined system of the guide robot and the user based on the state of the guide robot and the state of the user; generating a collision-free global path based on the position of the obstacle; generating a control command based on the state update equation for the combined system and the collision-free global path; and driving the guide robot to move based on the control command.

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Sunnyvale, CA, United States of America

Liyang Wang