Teng Zhang, Xiangji Wang, Fusheng Zha, Fucheng Liu
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引用次数: 0
Abstract
Three-dimensional navigation of legged robots is crucial for field exploration and post-disaster rescue. Existing optimization-based local trajectory planners predominantly focus on obstacle avoidance, neglecting negative obstacles (e.g., pits) and varying ground features (e.g., different terrain types). Additionally, non-overlapping areas between the planned space in three-dimensional trajectory planning and the robot’s actual reachable space lead to decision-making issues between crossing and obstacle avoidance, making it challenging to differentiate between passable and hazardous areas, thus impacting navigation safety and stability. To address these limitations, we propose a novel visual local planner, LSF-Planner (Visual Local Planner for Legged Robots Based on Ground Structure and Feature Information). The LSF-Planner employs a multi-layer local perception map that integrates ground feature semantics, sensor range, and negative obstacles (e.g., voids detected by depth sensors) to construct a ground reliability representation. The Label2Grad method is introduced to convert this representation into gradient layers, incorporating a ground reliability penalty function into trajectory optimization. By incorporating constraints on the center of mass height and crossing angles, LSF-Planner effectively differentiates between traversable and hazardous areas. Experimental results show that LSF-Planner significantly outperforms existing methods in 3D trajectory planning, enhancing the navigation performance of legged robots in unstructured environments.
期刊介绍:
Autonomous Robots reports on the theory and applications of robotic systems capable of some degree of self-sufficiency. It features papers that include performance data on actual robots in the real world. Coverage includes: control of autonomous robots · real-time vision · autonomous wheeled and tracked vehicles · legged vehicles · computational architectures for autonomous systems · distributed architectures for learning, control and adaptation · studies of autonomous robot systems · sensor fusion · theory of autonomous systems · terrain mapping and recognition · self-calibration and self-repair for robots · self-reproducing intelligent structures · genetic algorithms as models for robot development.
The focus is on the ability to move and be self-sufficient, not on whether the system is an imitation of biology. Of course, biological models for robotic systems are of major interest to the journal since living systems are prototypes for autonomous behavior.