Terrain-Aware Step Planning for Legged Robots in Constrained Spaces
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Solution Overview
Problem
Legged robotic devices face challenges in navigating constrained environments, such as cluttered rooms and staircases, as they often stop frequently, collide with objects, and lose balance, necessitating a method for real-time step planning that avoids obstacles while maintaining speed and balance.
Innovation Solution
A robotic system that uses image data from sensors like stereo cameras, LIDAR, or LADAR to generate body-obstacle, ground height, and step-obstacle maps, allowing it to plan a body path and step path that avoids collisions and maintains balance by adjusting nominal step patterns based on constraints like center of pressure offset and no-step regions.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If real-time step planning is implemented to avoid obstacles, then navigation safety is improved, but computational complexity increases
Solution Approach 1:
The navigation problem is segmented into two separate planning processes: body path planning and step path planning. The body path planner generates a high-level trajectory avoiding obstacles, while the step path planner refines this into specific leg movements. This segmentation allows each module to focus on specific aspects, reducing overall computational complexity while maintaining safety through coordinated planning.
Solution Approach 2:
The body path is planned in advance as a preliminary action before generating the detailed step path. By pre-computing the overall trajectory and identifying no-step regions beforehand, the system reduces the real-time computational burden during step execution, enabling faster response while ensuring navigation safety.
2Productivity
If frequent stopping is avoided to maintain speed, then navigation efficiency is improved, but balance stability deteriorates
Solution Approach 1:
The step planning system dynamically adjusts step parameters including timing, position, and center of pressure distribution based on real-time balance requirements. By continuously optimizing step characteristics rather than using fixed patterns, the robot can maintain balance during continuous movement without frequent stopping, thus improving navigation efficiency while preserving stability.
Solution Approach 2:
The system changes multiple step parameters simultaneously including step height, step position, and center of pressure offset to maintain balance during continuous locomotion. By dynamically adjusting these parameters based on the planned body trajectory and terrain conditions, the robot achieves stable movement without needing to stop frequently, thereby maintaining both speed and balance.
3Adaptability or versatility
If complex terrain adaptation is implemented, then environment versatility is improved, but processing time increases
Solution Approach 1:
The terrain adaptation process is segmented into distinct stages: image data acquisition, map generation (body-obstacle map, ground height map, step-obstacle map), body path planning, and step path planning. This segmentation allows parallel processing of different tasks and enables the system to handle complex terrains efficiently by processing only relevant information at each stage, reducing overall processing time while maintaining versatility.
Solution Approach 2:
Environmental information is processed in advance to generate comprehensive maps including no-step regions and terrain characteristics before path planning begins. By pre-processing terrain data and identifying constraints beforehand, the system reduces real-time processing requirements during navigation, enabling fast adaptation to complex environments without excessive processing delays.
Data Source
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Figure 2A
Figure 2B~2D
AI summary
A method for terrain and constraint planning a step plan includes receiving, at data processing hardware (36) of a robot (10), image data (17) of an environment (8) about the robot from at least one image sensor (31). The robot includes a body (11) and legs (12). The method also includes generating, by the data processing hardware, a body-obstacle map (112), a ground height map (116), and a step-obstacle map (114) based on the image data and generating, by the data processing hardware, a body path (510) for movement of the body of the robot while maneuvering in the environment based on the body-obstacle map. The method also includes generating, by the data processing hardware, a step path (350) for the legs of the robot while maneuvering in the environment based on the body path, the body-obstacle map, the ground height map, and the step-obstacle map.