Terrain-Aware Step Planning for Stable Legged Robot Navigation
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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 slow down, collide with objects, and lose balance due to the lack of real-time navigation and step planning that avoids obstacles while maintaining balance and speed.
Innovation Solution
A method for planning a sequence of steps in constrained environments using image data from sensors like stereo cameras, LIDAR, or LADAR to generate body-obstacle, ground height, and step-obstacle maps, which allows the robot to plan a body path and step path that avoids obstacles and maintains balance by adjusting nominal step patterns and constraints like center of pressure offset and self-collision avoidance.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If real-time navigation and step planning is implemented to avoid obstacles, then collision avoidance and safety are improved, but computational complexity and processing time increase
Solution Approach 1:
The navigation system is divided into separate functional modules: body path planning generates high-level navigation paths while step path planning handles detailed leg coordination. This segmentation allows each module to operate independently with appropriate computational resources, reducing overall system complexity while maintaining collision avoidance capabilities through coordinated operation of both planners
Solution Approach 2:
The body path planner generates preliminary navigation paths before the step path planner creates detailed leg movements. This preliminary action separates strategic decision-making from tactical execution, allowing the system to pre-compute safe body trajectories and then efficiently generate corresponding step sequences without recalculating the entire path plan in real-time
2Stability of the object's composition
If complex terrain awareness and constraint planning is used to maintain balance, then balance stability is improved, but navigation speed decreases
Solution Approach 1:
The step path planner dynamically adjusts leg movements based on real-time terrain constraints and body position feedback. The system continuously recalculates step parameters such as foot placement location, swing duration, and leg trajectory to maintain balance stability while adapting to changing environmental conditions, enabling stable navigation at varying speeds rather than requiring conservative fixed-speed operation
Solution Approach 2:
The system changes key parameters including center of pressure offset ranges, step timing, and foot placement coordinates to optimize the balance-speed tradeoff. By dynamically adjusting these parameters based on terrain difficulty and robot state, the system can maintain balance stability on complex terrain while preserving navigation speed through efficient parameter selection rather than conservative slow operation
3Reliability
If multiple constraint checks are performed for each step plan, then navigation safety and balance are improved, but processing time increases
Solution Approach 1:
The body path planner performs preliminary safety validation by generating body-obstacle maps and identifying no-body regions before the step path planner creates detailed leg movements. This preliminary constraint checking ensures that subsequent step planning operates within pre-validated safe zones, reducing the computational burden of repeated full-system safety checks and enabling faster real-time processing while maintaining navigation safety
Solution Approach 2:
Constraint checking is segmented into body-level constraints (no-body regions, body-obstacle collisions) handled by the body path planner and step-level constraints (no-step regions, self-collision, center of pressure) handled by the step path planner. This segmentation distributes computational workload across planning layers, allowing each to perform focused constraint validation efficiently without redundant full-system checks, thus improving processing time while preserving navigation safety through layered verification
Data Source
AI summary
A method for terrain and constraint planning a step plan includes receiving, at data processing hardware of a robot, image data of an environment about the robot from at least one image sensor. The robot includes a body and legs. The method also includes generating, by the data processing hardware, a body-obstacle map, a ground height map, and a step-obstacle map based on the image data and generating, by the data processing hardware, a body path 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 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.