Legged Robot Gait Controller Obstacle Avoidance
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Solution Overview
Problem
Selecting a suitable gait for a legged robot can be challenging, as some gait variations may result in instability or undesirable movement, and existing systems lack fine-grained control over locomotion, especially when encountering obstacles.
Innovation Solution
The implementation involves a list of gait controllers that map robot states and steering commands to output parameters, with a validity check ensuring the gait can be achieved, and a cost function to prioritize gaits based on similarity to target commands and obstacle proximity, allowing for real-time adjustment of movement to avoid collisions.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If multiple gait variations are provided for different cadences and steering directions, then the robot's adaptability to different locomotion requirements is improved, but the complexity of gait selection and control increases
Solution Approach 1:
The gait controller dynamically selects and transitions between different gait variations based on real-time robot state and steering commands. The system continuously adjusts the active gait variation to match current locomotion requirements, making the gait selection process adaptive rather than static.
Solution Approach 2:
The system uses feedback from the robot's current state (position, velocity, orientation) and compares it with desired steering commands to determine the appropriate gait variation. This closed-loop control enables intelligent gait selection without requiring complex manual configuration.
2Ease of operation
If gait selection is based on robot state and steering commands, then fine-grained control over locomotion is achieved, but the computational complexity and processing time increase
Solution Approach 1:
The gait controller is segmented into modular components: state processing module, steering command processing module, gait variation selection module, and transition management module. This segmentation allows each component to handle specific tasks independently, reducing overall computational complexity while maintaining fine-grained control.
Solution Approach 2:
The system changes control parameters (cadence, duty factor, step length) based on robot state and steering commands to achieve fine-grained locomotion control. By adjusting these parameters within existing gait frameworks rather than selecting from entirely different control architectures, computational complexity is reduced.
3Reliability
If validity checks are performed for each gait controller, then gait stability and feasibility are ensured, but the processing time for gait selection increases
Solution Approach 1:
Validity checks and feasibility assessments are performed in advance during gait controller initialization and ordering. By pre-evaluating which gait controllers are valid for specific robot states and steering commands, the system avoids performing these checks repeatedly during real-time operation, thus reducing processing time while maintaining reliability.
4Adaptability or versatility
If gait controllers are ordered in a list based on similarity to target commands, then the relevance and appropriateness of gait selection is improved, but the complexity of ordering and traversal increases
Solution Approach 1:
The ordering of gait controllers in the list is determined by comparing parameters (cadence, steering direction, duty factor) between available gait controllers and target commands. Gait controllers with parameter values closer to the target commands are placed higher in the list, enabling relevant gait selection without complex algorithms.
Data Source
AI summary
Based on input steering commands, a legged robot may select a target gait. Based on the target gait, the legged robot may obtain a list of gait controllers. Each gait controller may define a gait of the legged robot, and include validity tests and steering commands. The legged robot may apply a cost function to the gait controllers, where the cost for a gait controller is based on a difference between the steering commands of the gait controller and the input steering commands, and a proximity of the legged robot to obstacles should the legged robot operate according the gait controller. The legged robot may reorder the list in increasing magnitude of the cost function, and traverse the list until a validity test associated with a particular gait controller passes. The legged robot may actuate its legs according to the steering commands of the particular gait controller.