Bipedal Robot Balancing State Control for Precise Object Handling
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
The inefficiencies and challenges in automating tasks such as moving goods within distribution centers and last-mile delivery due to the need for human workers, particularly in maintaining balance and precision during object handling by bipedal robots, lead to reduced efficiency and increased operational costs.
Innovation Solution
A method for bipedal robots to selectively bypass transitions to a standing state during high-precision object handling operations, using feedback-based balancing to determine dynamic balancing states and autonomously deciding when to transition between standing and non-standing states based on task requirements.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If the robot transitions to a standing state during object handling operations, then stability and precision are improved, but time consumption and operational efficiency deteriorate
Solution Approach 1:
The robot dynamically adjusts its balancing state based on real-time task requirements. The system transitions between standing and non-standing states by evaluating the precision needs of current operations, allowing the robot to maintain optimal performance while minimizing transition overhead. This dynamic state management resolves the contradiction by making the standing state available only when precision is required.
Solution Approach 2:
The system changes the balancing state parameter (standing vs. non-standing) based on operational context. By monitoring task requirements and automatically adjusting the balancing state, the system achieves high precision when needed while spending most time in the more efficient non-standing state, thus resolving the time-precision tradeoff.
2Adaptability or versatility
If the robot frequently transitions between standing and non-standing states, then adaptability to task requirements is improved, but wear on movable parts and energy consumption increase
Solution Approach 1:
The system employs feedback-based balancing that continuously monitors operational requirements and adjusts the balancing state accordingly. By using sensor feedback and autonomous decision-making, the robot transitions states only when task requirements demand it, avoiding unnecessary transitions that would waste energy and increase wear on movable parts.
3Extent of automation
If the robot uses feedback-based balancing for autonomous state transitions, then operational autonomy and responsiveness are improved, but computing resource consumption increases
Solution Approach 1:
The system changes computational intensity based on operational context. During routine operations, the system uses simpler balancing controls, reserving intensive feedback-based autonomous state transitions only when task requirements necessitate them. This contextual parameter adjustment reduces overall computing resource consumption while maintaining high automation where needed.
Data Source
AI summary
A method in accordance with at least some embodiments of the present technology includes retrieving, by a legged robot, an object from a pick target while the legged robot is in a first dynamic balancing state. The method further includes carrying, by the legged robot, the object toward a place target spaced apart from the pick target while the legged robot ambulates over a floor surface. The method further includes placing, by the legged robot, the object at the place target while the legged robot is in a second dynamic balancing state. The legged robot in one of the first and second dynamic balancing states maintains its balance while its feet are planted on the floor surface. The legged robot in the other of the first and second dynamic balancing states maintains its balance while at least one of the feet moves relative to the floor surface.