Legged Robot Dock Association Control for Multi-Dock Recharging
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Solution Overview
Problem
Existing robotic systems face challenges in efficiently and dynamically performing docking maneuvers, particularly in environments with multiple docks, leading to inefficiencies and computational issues due to limited docking options and varying battery levels.
Innovation Solution
The system employs dock association data to dynamically identify and instruct robots to perform docking maneuvers at appropriate docks based on sensor data, battery levels, and mission data, allowing for dynamic assignment and reassignment of docking locations.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If a robot is limited to a single dock assignment, then the system is simpler to manage, but the robot cannot adapt to dynamic situations such as dock occupancy or varying battery levels
Solution Approach 1:
The system segments the dock assignment by creating multiple dock associations for each robot, where each association represents a different dock that the robot can use. This segmentation allows the robot to have multiple docking options rather than being limited to a single dock, thereby improving adaptability while managing complexity through structured data organization.
Solution Approach 2:
The dock association data structure is designed to be dynamic, allowing the primary dock and secondary dock assignments to be updated in real-time based on current system conditions. This enables the robot to adapt to changing situations such as dock occupancy, maintenance status, or varying battery levels, transforming a static single-dock system into a dynamic multi-dock system.
2Loss of information
If the robot must compute distances to all docks in the environment, then the robot has complete information for docking decisions, but computational time and processing load increase
Solution Approach 1:
The system extracts and prioritizes only the most relevant dock information for each robot by establishing primary and secondary dock associations. Instead of processing all docks in the environment, the robot receives pre-filtered dock data that has been selected based on relevance to the robot's current mission and state, thereby reducing computational load while maintaining sufficient information for docking decisions.
Solution Approach 2:
The system performs preliminary dock selection and association setup before the robot needs to make docking decisions. By pre-establishing primary and secondary dock associations based on mission requirements and robot characteristics, the system prepares the docking information in advance, so that when the robot needs to dock, it can quickly access pre-computed associations rather than calculating distances to all docks at the moment of need.
3Speed
If the robot docks at any available dock, then docking speed is maximized, but the robot may not reach optimal charging locations efficiently
Solution Approach 1:
The system applies local quality by assigning different dock associations (primary and secondary) with different characteristics and priorities based on local conditions. Each dock association has specific qualities such as distance, charging capacity, and mission relevance that are tailored to the robot's current needs, allowing the robot to select the most appropriate dock rather than treating all docks uniformly, thereby optimizing both docking speed and mission efficiency.
Solution Approach 2:
The system incorporates feedback mechanisms where the robot's current state (battery level, mission progress, location) continuously informs dock association selection. The primary and secondary dock assignments are dynamically adjusted based on real-time feedback about the robot's needs and environmental conditions, ensuring that the robot docks at optimal locations that balance speed with overall mission efficiency.
Data Source
AI summary
Systems and methods are described for docking a legged robot. A system can obtain sensor data from one or more sensors of the legged robot. The system can obtain dock association data associated with the legged robot. The dock association data may indicate an association of two or more docks to the legged robot. Based on the dock association data, the system can identify a dock of the two or more docks. The system can instruct the legged robot to perform a docking maneuver relative to the dock based on the sensor data and based on identifying the dock. For example, the system can instruct the legged robot to dock at the dock.