Multi-Robot Integration for Collective Mobility in Complex Environments
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Solution Overview
Problem
Current robotic systems face limitations in mobility and adaptability, particularly in navigating complex or dynamic environments, which restrict their ability to reach hard-to-reach or hazardous locations and respond to environmental changes, affecting their effectiveness in various industrial tasks.
Innovation Solution
An automated system integrates multiple robots using machine learning models and real-time monitoring to determine collective mobility, enabling them to perform activities in diverse environments by selecting and configuring secondary robots to enhance the primary robot's capabilities.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If a single robot is used to perform tasks, then the system structure is simple, but the mobility and adaptability are limited
Solution Approach 1:
The patent combines multiple robots into a collaborative system where they work together to perform tasks. The system integrates several robotic units with different mobility capabilities (wheeled, legged, flapping) to achieve collective adaptability while maintaining manageable system complexity through modular architecture and centralized control.
Solution Approach 2:
The robotic system is designed with multi-functional capabilities where different robot types (wheeled, legged, flapping) can perform various tasks depending on environmental conditions. The system can adapt its composition and configuration based on task requirements, providing universal functionality across diverse operating scenarios.
2Adaptability or versatility
If multiple robots are integrated to enhance mobility, then the adaptability improves, but the system complexity increases
Solution Approach 1:
The system segments the robotic fleet into distinct units with specialized mobility capabilities (wheeled robots for flat terrain, legged robots for rough terrain, flapping robots for aerial access). Each segment operates semi-independently but can be coordinated through the centralized control system, managing complexity through functional segmentation.
Solution Approach 2:
A centralized control system acts as an intermediary between multiple robotic units, coordinating their actions and managing their integration. This mediator handles task allocation, path planning, and real-time coordination, reducing the complexity burden on individual robots while enabling collective adaptability.
3Productivity
If robots are configured for specific environments, then the task performance improves, but the ease of operation decreases
Solution Approach 1:
The robotic system employs dynamic configuration where robot assignments and operational parameters are adjusted in real-time based on environmental conditions and task requirements. The centralized control system continuously monitors and reconfigures the robotic fleet, enabling high task performance without requiring manual reconfiguration by operators.
Solution Approach 2:
The robotic system performs self-configuration and self-coordination through autonomous decision-making algorithms and inter-robot communication. The centralized control system automatically optimizes task allocation and operational parameters, reducing the need for human intervention in configuration management while maintaining high task performance.
Data Source
AI summary
Integration of a set of robots to perform an activity includes obtaining a mobility parameter of each of a plurality of robots. The collective mobility of the set of robots from the plurality of robots is determined. The set of robots are integrated in an environment to perform an activity in case the collective mobility satisfies a target mobility threshold. The set of robots further performs, based on the integration of the set of robots, the activity in the environment.