Decentralized Robot Coordination Using DLT Smart Contracts
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Centralized robotic control systems face challenges in complex environments with multiple parties involved, as they lack decentralization, leading to single points of failure and inadequate robot-to-robot communication, which hinders efficient task execution and collaboration.
Innovation Solution
A decentralized robotic control system leveraging distributed ledger technology (DLT) allows multiple robots to communicate and collaborate by translating service requests into tasks, identifying necessary capabilities, and selecting candidate robots based on proof of computation, ensuring secure and modular operation.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of operation
If centralized control mechanism is used, then control over robots is simplified and coordination is easier, but single point of failure risk increases and robot-to-robot communication is hindered
Solution Approach 1:
The patent divides the centralized control architecture into multiple decentralized nodes distributed across the network. Each node can independently control and coordinate robots, eliminating the single point of failure. The control functions are segmented and distributed rather than concentrated in one central server.
Solution Approach 2:
The patent introduces a blockchain-based distributed ledger as an intermediary layer that enables direct peer-to-peer communication and coordination between robots and control nodes. This intermediary provides trustless verification and coordination without requiring a central authority, allowing robots to communicate and coordinate directly through smart contracts.
2Productivity
If centralized control system is used, then task assignment is straightforward, but robot-to-robot learning and collaboration are limited
Solution Approach 1:
The patent implements a feedback mechanism where robots record their operational data, sensor readings, and task completion status on the blockchain. Other robots can access this recorded information through the distributed ledger, enabling them to learn from the experiences and successes of their peers without requiring centralized data aggregation and redistribution.
Solution Approach 2:
The blockchain serves multiple functions simultaneously: it acts as a task assignment registry, a communication channel between robots, a database for storing robot experiences and sensor data, and a verification mechanism for task completion. This universal platform enables both efficient task assignment and inter-robot learning through the same distributed infrastructure.
3Adaptability or versatility
If multiple entities control their own robots, then entity autonomy is maintained, but centralized coordination becomes difficult
Solution Approach 1:
The patent implements dynamic control where entities can choose to participate in the decentralized network on their own terms. Each entity's robot controller can independently join or leave the network, and the system dynamically adapts to changing participation levels. Smart contracts enable flexible task assignment and coordination that adjusts to the current state of available robots and entities without requiring rigid centralized management.
Data Source
AI summary
Systems and methods of the present disclosure leverage distributed ledger technology (DLT) to provide decentralized control of cooperative tasks performed by a plurality of robots. Characteristics of the plurality of robots may be stored in a distribute ledger, which may be provided by a blockchain or a distributed database system. When a service request is received, a set of tasks may be identified for providing the requested service and the robot characteristics recorded to the distributed ledger may be used to identify a list of candidate robots possessing characteristics corresponding to the set of tasks may be identified. A smart contract may be utilized to select one or more candidate robots for performing the task and to verify the selected robot(s) successfully completed the task. State information associated with operation of the selected robot(s) may be monitored to verify task completion.