Multi-Robot Formation Control via Virtual Point
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Solution Overview
Problem
Current systems face challenges in enabling humans to control multiple autonomous robots (AMRs) or drones to converge into desired formations while avoiding collisions with each other and obstacles in a decentralized manner, requiring complex algorithms and communication methods.
Innovation Solution
A decentralized algorithm that allows humans to intuitively control multiple robots by transforming global coordinate state information into relative positions, using sensors and communication to maintain desired formations in 2D or 3D environments, ensuring collision avoidance and convergence towards a desired shape around a human or point.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of operation
If a decentralized algorithm is used to enable human control of multiple robots, then ease of operation is improved, but device complexity increases due to the need for complex coordination algorithms
Solution Approach 1:
The patent introduces a virtual control point as an intermediary that simplifies human control of multiple robots. Instead of directly controlling each robot, the human operator controls a single virtual point, and the decentralized algorithm automatically coordinates the robots to converge around this point in desired formations. This mediator approach resolves the contradiction by hiding the algorithmic complexity from the operator while maintaining simple control interfaces.
Solution Approach 2:
The system employs self-service through autonomous coordination algorithms that enable robots to automatically adjust their positions and formations without continuous human intervention. Once the virtual control point is set, the robots autonomously compute their trajectories and maintain desired spatial relationships, reducing the operational burden on humans while managing the inherent system complexity through intelligent automation.
2Adaptability or versatility
If multiple robots operate in shared workspaces without lane markings or special conditioning, then adaptability is improved, but reliability decreases due to increased risk of collisions
Solution Approach 1:
The patent implements dynamic formation control where robots continuously adjust their positions, velocities, and orientations based on real-time spatial relationships with the virtual control point and each other. This dynamic coordination allows robots to adapt to various workspace configurations and obstacles without pre-defined paths or lane markings, while maintaining safe distances through continuous computational adjustment of trajectories, thus achieving both adaptability and reliability.
3Productivity
If robots converge into desired formations around a point, then productivity is improved through coordinated operation, but device complexity increases due to trajectory control requirements
Solution Approach 1:
The patent segments the complex multi-robot coordination problem into individual robot control tasks. Each robot independently computes its own trajectory to the virtual control point based on its current position and the desired formation geometry. This segmentation allows robots to operate autonomously while maintaining coordinated formations, improving productivity through efficient spatial utilization without requiring a monolithic complex control system.
Data Source
AI summary
A robot configured to be operable within a multi-robot system. The robot includes an input configured to receive global coordinate state information of the robot and of any neighboring robots or obstacles; and processing circuitry configured to: transform the global coordinate state information into a relative coordinate system that is with respect to the robot and is based on a type of desired formation of the robot and any neighboring robots or obstacles around a point; generate a reference formation algorithm which is based on the desired formation; and controlling, based on the reference formation algorithm and tracking errors between the desired formation and a current state of the robot, a trajectory of the robot to converge towards the desired formation while avoiding collisions with any neighboring robots or obstacles.