Swarm Robot Pack Control via Local Relative Positioning
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Solution Overview
Problem
Existing methods for piloting swarms of robots, such as drones or underwater vehicles, face challenges in maintaining a predetermined pack configuration and trajectory while being resource-intensive and less robust, especially in environments where communication is difficult and precision is hard to achieve.
Innovation Solution
A method where each robot generates a guidance setpoint to correct its speed vector based on relative positioning constraints with neighbors, using a common set of constraints that includes ideal distances and trajectories, allowing robots to maintain a pack configuration without relying on absolute positions or extensive communication, enabling robust and adaptive control.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If centralized control or full communication between robots is used to maintain swarm configuration, then positioning accuracy is improved, but communication cost and system complexity increase significantly
Solution Approach 1:
The patent divides the swarm into local neighborhoods where each robot only communicates with and maintains configuration relative to its immediate neighbors rather than all robots. This segmentation reduces communication complexity from O(n²) to O(k) where k is the average neighborhood size, while still achieving acceptable positioning accuracy through local constraints.
Solution Approach 2:
The patent applies different control strategies at different levels: local relative positioning constraints are enforced between neighboring robots, while global trajectory following is achieved through individual guidance commands. This local quality approach allows each robot to have simplified local interactions while the swarm collectively achieves coordinated motion.
2Measurement precision
If absolute positioning with high precision is used to maintain swarm configuration, then relative positioning accuracy is improved, but system robustness and adaptability decrease
Solution Approach 1:
The patent merges absolute trajectory following with relative configuration maintenance into a unified control framework. Each robot receives a global guidance command for trajectory following while simultaneously enforcing local relative positioning constraints with neighbors. This combination allows the swarm to maintain accurate relative positioning without relying solely on high-precision absolute positioning, thereby improving robustness.
Solution Approach 2:
The patent makes the swarm configuration dynamic by allowing robots to adapt their relative positions based on local constraints and environmental conditions rather than maintaining rigid absolute positions. The control system dynamically adjusts individual robot commands to satisfy both global trajectory requirements and local configuration constraints, enhancing adaptability and robustness.
3Reliability
If robots are placed closer together to avoid gaps in exploration, then coverage completeness is improved, but exploration efficiency decreases
Solution Approach 1:
The patent implements feedback through local relative positioning constraints where each robot continuously monitors and adjusts its position relative to neighbors. This feedback mechanism ensures that robots maintain optimal spacing to avoid gaps in coverage while preventing excessive clustering that would reduce exploration efficiency. The system dynamically balances coverage completeness and exploration speed.
4Stability of the object's composition
If constant monitoring of all robots is applied to maintain configuration and manage collisions, then swarm cohesion is improved, but system adaptability and robustness decrease
Solution Approach 1:
The patent segments the monitoring task by having each robot only monitor and interact with its local neighborhood rather than constantly monitoring all robots. Local relative positioning constraints enforce swarm cohesion through distributed peer-to-peer monitoring, reducing the computational burden and increasing system adaptability while maintaining overall swarm coherence.
Data Source
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AI summary
A method for controlling a set of robots comprising, for each robot, a step of developing a guidance instruction for said robot at a given time, the guidance instructions of the set of robots being defined from a single set of constraints comprising a pack constraint comprising at least one set of at least one first relative positioning constraint between one robot and the neighbours of said robot leading in the pack, such that the set of robots forms a pack having a predefined shape, the method further comprising a step of controlling the robot in such a way as to comply with the guidance instruction.