Robot Swarm Navigation Using Neural Motion Vectors
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Solution Overview
Problem
Current swarming technologies face challenges in decentralized robot swarm navigation, particularly in complex and unknown environments, where they lack agility, efficiency, and resilience due to inadequate spatial awareness and navigation strategies.
Innovation Solution
A method utilizing a neural network-based approach that determines robot-based and objective-based synaptic weights, incorporating oscillating signals and phase comparisons to calculate motion vectors, allowing robots to navigate based on spatial relationships and environmental cues, similar to mammalian brain circuits for spatial navigation.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If traditional swarming navigation methods are used, then the system structure is simple, but the navigation efficiency and spatial awareness are insufficient in complex environments
Solution Approach 1:
The patent replaces traditional mechanical control systems with a neuroinspired computational system. Each robot is equipped with a neural network that processes spatial information and generates navigation commands autonomously, substituting centralized mechanical control with distributed intelligent processing. This enables improved navigation efficiency while maintaining relatively simple individual robot structures.
Solution Approach 2:
The patent introduces oscillating signals as an intermediary mechanism between robots. These signals mediate spatial relationships and enable robots to infer positions and movements of others without direct line-of-sight communication. This intermediary allows efficient coordination in complex environments while keeping individual robot sensors and processors relatively simple.
2Reliability
If decentralized navigation is implemented, then the system resilience is improved, but the spatial awareness capability deteriorates
Solution Approach 1:
The patent merges multiple information sources including direct sensor data, oscillating signals from other robots, and neural network processing into a unified spatial awareness system. Each robot combines these inputs to build a comprehensive environmental model, achieving both decentralized operation and precise spatial understanding through integrated information processing.
Solution Approach 2:
The patent adds the temporal dimension through oscillating signals with specific frequencies and phases. Robots encode spatial information in the temporal patterns of oscillations, allowing them to distinguish between simultaneous signals from different directions and achieve accurate spatial awareness through time-based differentiation rather than relying solely on spatial sensor arrays.
3Use of energy by moving object
If oscillating signals are used for communication, then the energy consumption is reduced, but the information exchange capability is limited
Solution Approach 1:
The patent uses periodic oscillating signals instead of continuous communication. Robots transmit information through rhythmic on/off patterns that encode spatial and identity data. This periodic transmission dramatically reduces energy consumption compared to continuous communication while maintaining effective information exchange through the temporal patterns of the oscillations.
Solution Approach 2:
The patent encodes multiple information parameters within the oscillating signals by varying frequency, phase, and amplitude. A single oscillating signal can convey robot identity, position, velocity, and intent through different parameter combinations, maximizing information exchange capability while minimizing the number of separate communication channels and associated energy consumption.
Data Source
AI summary
Methods, systems, and apparatus, including computer programs encoded on computer -storage media, for swarming technology. In some implementations, the system can be modeled on the spatial reasoning process found in rodents and other mammals. A plurality of synaptic weights can be formed within a neural network. The neural network along with additional data of a system can be used to determine motion vectors for one or more devices within a group of devices or swarm. The motion vectors can help determine where the devices within a group of devices are located at a given time. Motion vectors can direct devices to areas of high activity in a similar manner to how spatial cells in a brain direct animals in accordance with high activity rates of particular cells within their brain.