Autonomous Ground Vehicle Swarm Task Allocation Under Distributed Control
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Solution Overview
Problem
Coordinating multi-agent swarms of autonomous ground vehicles is challenging due to the need for robust and flexible control systems that can adapt to changing environments, effective communication, and efficient task allocation, especially when faced with varying agent capabilities and dynamic conditions.
Innovation Solution
An autonomous ground vehicle system that includes processors, sensors, and wireless communication modules to translate high-level mission objectives into specific tasks, assign tasks efficiently, and execute them while adhering to constraints, using cost functions and translation algorithms to optimize task allocation and execution.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of operation
If centralized control approaches are used to coordinate multi-agent swarms, then task allocation and coordination can be managed effectively, but the system becomes vulnerable to single points of failure and cannot scale effectively as the number of agents increases
Solution Approach 1:
The patent segments the centralized control function into distributed control modules deployed across multiple agents. Each agent runs local translation algorithms and cost function evaluations independently, eliminating the single point of failure while maintaining coordinated task allocation through peer-to-peer communication and consensus mechanisms.
Solution Approach 2:
The patent transitions from a single-layer centralized control architecture to a multi-layer distributed architecture where control functions operate at multiple levels: individual agent level (local translation and execution), swarm level (consensus and coordination), and mission level (overall objective management). This dimensional change enables both scalability and reliability.
2Productivity
If the number of agents in a swarm is increased to expand coverage and capability, then mission efficiency and redundancy improve, but control system complexity and communication overhead increase significantly
Solution Approach 1:
The patent divides the control system into modular translation algorithms that can be independently executed by each agent. This segmentation allows the system to scale to large numbers of agents without proportionally increasing overall system complexity, as each agent processes only its own task translations and local coordination.
Solution Approach 2:
The patent employs cost functions with adjustable parameters that adapt to swarm size and mission requirements. These parameter changes enable the control system to maintain optimal performance across varying swarm configurations without requiring complete reconfiguration of the control architecture.
3Productivity
If translation algorithms are used to convert high-level mission objectives into agent-specific tasks, then task allocation efficiency improves, but the system must handle varying agent capabilities and dynamic environmental conditions
Solution Approach 1:
The patent implements local quality by tailoring task translations to each agent's specific capabilities, sensors, and actuators. The translation algorithms evaluate agent-specific parameters and generate customized task representations that match individual agent characteristics, enabling efficient allocation while accommodating heterogeneity across the swarm.
Solution Approach 2:
The patent employs dynamic translation algorithms that continuously adapt to changing environmental conditions and mission requirements. The cost functions are updated in real-time based on sensor feedback and mission progress, allowing the system to maintain optimal task allocation efficiency while responding to dynamic conditions.
Data Source
AI summary
The present disclosure provides an autonomous ground vehicle system configured to be deployed in and coordinate with a multi-agent swarm of autonomous ground vehicles. The system includes one or more processors, one or more memories, one or more wireless communication modules, one or more motors, and one or more sensors configured to observe an environment through which the autonomous ground vehicle system navigates. The system is configured to receive a multi-agent behavior request requiring multiple agents to be performed, translate the request into one or more tasks each configured to be performed by a single agent, assign at least a first task to the autonomous ground vehicle system based on a cost function indicating efficiency, and autonomously execute the first task causing the system to navigate through the environment using the sensors and motor.