Multi-Robot Control Architecture for Agricultural Coordination
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Solution Overview
Problem
Current agricultural robotic systems face challenges in achieving full autonomy and efficient operation in unpredictable environments, particularly in agricultural settings, where coordination of multiple robots is complex and requires robust control architectures to ensure safe and reliable performance.
Innovation Solution
A five-layer Individual Robot Control Architecture (IRCA) is developed, incorporating a Sensing Layer, Behavior Layer, Finite State Machine Layer, Control Module Layer, and Actuator Layer, along with a Global Information Module (GIM) for inter-robot coordination, enabling robust and fault-tolerant operation and prioritization of sensor information for effective task execution.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If multiple robots are deployed to increase productivity, then productivity is improved, but coordination complexity increases
Solution Approach 1:
The control architecture is segmented into multiple independent layers (sensing layer, behavior layer, finite state machine layer, control module layer, actuator layer), where each layer handles specific functions. This segmentation allows individual robot control to be developed and tested independently before integration into multi-robot systems, reducing overall coordination complexity while maintaining high productivity.
Solution Approach 2:
The Global Information Module (GIM) acts as an intermediary that manages information exchange and coordination between multiple robots. The GIM receives global information from the environment and distributes it to individual robots, while also collecting local information from robots and synthesizing it into global context, thereby simplifying multi-robot coordination.
2Extent of automation
If autonomous operation is implemented to reduce labor costs, then labor costs are reduced, but control system complexity increases
Solution Approach 1:
The autonomous control system is divided into five functional layers, each with specific responsibilities. The sensing layer acquires environmental data, the behavior layer processes this data into actionable information, the finite state machine layer makes decisions based on robot state and environment, the control module layer generates control commands, and the actuator layer executes commands. This segmentation reduces control system complexity by making each layer's function well-defined and manageable.
Solution Approach 2:
The architecture incorporates continuous feedback loops at multiple levels. Sensors continuously monitor the environment and robot state, feeding information back through the behavior layer and finite state machine layer to adjust control commands in real-time. This feedback mechanism enables autonomous operation while maintaining manageable complexity through iterative adjustment rather than complex open-loop control.
3Manufacturing precision
If sensor information is extensively processed to improve task execution, then task execution accuracy is improved, but information processing time increases
Solution Approach 1:
Sensor information processing is segmented across different layers: the sensing layer performs initial data acquisition and filtering, the behavior layer processes spatial and temporal patterns, and the finite state machine layer makes high-level decisions based on processed information. This segmentation allows critical processing to occur in parallel across layers, improving task execution accuracy without excessive delays.
Solution Approach 2:
The sensing layer performs preliminary processing of sensor data, including filtering, noise reduction, and basic feature extraction, before passing information to higher layers. This preliminary action reduces the complexity and time required for subsequent processing in the behavior and finite state machine layers, maintaining accuracy while reducing overall processing time.
Data Source
AI summary
A multiple robot control architecture including a plurality of robotic agricultural machines including a first and second robotic agricultural machine. Each robotic agricultural machine including at least one controller configured to implement a plurality of finite state machines within an individual robot control architecture (IRCA) and a global information module (GIM) communicatively coupled to the IRCA. The GIMs of the first and second robotic agricultural machines being configured to cooperate to cause said first robotic agricultural machine and said second agricultural machine to perform at least one agricultural task.