Autonomous Excavator Remote Computing for Digging Sequence Planning
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Solution Overview
Problem
Existing excavator computing systems face computational constraints that prevent them from processing complex sensor data for optimal digging location and dumping sequence determination during autonomous operations, and they are prone to premature failure in harsh conditions.
Innovation Solution
A method and system where a machine transmits data from sensor devices to remote computing devices for processing, generating digging information that includes optimal sequences of digging and dumping locations, and adjusts the digging operation based on updated information.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Speed
If typical computing devices process sensor data locally, then real-time control is maintained, but computational complexity and processing time increase beyond device capabilities
Solution Approach 1:
The patent extracts the computationally intensive data processing function from the excavator's local computing device and relocates it to a remote server. The local device retains only lightweight functions for data collection and basic control, while the remote server performs complex sensor data processing, digging location determination, and dumping sequence optimization. This extraction resolves the contradiction by maintaining real-time responsiveness locally while offloading computational complexity to remote infrastructure.
2Power
If more computing power is allocated locally, then processing capability improves, but device reliability decreases due to harsh operating conditions
Solution Approach 1:
The patent introduces a communication network as an intermediary between the excavator and the remote server. This intermediary enables the excavator to access high-performance computing resources remotely without exposing its local computing hardware to the harsh operating conditions that would otherwise degrade reliability. The communication network acts as a buffer, allowing the excavator to leverage remote computing power while maintaining operational reliability in challenging environments.
3Manufacturing precision
If complex algorithms are executed locally, then digging optimization improves, but energy consumption increases
Solution Approach 1:
The patent extracts the energy-intensive complex algorithms for digging optimization and dumping sequence determination from the excavator's local system and relocates them to a remote server. The local system only performs lightweight tasks such as sensor data acquisition and executing pre-computed optimization results. This extraction dramatically reduces the excavator's energy consumption while maintaining high digging precision, as the computationally demanding operations are performed remotely where energy resources are more abundant.
Data Source
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AI summary
A machine may include an implement; one or more sensor devices; and a controller. The controller may be configured to receive, from the one or more sensor devices, data regarding a ground surface on which the machine is to perform a digging operation; transmit the data to one or more remote computing devices to cause the one or more remote computing devices to generate digging information based on the data; and receive the digging information from the one or more remote computing devices. The digging information may include information identifying a sequence of digging locations in an area of the ground surface and information identifying corresponding dumping locations. Based on the digging information, the controller may be configured to cause the machine to navigate to a digging location of the digging locations, and cause the implement to initiate the digging operation at the digging location.