Gradient-Based Navigation for Low-Memory Autonomous Robots
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Solution Overview
Problem
Existing navigation methods for autonomous mobile systems require significant processing power and memory resources, especially when finding trajectories that avoid obstacles, which is complex, time-consuming, and costly for electronic control units.
Innovation Solution
A navigation method that uses unsupervised learning to generate an environmental representation from sensor data, calculating a gradient to determine movement direction, allowing for efficient path selection without a separate path planning algorithm, and adapting trajectory calculation to reduce resource usage.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If a metric map is built and stored in memory for navigation, then path planning can be performed, but processing power and memory resources are significantly consumed
Solution Approach 1:
The patent extracts only the essential gradient information needed for navigation from the complete metric map, storing only gradient vectors and reference point data rather than full environmental representations. This extraction reduces memory requirements while maintaining the ability to compute accurate movement directions toward targets.
Solution Approach 2:
Instead of building a complete metric map and then performing complex path planning algorithms, the patent inverts the approach by directly computing gradient fields from sensor data and using simple gradient descent for navigation. This eliminates the need for traditional path planning algorithms and reduces processing complexity.
2Productivity
If a complete metric map is stored in memory, then trajectory planning can be performed, but memory resources are consumed
Solution Approach 1:
The patent extracts only the gradient field information from the environment, storing minimal data structures consisting of reference points and their associated gradient vectors. This extraction allows trajectory planning to be performed with dramatically reduced memory consumption compared to storing complete metric maps.
3Reliability
If traditional path planning algorithms are executed, then obstacle avoidance trajectories can be found, but processing time is consumed
Solution Approach 1:
The patent replaces complex mechanical path planning algorithms with a physics-inspired gradient descent approach. By substituting iterative optimization algorithms with continuous gradient field computation and simple descent movements, the system achieves real-time obstacle avoidance with minimal processing time.
Solution Approach 2:
The patent performs preliminary computation of gradient fields from sensor data before navigation decisions are required. By pre-computing the gradient information that guides movement, the system eliminates the need for time-consuming path planning during actual navigation, enabling real-time response to obstacles.
Data Source
AI summary
A method and system for navigating a mobile system and corresponding mobile system is disclosed, in particular for autonomous mobile systems such as robots, for example lawn mowers or even smartphones. The mobile device includes at least one sensor, an electronic control unit, and an output unit. The method includes acquiring sensor data on an environment of the mobile device, calculating a gradient of a difference of a target environmental representation and a current environmental representation, and determining a movement direction to reach a target position corresponding to the target environmental representation based on the estimated gradient. The determined movement direction for navigating the mobile device is output, for example to a steering system of the mobile device or to a display. The method can include generating an environmental representation by performing unsupervised learning from the acquired sensor data.