Path planning
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Solution Overview
Problem
Current path planning algorithms for mobile robotic devices require significant processing power and time, making them costly and difficult to implement on consumer devices like robotic vacuum cleaners, which need to navigate efficiently around obstacles while extending battery life.
Innovation Solution
A method that divides the path-planning task into smaller processes by iteratively processing waypoints, identifying and adjusting their locations based on clearance thresholds and geometrical analysis, reducing processing demands and allowing lower-powered processors to be used.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If traditional path planning algorithms are used to calculate all possible paths and cost functions, then accurate obstacle-free path determination is achieved, but processing power requirements and processing time increase significantly
Solution Approach 1:
The patent segments the continuous path planning problem into discrete grid-based cells and iterative depth-first search steps. By dividing the environment into manageable grid cells and processing paths through iterative exploration rather than calculating all possible paths simultaneously, the algorithm reduces computational complexity while maintaining path accuracy.
Solution Approach 2:
The patent performs preliminary actions by pre-processing the environment map into a grid structure with identified obstacles and free spaces before path planning begins. This preliminary grid construction and obstacle mapping allows the subsequent pathfinding algorithm to operate efficiently on pre-organized data rather than raw sensor inputs.
2Productivity
If powerful processors are used to carry out path planning, then path planning accuracy and speed are improved, but device cost and power consumption increase
Solution Approach 1:
The patent divides the path planning computation into segmented grid cells and iterative depth-first search steps, allowing the processor to handle smaller discrete units rather than computing entire paths at once. This segmentation enables efficient use of lower-power processors by breaking down computational tasks into manageable chunks that can be processed sequentially.
Solution Approach 2:
The patent employs simple data structures and algorithms that can be implemented on low-cost, low-power processors rather than requiring expensive high-performance computing hardware. By using basic grid representations and iterative search methods instead of complex path optimization algorithms, the system achieves acceptable path planning performance on consumer-grade battery-powered devices.
3Reliability
If comprehensive path planning is performed to account for all obstacles, then navigation reliability is improved, but processing time and computational resources increase
Solution Approach 1:
The patent performs preliminary mapping and obstacle identification by constructing a grid representation of the environment before path planning begins. By pre-processing sensor data to identify obstacles and free spaces in advance, the system reduces the computational burden during actual pathfinding while ensuring all obstacles are accounted for in the navigation plan.
Solution Approach 2:
The patent uses iterative depth-first search that periodically explores grid cells and backtracks when obstacles are encountered. This periodic exploration pattern allows the algorithm to systematically cover the entire environment and identify all obstacles while maintaining efficient use of processing time through structured iteration rather than exhaustive computation.
Data Source
AI summary
A method of determining an obstacle-free path between a start point and a target point. The method includes the steps of providing a plurality of waypoints wherein the waypoints are linearly distributed between the start point and the target point; determining a sequence for the waypoints where the first waypoint is positioned at the start point and the last waypoint is positioned at the target point; and processing each of the plurality of waypoints in the sequence iteratively from the second waypoint to the last waypoint. The step of processing for each of the plurality of waypoints comprises identifying an obstacle closest to a waypoint of interest and calculating a first distance between the waypoint of interest and the identified closest obstacle, and subsequently changing the location of the waypoint of interest if the first distance is less than a clearance threshold.