Robotic Path Planning Using Cost Maps and Mask-Based Path Selection
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Solution Overview
Problem
Conventional mobile robots face challenges in optimizing both task performance and navigation, particularly in dynamic environments where they need to travel to a destination while performing tasks like cleaning, as they struggle to balance these behaviors effectively.
Innovation Solution
The system generates a cost map of the environment, creates masks representing potential paths, and determines mask costs based on obstacles and recovery conditions, allowing the robot to select and actuate on the most suitable path for navigation and task completion.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Speed
If conventional mobile robots follow a set path to a destination, then navigation is achieved, but task performance in dynamic environments deteriorates
Solution Approach 1:
The system dynamically generates multiple candidate paths and evaluates them based on current environmental conditions and task requirements. The path planning is not static but adapts in real-time by generating new paths and selecting optimal ones based on changing conditions, resolving the contradiction between maintaining navigation speed and adapting to dynamic task performance needs.
Solution Approach 2:
The path planning process is segmented into multiple candidate paths that are generated and evaluated separately. Each path can be independently assessed for its suitability for both navigation and task performance, allowing the system to select the optimal path based on current conditions rather than following a single rigid trajectory.
2Productivity
If conventional mobile robots perform tasks in a space without a destination, then task completion is achieved, but navigation efficiency deteriorates
Solution Approach 1:
The path planning system serves multiple functions simultaneously: it plans paths for navigation to destinations and generates task sequences for performing tasks in specific locations. This multi-functional approach allows the robot to optimize both navigation efficiency and task completion rate by using a unified planning framework that considers both objectives.
3Speed
If robots optimize for destination travel, then navigation efficiency is improved, but task performance optimization deteriorates
Solution Approach 1:
The system adds an additional dimension to path evaluation by considering not just navigation metrics but also task performance metrics. Paths are evaluated in a multi-dimensional space that includes both travel efficiency and task completion potential, allowing the selection of paths that optimize overall productivity rather than just travel speed.
4Productivity
If robots optimize for task performance, then task completion is improved, but navigation efficiency deteriorates
Solution Approach 1:
The system changes the parameters used for path evaluation from single-objective metrics to multi-objective parameters that include both task performance and navigation efficiency. By adjusting and weighting these parameters dynamically, the system can balance task completion rate with navigation time, selecting paths that optimize the overall productivity rather than sacrificing navigation efficiency.
Data Source
AI summary
Systems and methods for robotic path planning are disclosed. In some implementations of the present disclosure, a robot can generate a cost map associated with an environment of the robot. The cost map can comprise a plurality of pixels each corresponding to a location in the environment, where each pixel can have an associated cost. The robot can further generate a plurality of masks having projected path portions for the travel of the robot within the environment, where each mask comprises a plurality of mask pixels that correspond to locations in the environment. The robot can then determine a mask cost associated with each mask based at least in part on the cost map and select a mask based at least in part on the mask cost. Based on the projected path portions within the selected mask, the robot can navigate a space.