Systems and methods for robotic path planning
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Solution Overview
Problem
Conventional mobile robots face challenges in optimizing both task performance and navigation in dynamic environments, struggling to efficiently plan paths that balance task execution and destination travel while avoiding obstacles and adapting to changing conditions.
Innovation Solution
The method involves generating a cost map of the environment, creating masks for projected paths, determining mask costs based on the cost map, and selecting the optimal path for the robot to travel, which includes adjusting the cost map and masks in response to obstacles and user feedback to ensure safe and efficient navigation and task completion.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If conventional mobile robots follow a set path to a destination, then navigation is simple and reliable, but the robot cannot efficiently perform tasks in dynamic environments or adapt to changing conditions
Solution Approach 1:
The cost map is dynamically updated based on robot position, task progress, and environmental feedback. The path planning system continuously recalculates optimal paths by adjusting cost values in the cost map, allowing the robot to adapt to changing conditions while maintaining computational efficiency through incremental updates rather than complete replanning
Solution Approach 2:
The path planning is divided into multiple candidate paths with different priorities and cost structures. The system segments the navigation problem into task-oriented path selection and obstacle avoidance, allowing independent optimization of each aspect while combining them through the cost map framework
2Productivity
If the robot optimizes for task completion, then productivity increases, but navigation safety and obstacle avoidance may be compromised
Solution Approach 1:
Different regions of the cost map are assigned different quality characteristics: task-related locations receive cost adjustments based on task priority, while obstacle regions receive high cost penalties for safety. This allows the system to simultaneously optimize for productivity in task areas and safety in navigation areas through spatially differentiated cost weighting
Solution Approach 2:
The system continuously monitors robot position, task completion status, and environmental obstacles, using this feedback to dynamically adjust the cost map and reselect optimal paths. This closed-loop control ensures that productivity goals are maintained while safety constraints are continuously enforced through real-time path reassessment
3Adaptability or versatility
If the robot uses complex path planning algorithms to optimize both task and navigation, then adaptability improves, but computational time and processing requirements increase
Solution Approach 1:
The system generates a limited set of candidate paths with different characteristics rather than exhaustively exploring all possible paths. By evaluating only the most promising candidates based on task priority and environmental constraints, the system achieves sufficient adaptability while significantly reducing computational time and processing requirements
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.