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 system generates a cost map of the environment, projects multiple path trajectories, and adjusts these paths based on obstacle presence and robot-centric orientation, using a cost penalty mechanism to optimize path selection and actuator commands for efficient 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 efficiency is improved, but the ability to perform tasks in the space is compromised
Solution Approach 1:
The robot dynamically switches between navigation mode (following set paths to destinations) and task execution mode (performing tasks in spaces without specific destinations). The system adapts its behavior based on the current operational context, allowing it to efficiently navigate when destinations are involved while also performing cleaning or other tasks when no specific destination is required.
2Productivity
If conventional mobile robots perform tasks in a space without a destination, then task execution is improved, but navigation efficiency deteriorates
Solution Approach 1:
The robot dynamically adjusts its operational mode based on the task requirements. When performing tasks like cleaning in a space without a specific destination, the robot optimizes for task execution efficiency by operating in task mode. When a destination is involved, it switches to navigation mode to optimize travel efficiency.
3Measurement precision
If the robot generates multiple projected paths and evaluates costs, then path selection accuracy is improved, but computational complexity increases
Solution Approach 1:
The path planning process is segmented into distinct phases: generating multiple projected paths, evaluating each path's cost based on predefined criteria, and selecting the optimal path. This segmentation allows the system to manage computational complexity by breaking down the decision-making process into manageable steps while maintaining high path selection accuracy through comprehensive evaluation.
Solution Approach 2:
The robot generates a limited number of projected paths (not all possible paths) that are sufficient for making an optimal decision. By evaluating a representative subset of paths rather than exhaustively analyzing every possible route, the system achieves high path selection accuracy while keeping computational complexity manageable.
4Reliability
If the robot modifies cost map and masks based on recovery conditions, then collision avoidance is improved, but processing time increases
Solution Approach 1:
The robot pre-defines recovery conditions and corresponding modifications to the cost map and masks before actual navigation occurs. By having these modifications prepared in advance based on anticipated scenarios, the robot can quickly respond to collision risks without performing complex real-time calculations, thus improving collision avoidance while minimizing processing time delays.
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.