Mobile Robot Route Planning for Dynamic Obstacles and Surface Types
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Solution Overview
Problem
Mobile robotic devices face inefficiencies in navigating and performing tasks due to varying work surface types and dynamic obstacles, which affect their operational efficiency and safety, as they struggle to autonomously plan optimal routes and duties without considering environmental changes and surface types.
Innovation Solution
A method where a mobile robotic device processes historical sensor data to determine the most efficient navigational route and work duties, using machine learning to adapt to dynamic obstacles and surface types, and updates its plans based on new sensor data collected during operations, creating a map of the environment and prioritizing tasks to maximize efficiency.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If mobile robotic devices navigate and perform tasks without considering varying work surface types and dynamic obstacles, then the operational process is simple, but the operational efficiency and safety deteriorate
Solution Approach 1:
The system performs preliminary mapping and surface type detection before executing navigation and task execution. Historical sensor data is processed in advance to create environment models, allowing the robot to plan optimal paths that consider surface types and avoid obstacles before encountering them, thereby improving operational efficiency without proportionally increasing real-time complexity
Solution Approach 2:
The navigation and task execution plan is made dynamic and adaptive. The system continuously updates its environmental model based on new sensor data and re-plans routes in real-time to account for dynamic obstacles and changing surface conditions. This allows the robot to maintain high operational efficiency in varying environments while managing complexity through adaptive rather than static planning
2Reliability
If mobile robotic devices avoid all dynamic obstacles and optimize for each surface type, then safety and efficiency improve, but the computational processing required increases
Solution Approach 1:
The system applies different processing strategies to different areas of the environment based on local characteristics. High-risk areas with dynamic obstacles or complex surface types receive more intensive sensor processing and planning attention, while safe, familiar areas use simplified navigation. This localized approach improves safety where needed while conserving computational energy in low-risk zones
Solution Approach 2:
The system dynamically adjusts processing parameters such as sensor sampling rates, map update frequencies, and planning horizon lengths based on environmental conditions and robot state. When battery power is low or computational resources are constrained, the system reduces processing intensity while maintaining essential safety functions, thereby improving reliability without proportionally increasing energy consumption
3Measurement precision
If mobile robotic devices create detailed maps and process historical sensor data, then navigation accuracy improves, but the time required for planning increases
Solution Approach 1:
The system performs preliminary map creation and surface classification during initial exploration phases, building a detailed environmental model in advance. Once the map is established, the system uses it for faster route planning by matching current sensor data against the pre-processed map rather than creating maps in real-time, thereby improving navigation accuracy while reducing planning time during task execution
Solution Approach 2:
The system processes and stores more sensor data than immediately necessary for current navigation decisions, creating an enriched historical dataset. This excessive data collection in advance allows for more accurate environmental modeling and better navigation decisions, while the actual planning process uses only the essential processed information, balancing accuracy with planning speed
Data Source
AI summary
A method for a robot to autonomously plan a navigational route and work duties in an environment of the robot including accessing historical sensor data stored from prior work cycles, determining the navigational route and work duties of the robot by processing probabilities based on the historical sensor data, enacting the navigational route and work duties by the robot, capturing new sensor data while the robot enacts the navigational route and work duties, processing the new sensor data, and altering the navigational route and work duties based on the new sensor data processed.