Mobile Robot Route Planning Around Human Presence and Obstacles
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Solution Overview
Problem
Existing mobile robotic devices lack the ability to autonomously plan the most efficient work operational plan in a dynamic environment, considering factors like human presence, surface types, and dynamic obstacles.
Innovation Solution
The method involves a processor on the robot obtaining data on human presence and surface types, executing work duties such as cleaning, capturing data during operations, and altering navigational routes or work duties based on collected data to optimize efficiency.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If the robot executes work duties autonomously without human intervention, then productivity is improved, but the robot may collide with dynamic obstacles such as humans
Solution Approach 1:
The robot performs preliminary sensing and detection of the environment before executing work duties. The processor continuously obtains data about human presence and surface types, allowing the robot to plan its navigation route in advance and avoid collisions while maintaining productivity.
Solution Approach 2:
The robot dynamically adjusts its navigation route and work duties based on real-time environmental data. When humans or obstacles are detected, the processor alters the planned path dynamically, enabling the robot to adapt to changing conditions while continuing to execute work duties efficiently.
2Adaptability or versatility
If the robot continuously senses and adapts to environmental changes, then adaptability is improved, but device complexity increases
Solution Approach 1:
The processor performs multiple functions using a single integrated system: it senses environmental data, identifies surface types, detects human presence, plans navigation routes, and adjusts work duties. This multi-functionality reduces the need for separate dedicated systems for each task, thereby limiting complexity while maintaining high adaptability.
Solution Approach 2:
The robot autonomously processes environmental data and makes decisions about its own navigation and work duties without external control. The processor self-manages the complexity of sensing, analysis, and decision-making, allowing the system to adapt to environmental changes while keeping the control architecture relatively simple.
3Productivity
If the robot alters navigation route based on real-time data, then productivity is improved by avoiding obstacles, but loss of time occurs due to route recalculation
Solution Approach 1:
The processor continuously gathers environmental data and pre-plans the navigation route before the robot begins its work duties. By having a preliminary route plan ready, the robot can execute work efficiently without needing to recalculate routes in real-time, thus minimizing time loss while still being able to adapt to obstacles.
Solution Approach 2:
The navigation system is designed to dynamically adjust routes only when necessary, based on real-time detection of obstacles or changes in the environment. This dynamic approach allows the robot to maintain productivity by avoiding unnecessary route recalculations while still responding appropriately to actual obstacles.
Data Source
AI summary
A method for autonomously planning work duties of a robot within an environment of the robot, including: obtaining, with a processor of the robot, first data indicative of a presence or an absence of at least one human within the environment at a particular time; actuating, with the processor, the robot to execute work duties based on the first data, wherein: the robot executes the work duties when the first data indicates the absence of the at least one human from the environment; and the work duties comprise cleaning at least a portion of the environment; capturing, with at least one sensor of a plurality of sensors disposed on the robot, second data while the robot executes the work duties; and altering, with the processor, a navigational route of the robot or the work duties based on the second data.