Mixed-Mode Mobile Robot for Autonomous Navigation and Manual Positioning
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Solution Overview
Problem
Current autonomous mobile robots are limited in their ability to navigate and perform tasks in dynamic and variable environments, such as hospitals and restaurants, due to their reliance on full autonomy, which is economically unfeasible and difficult to implement, and they lack manual maneuverability features that would allow human operators to easily position them next to objects or in tight spaces.
Innovation Solution
The development of mixed mode mobile robots that can seamlessly transition between autonomous and manual modes, allowing human operators to manually move and position the robots using ergonomic handles and sensors, such as capacitive sensors or torque sensors, enabling easy navigation in tight spaces and around humans, and featuring hub motors with high-resolution encoders and hall sensors for autonomous operation.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If full autonomy is implemented in mobile robots, then robots can reliably perform time-consuming and difficult tasks, but the utility is limited and economically unfeasible for simple tasks
Solution Approach 1:
The robot system dynamically switches between autonomous and manual modes based on task requirements and environmental conditions. The control system can transition from fully autonomous operation to manual teleoperation, allowing the same robot to handle both complex autonomous tasks and simple manual positioning tasks across different domains.
Solution Approach 2:
The mobile robot is designed with multi-functionality to operate in both autonomous and manual modes, making it universally applicable across diverse domains including healthcare, logistics, and service industries. This dual-mode capability allows a single robot platform to replace multiple specialized systems.
2Extent of automation
If full autonomy is implemented in mobile robots, then robots can navigate structured environments, but they struggle with simple positioning tasks in dynamic environments
Solution Approach 1:
The robot employs dynamic mode switching that allows transition from autonomous navigation to manual positioning mode. When autonomous navigation succeeds, the robot performs automated path planning and execution. When precise positioning is needed in dynamic environments, the system switches to manual teleoperation mode, combining the benefits of both approaches.
Solution Approach 2:
A teleoperation interface acts as an intermediary between the operator and the robot, enabling intuitive manual control when autonomous positioning fails. This intermediary system includes haptic feedback and visual aids that make manual positioning as easy as autonomous operation.
3Ease of operation
If manual maneuverability is added to mobile robots, then human operators can easily position robots, but the robot complexity increases
Solution Approach 1:
The control system dynamically adjusts its complexity by switching between autonomous and manual modes. When in manual mode, the robot simplifies its control architecture to direct teleoperation, reducing computational complexity. When in autonomous mode, the full navigation stack is activated. This dynamic adaptation allows manual maneuverability without permanently increasing system complexity.
4Adaptability or versatility
If manual handles and sensors are added for teleoperation, then human-robot interaction is enhanced, but the device complexity increases
Solution Approach 1:
The teleoperation handles and sensors serve multiple functions: they enable manual control, provide tactile feedback during autonomous operation, and act as additional sensors for navigation. This multi-functionality justifies the added complexity by providing multiple benefits from a single component set.
Solution Approach 2:
The manual control handles are merged with the autonomous navigation sensor suite, allowing the same physical components to serve both teleoperation and autonomous functions. This merging reduces the overall complexity compared to having separate systems for manual and autonomous operation.
Applied Scientific Principles
This section explains which scientific principles are used to turn an abstract innovation direction into a practical engineering solution.
Function Achieved in This Case
Enables widespread commercial adoption of robots in various domains by allowing intuitive and cost-effective manual positioning and navigation, enhancing human-robot interaction and safety, while maintaining high-performance autonomous capabilities, thus overcoming the limitations of existing autonomous robots.
Implementation Method 1
capacitive sensors or torque sensors
Implementation Method 2
capacitive sensors or torque sensors
Implementation Method 3
hub motors with high-resolution encoders and hall sensors
Data Source
AI summary
A mixed mode robot is provided having an autonomous mode, wherein the robot moves autonomously, and a manual mode, wherein the robot is passive and allows manual manipulation by a user. The mixed mode robot is wheeled and in an embodiment is powered by one or more direct drive motor while in the autonomous mode. One or more handholds are adapted for use by a user to move the robot when the robot is in the manual mode. A processor associated with the robot places the robot in a selected one of the autonomous mode and the manual mode, such that the robot is easily moved by a user when in the manual mode.