Autonomous Mobile Speed Control Using Tilt and Obstacle Sensing
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Solution Overview
Problem
Autonomous mobile devices (AMDs) face challenges in safely navigating through physical spaces with varying obstacles, as existing systems often rely on single sensors that can be unreliable in different environmental conditions, leading to erratic speed changes and potential safety hazards.
Innovation Solution
A speed control system for AMDs that utilizes a combination of multiple sensors, including tilt, forward, and downward sensors, to determine safe operating speeds by transitioning between states (normal, caution, slow, and stop) based on sensor data, ensuring accurate obstacle characterization and a smooth navigation experience.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Device complexity
If a single sensor is used to detect obstacles, then the device complexity is reduced, but the reliability of obstacle detection deteriorates in varying environmental conditions
Solution Approach 1:
The patent combines multiple sensors (forward sensors, downward sensors, and tilt sensors) into an integrated sensing system. The forward sensors detect obstacles in the path, downward sensors detect surface characteristics and drops, and tilt sensors detect inclination. By merging these sensor types, the system achieves reliable obstacle detection across varying environmental conditions while maintaining manageable device complexity through unified processing logic.
Solution Approach 2:
The sensor system is designed with multi-functionality where each sensor type serves multiple detection purposes. For example, the combination of forward, downward, and tilt sensors creates a universal detection system that can identify various obstacle types (elevations, depressions, inclines, declines) and environmental conditions (carpets, drops, ramps) using the same processing framework, thereby improving reliability without proportionally increasing complexity.
2Reliability
If multiple sensors are used to improve detection accuracy, then the reliability of obstacle characterization is improved, but the device complexity increases
Solution Approach 1:
The patent segments the sensing system into distinct functional modules: forward sensors for path obstacle detection, downward sensors for surface characterization, and tilt sensors for inclination detection. Each sensor type is processed independently through dedicated logic that evaluates its specific data, then the results are integrated to determine overall operating state. This segmentation reduces integration complexity while maintaining high characterization accuracy.
Solution Approach 2:
The system dynamically adjusts the weighting and integration of sensor data based on current operating conditions. The processing logic evaluates sensor inputs in real-time and adapts the decision-making process to prioritize relevant sensor types for specific obstacle scenarios, thereby managing complexity through dynamic rather than static integration strategies.
3Productivity
If the device operates at high speed to improve productivity, then the task completion rate increases, but the safety risk increases when obstacles are present
Solution Approach 1:
The operating speed of the device is dynamically adjusted based on real-time sensor input and determined operating state. When sensors detect clear paths, the device operates at higher speeds to maximize productivity. When obstacles are detected (elevations, depressions, inclines, declines), the system automatically transitions to appropriate speed states (caution, slow, or stop), thereby maintaining safety while optimizing overall productivity through adaptive speed control.
Solution Approach 2:
The system implements continuous feedback loops where sensor data from forward, downward, and tilt sensors is constantly monitored and fed back to the control logic. This feedback mechanism enables real-time speed adjustments in response to detected obstacles, ensuring that productivity gains do not compromise safety. The feedback-driven control systematically balances speed and safety based on environmental conditions.
4Reliability
If the device frequently changes speed to respond to obstacles, then the safety is improved, but the navigation smoothness deteriorates due to erratic speed changes
Solution Approach 1:
The system employs dynamic speed transitions that are conditioned on sustained sensor detections rather than transient signals. When obstacles are detected, the system transitions through defined speed states (normal → caution → slow/stop) based on the persistence and confidence of sensor data. This dynamic approach maintains safety by responding to genuine obstacles while preserving smoothness by avoiding erratic transitions from brief or ambiguous sensor readings.
Data Source
AI summary
An autonomous mobile device (AMD) may perform tasks within a physical space. The AMD may move over ramps, bumps, or navigate around obstacles. The AMD may have an inertial measurement unit (IMU) and distance sensors. The IMU provides tilt information indicative of the AMD being on a flat surface or a ramp. The distance sensors provide information on distances between the AMD and surrounding obstacles. Using IMU measurements, the AMD determines a first speed limit that is safe given the tilt of the AMD. Using the distance sensors, the AMD determines a second speed limit that is safe given a distance to an obstacle. The AMD determines a maximum speed based on the first and second speed limits. Based on the maximum speed, the AMD determines whether to adjust a current speed and by how much.