Mobile Robot Sensor Fusion for Smooth Mapping and Obstacle Avoidance
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Solution Overview
Problem
Existing mobile robot cleaners face challenges in creating accurate maps with minimal speed changes and avoiding obstacles while mapping, often resulting in inefficient movement and potential collisions.
Innovation Solution
The mobile robot is equipped with a lidar sensor, camera sensor, and controller that fuse image and detection signals to select a front edge for movement, avoiding obstacles by determining the center point of obstacle-free spaces and adjusting movement paths to prevent collisions.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Device complexity
If the robot cleaner travels to draw a map using simple obstacle avoidance logic, then the device complexity is reduced, but the manufacturing precision of the map and the reliability of obstacle avoidance deteriorate
Solution Approach 1:
The robot dynamically adjusts its sensing radius based on the detected environment. When an obstacle is detected within the current sensing radius, the robot reduces the sensing radius to focus on local obstacle avoidance. When no obstacle is detected, the robot expands the sensing radius to efficiently map the environment. This dynamic adjustment allows the system to achieve both accurate mapping and reliable obstacle avoidance without requiring overly complex control logic.
Solution Approach 2:
The system changes the sensing parameter (sensing radius) based on environmental conditions. By varying the sensing radius between a first radius (for efficient mapping) and a second radius (for obstacle avoidance), the system adapts its behavior to different situations, achieving both accurate map creation and reliable obstacle detection without maintaining permanently complex control structures.
2Reliability
If the robot cleaner uses a large sensing radius to detect obstacles early, then the reliability of obstacle avoidance is improved, but the productivity of mapping deteriorates due to frequent direction changes
Solution Approach 1:
The sensing radius is dynamically adjusted based on whether obstacles are detected. During efficient mapping traversal, the robot uses a larger sensing radius to detect obstacles early. When an obstacle is detected, the robot switches to a smaller sensing radius for focused obstacle avoidance. This dynamic switching allows the system to maintain both high mapping productivity and reliable obstacle avoidance without constant direction changes.
Solution Approach 2:
The robot uses a larger sensing radius than strictly necessary for immediate obstacle avoidance, allowing early detection of potential obstacles. This excessive sensing action enables the robot to plan its path more efficiently by detecting obstacles at a distance, reducing the frequency of sudden direction changes while still maintaining reliable obstacle avoidance capability.
3Reliability
If the robot cleaner frequently changes direction to avoid obstacles, then the reliability of obstacle avoidance is improved, but the manufacturing precision of the map deteriorates due to speed changes
Solution Approach 1:
The robot dynamically adjusts its operational mode based on obstacle detection. When no obstacle is detected, the robot maintains steady traversal for accurate mapping. When an obstacle is detected within the sensing radius, the robot switches to obstacle avoidance mode, making directional changes only when necessary. This dynamic mode switching minimizes unnecessary speed changes while maintaining reliable obstacle avoidance, thereby preserving map accuracy.
Solution Approach 2:
The robot uses a proactive obstacle detection approach by maintaining a sensing radius that extends beyond immediate proximity. This allows the robot to detect and plan around obstacles in advance, skipping over potential collision scenarios by making smooth, pre-planned directional adjustments rather than reactive, abrupt changes that would compromise mapping precision.
4Device complexity
If the robot cleaner uses a fixed sensing radius, then the device complexity is reduced, but the adaptability to different environments deteriorates
Solution Approach 1:
The sensing radius is made dynamic rather than fixed, automatically adjusting based on environmental conditions. When obstacles are detected, the sensing radius reduces to focus on local navigation. When the environment is clear, the sensing radius expands to efficiently map larger areas. This dynamic adjustment provides environmental adaptability without requiring complex manual configuration or multiple fixed-radius modes.
Solution Approach 2:
The robot autonomously adjusts its own sensing radius based on environmental feedback from obstacle detection sensors. The system self-regulates its sensing parameters without external intervention, adapting to different environments automatically. This self-service capability provides environmental versatility while maintaining simple control logic, as the adjustment mechanism is triggered automatically by sensor inputs.
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
This solution enables the robot to create accurate maps with minimal speed changes and avoid obstacles effectively, ensuring efficient navigation and preventing collisions by utilizing lidar and camera sensors to detect and adapt to its environment.
Implementation Method 1
a lidar sensor configured to acquire terrain information outside the main body
Implementation Method 2
a camera sensor configured to acquire an image outside the main body
Data Source
AI summary
A mobile robot of the present disclosure includes: a traveling unit configured to move a main body; a lidar sensor configured to acquire terrain information outside the main body; a camera sensor configured to acquire an image outside the main body; and a controller configured to fuse the image and a detection signal of the lidar sensor to select a front edge for the next movement and set a target location of the next movement at the front edge to perform mapping travelling. Therefore, in a situation where there is no map, the mobile robot can provide an accurate map with a minimum speed change when travelling while drawing the map.