Autonomous Cart Navigation With Dense Vision and Adaptive Maps
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Solution Overview
Problem
Conventional automated guided vehicles (AGVs) face limitations in navigation, particularly with LIDAR systems, which struggle to accurately detect obstacles, identify changes in environments, and operate effectively in uncontrolled or dynamic settings, leading to inefficiencies and safety issues.
Innovation Solution
The development of autonomous carts equipped with cameras, complex algorithms, and persistent maps that adapt to environmental changes, allowing them to navigate autonomously in various conditions, including the use of load cells for mass-triggered departures and haptic control, enabling efficient operation in dynamic environments.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If LIDAR is used for navigation, then positioning accuracy is improved, but the system fails to detect obstacles in uncontrolled or dynamic environments
Solution Approach 1:
The autonomous cart employs multiple sensor types (cameras, LIDAR, ultrasonic sensors, infrared sensors) that work together to provide both precise positioning and environmental adaptability. The camera system with computer vision algorithms handles dynamic obstacle detection while LIDAR provides accurate positioning, creating a universal navigation system that functions in both controlled and uncontrolled environments.
Solution Approach 2:
Computer vision algorithms act as an intermediary between the camera sensors and the navigation system, processing visual information to identify obstacles and navigate dynamic environments. This intermediary layer enables the system to interpret complex environmental data and make navigation decisions in uncontrolled settings.
2Ease of operation
If conventional AGV control interfaces are used, then manual control is possible, but operation efficiency decreases due to complex control procedures
Solution Approach 1:
The autonomous cart performs self-navigation and self-control through onboard computer vision algorithms and processing systems. The cart automatically detects obstacles, plans paths, and executes movements without requiring continuous human intervention, thereby maintaining ease of operation while significantly improving productivity.
Solution Approach 2:
The patent replaces traditional mechanical control interfaces (joysticks, buttons) with an autonomous control system based on computer vision and algorithms. The cart substitutes human-operated mechanical controls with automated digital processing and actuation systems, eliminating the trade-off between ease of operation and productivity.
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
The autonomous carts can operate effectively in complex environments, adapt to changes, and provide efficient navigation and control, enhancing operational efficiency and safety by automatically updating maps and planning optimal routes, and allowing for intuitive human interaction.
Implementation Method 1
A load cell can be mechanically coupled to the chassis. The load cell can sense force exerted on the chassis and can generate a signal representing a magnitude and direction of the force.
Implementation Method 2
A motor can be mechanically coupled to at least one wheel in the plurality of wheels. The motor can rotate the at least one wheel and turn the at least one wheel to slow and/or stop the autonomous cart.
Data Source
AI summary
An autonomous cart moves products and materials in an industrial environment. It is different from conventional carts because it can navigate autonomously indoors or outdoors in dynamic environments where things change frequently. This autonomous cart uses state-of-the-art “dense” visual perception giving it unequalled and continuous awareness of its surroundings. With this it can operate at a cost, speed, level of safety and efficiency that has never been possible before. This robotic cart makes factories and warehouses more efficient and safer. It enables the movement of smaller batches of material more frequently, reduces the need for expensive conveyor systems, and helps eliminate dangerous and polluting fork trucks from indoor environments.