Pesticide Robot Obstacle Avoidance With Real-Time Path Correction
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Solution Overview
Problem
Existing pesticide application robots face challenges in obstacle avoidance, particularly under complex topographic conditions, with limited sensor capabilities and reliance on pre-stored maps, leading to reduced efficiency and the need for human assistance.
Innovation Solution
An automatic obstacle avoidance method for pesticide application robots using machine vision and path planning algorithms like D* to generate optimal paths, combined with real-time environmental perception to update obstacle information and correct paths, ensuring accurate pesticide application without collisions.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If traditional ultrasonic or infrared sensors are used for obstacle detection, then the system structure is simple, but the obstacle avoidance efficiency and accuracy are low
Solution Approach 1:
The patent combines multiple types of sensors (ultrasonic, infrared, camera) into an integrated sensing system that works together to detect obstacles and generate path planning information, thereby improving obstacle avoidance efficiency while distributing system complexity across multiple specialized components
Solution Approach 2:
The sensor system is designed to perform multiple functions: ultrasonic sensors detect proximity obstacles, infrared sensors detect thermal signatures, and camera systems capture visual information for map building and real-time path planning, allowing a single integrated system to handle diverse obstacle detection tasks
2Reliability
If pre-stored maps are used for path planning, then the initial path can be planned, but the map cannot be updated in real time leading to outdated navigation information
Solution Approach 1:
The system implements feedback mechanisms where sensors continuously detect environmental changes and obstacles, this information is fed back to the path planning module which then updates the robot's navigation path in real time, ensuring the map reflects current conditions
Solution Approach 2:
The path planning system transitions from static pre-stored maps to dynamic real-time map building and updating, allowing the navigation information to adapt continuously to changing environmental conditions and new obstacle discoveries during robot operation
3Adaptability or versatility
If a single obstacle avoidance scheme is used, then the system is simple to implement, but it cannot handle complex topographic conditions effectively
Solution Approach 1:
The obstacle avoidance system is segmented into multiple independent modules: ultrasonic detection module, infrared detection module, camera vision module, and path planning module. Each module handles specific aspects of obstacle detection and avoidance, allowing the system to adapt to different obstacle types while maintaining manageable complexity through modular architecture
Solution Approach 2:
The system employs a composite approach by integrating multiple sensing technologies (ultrasonic, infrared, visual) and multiple path planning algorithms (D*, A*) into a unified obstacle avoidance system, combining the strengths of different methods to handle diverse and complex topographic conditions
4Ease of operation
If manual assistance is required for obstacle avoidance, then the robot can achieve obstacle avoidance, but pesticide application efficiency is reduced
Solution Approach 1:
The robot is equipped with autonomous obstacle avoidance capabilities through integrated sensors and path planning algorithms that enable it to independently detect obstacles, calculate alternative paths, and navigate around obstacles without human intervention, thereby maintaining continuous pesticide application operations and improving overall efficiency
Data Source
AI summary
Provided are an automatic obstacle avoidance method and system of a pesticide application robot and a storage medium. The method includes: acquiring a plan view of a target pesticide application operation area and acquiring path information; acquiring start point information and end point information of a pesticide application robot and environmental information of the target pesticide application operation area, performing preliminary path planning, and acquiring an optimal path; causing the pesticide application robot to arrive at a specified operating point along the optimal path, and performing environmental perception by machine vision, determining obstacle point information and updating the plan view of the target pesticide application operation area with the obstacle point information; and correcting the optimal path with a current location of the pesticide application robot and an area to which a pesticide has been sprayed based on the updated plan view of the target pesticide application operation area.