A multi-objective path optimization method for plant protection robots based on improved A*-IWOA.

Area of Science:

• Robotics
• Artificial Intelligence
• Agricultural Engineering

Background:

• Plant protection robots are increasingly adopted, integrating intelligent technology with agricultural machinery.
• Limited battery capacity and weight constraints restrict robot autonomy and continuous operation duration.
• Efficient path planning is crucial for robots operating in challenging terrains like mountainous regions.

Purpose of the Study:

• To develop a multi-objective path optimization approach for plant protection robots in mountainous environments.
• To address challenges of low energy consumption and enhance path planning efficiency.
• To enable robots to perform tasks continuously over extended periods despite energy limitations.

Main Methods:

• Developed an energy consumption model for robots on slopes using kinematic and dynamic models.
• Implemented an improved A* algorithm with 8-domain diagonal search and a novel cost function.
• Utilized the Improved Whale Optimization Algorithm (A*-IWOA) for optimizing path curvature and energy consumption.
• Employed a 2.5D elevation grid map for path planning in simulation and orchard scenarios.

Main Results:

• The proposed A*-IWOA algorithm significantly reduced travel distance and improved path planning efficiency.
• The approach met driving accuracy and energy consumption requirements for robots in mountainous terrains.
• Demonstrated substantial improvements in computational accuracy, convergence speed, and overall efficiency.

Conclusions:

• The optimized path planning algorithm enhances robot efficiency and accuracy in complex mountain terrains.
• The method satisfies stringent energy consumption and path planning needs for autonomous operations.
• This approach has potential applications in other robotic fields, including picking and industrial inspection.