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Updated: Jan 11, 2026

Robotic Mirror Therapy System for Functional Recovery of Hemiplegic Arms

Robotic Mirror Therapy System for Functional Recovery of Hemiplegic Arms

Published on: August 15, 2016

EPSO-based rigid robotic arm for obstacle avoidance object grasping.

Tong Xu¹, Xiaomin Han², Honghui Liu²

¹School of Information Technology, Jiangsu Open University, Nanjing, 210000, China. xutong0901@126.com.

Scientific Reports

|November 11, 2025

Summary

This summary is machine-generated.

This study introduces a new robotic grasping framework for fruit picking, improving object recognition and obstacle avoidance. The EPSO-TPPP-YOLOv8 system ensures safe and efficient fruit harvesting with minimal iterations.

Keywords:

Object avoidance Object grasping Path planning Rigid robotic arm Safety distance analysis

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Area of Science:

Robotics
Computer Vision
Artificial Intelligence

Background:

Robotic fruit picking faces challenges in object recognition accuracy, performance efficiency, and obstacle collision avoidance.
Existing systems often struggle with complex orchard environments and precise grasping maneuvers.

Purpose of the Study:

To propose and evaluate a novel object grasping framework, EPSO-TPPP-YOLOv8, for robotic fruit picking.
To enhance obstacle avoidance and path planning for robotic arms in smart orchard applications.

Main Methods:

Developed a safe distance analysis model for robotic arms and obstacles (cylinders and spheres).
Implemented an enhanced Particle Swarm Optimization (EPSO) algorithm for robot arm path planning.
Constructed a unique dataset for vision-based object recognition using YOLOv8.

Main Results:

The EPSO algorithm successfully identified safe paths within 50 iterations without human intervention.
With human guidance, the EPSO-TPPP framework achieved safe path planning within 10 iterations.
Simulation and field experiments validated the robot arm's obstacle-avoidance grasping functionality.

Conclusions:

The EPSO-TPPP-YOLOv8 framework offers a reliable and efficient solution for robotic fruit picking in smart orchards.
The proposed methods significantly improve obstacle avoidance and path planning, crucial for autonomous harvesting.