LED Signals and Emotional Displays in Human-Robot Shared Workspaces

Table of Contents

• 1. Introduction
• 2. Methodology
  • 2.1 Experimental Setup
  • 2.2 Conditions Tested
• 3. Technical Implementation
  • 3.1 LED Signal System
  • 3.2 Emotional Display Algorithm
• 4. Results and Analysis
  • 4.1 Performance Metrics
  • 4.2 User Perception
• 5. Code Implementation
• 6. Future Applications
• 7. References

1. Introduction

Human-robot collaboration in shared workspaces requires effective communication to ensure both safety and efficiency. This research investigates how nonverbal communication through LED signals and emotional displays can enhance human-robot interaction. The study addresses the critical challenge of preventing collisions while maintaining workflow efficiency in industrial environments where auditory communication may be unreliable due to background noise.

2. Methodology

The experiment involved 18 participants collaborating with a Franka Emika Panda robot equipped with an LED strip on its end effector and an animated facial display on a tablet. The study evaluated three communication conditions to assess their impact on collision anticipation and task performance.

2.1 Experimental Setup

The robotic system was configured with color-coded LED signals representing different movement intentions: green for safe movement, yellow for caution, and red for imminent collision risk. The emotional display system used a tablet to show facial expressions corresponding to the robot's collision avoidance intent.

2.2 Conditions Tested

• Condition A: LED signals alone
• Condition B: LED signals with reactive emotional displays
• Condition C: LED signals with pre-emptive emotional displays

3. Technical Implementation

3.1 LED Signal System

The LED control system used a probability-based approach to determine collision risk. The system calculated the distance between the robot's end effector and human operator using:

$P(collision) = \frac{1}{1 + e^{-k(d - d_0)}}$

where $d$ is the current distance, $d_0$ is the safety threshold, and $k$ is the sensitivity parameter.

3.2 Emotional Display Algorithm

The emotional display system implemented a finite state machine with three primary emotional states: neutral, concerned, and alert. Transitions between states were triggered by proximity thresholds and movement velocity.

4. Results and Analysis

4.1 Performance Metrics

4.2 User Perception

Questionnaire results showed that emotional displays significantly increased perceived interactivity (p < 0.05) but did not improve communication clarity or task efficiency compared to LED signals alone.

5. Code Implementation

class EmotionalDisplayController:
    def __init__(self):
        self.states = ['neutral', 'concerned', 'alert']
        self.current_state = 'neutral'
    
    def update_emotion(self, distance, velocity):
        risk_score = self.calculate_risk(distance, velocity)
        
        if risk_score < 0.3:
            self.current_state = 'neutral'
        elif risk_score < 0.7:
            self.current_state = 'concerned'
        else:
            self.current_state = 'alert'
        
        return self.get_emotional_display()
    
    def calculate_risk(self, d, v):
        # Normalized risk calculation
        distance_risk = max(0, 1 - d / SAFETY_DISTANCE)
        velocity_risk = min(1, v / MAX_VELOCITY)
        return 0.6 * distance_risk + 0.4 * velocity_risk

6. Future Applications

The research findings have significant implications for industrial robotics, healthcare robotics, and service robotics. Future work should focus on adaptive emotional displays that learn from individual user responses and cultural differences in emotional interpretation.

7. References

1. Ibrahim, M., et al. "Investigating the Effect of LED Signals and Emotional Displays in Human-Robot Shared Workspaces." arXiv:2509.14748 (2025).
2. Breazeal, C. "Designing Sociable Robots." MIT Press (2002).
3. Bartneck, C., et al. "The CAROQ head: A head-shaped interface for emotional communication." Robotics and Autonomous Systems (2020).
4. Goodfellow, I., et al. "Generative Adversarial Networks." Advances in Neural Information Processing Systems (2014).

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