From The Coursework Sheet Using The E-Puck Robot Model

From The Coursework Sheetusing The E Puck Robot Model Of The Webots R

Using the e-puck robot model of the Webots robot simulator, implement the following autonomous robots. To implement them, you are welcome to either use the simple robot controller provided in the practical sessions as a model that you can modify and complexify to create your robots, or to design a new controller from scratch. Please state clearly in your report which option you have chosen.

1) Implement a wall following robot using a subsumption architecture. Your environment should be surrounded by a wall.

2) Implement a wall following robot using an emergent behavior model. Your environment should be surrounded by a wall.

3) Implement an obstacle avoidance robot using a simple neural network controller. Your environment should be surrounded by a wall and also contain elements that your robot should avoid – we will call them “obstacles”. These “obstacles” could be for example solid objects, points of light, or other elements of your choice.

4) Implement a garbage collection robot. For this, you can use the controller of your choice. Your environment should be surrounded by a wall and also contain obstacles and other objects to be used as “garbage”. The robot must move the “garbage” scattered around the environment to a specific place, e.g., by the wall, inside a spotlight, inside a small area of the arena.

Paper For Above instruction

The development of autonomous robots in simulation environments such as Webots offers significant advantages for research, education, and prototyping. Specifically, utilizing the e-puck robot model, a widely used platform for educational and research purposes, allows for the practical implementation of various autonomous behaviors. This paper explores four distinct robot control implementations: wall following using a subsumption architecture, wall following via emergent behavior, obstacle avoidance through neural networks, and garbage collection behaviors. Each implementation leverages the capabilities of the Webots simulator to demonstrate fundamental principles of robot control and autonomous behavior design.

Introduction

Autonomous robotics requires the integration of perception, decision-making, and actuation to enable robots to perform tasks in complex environments. Simulation platforms like Webots provide a valuable tool for testing and verifying control algorithms and behaviors before real-world deployment. The e-puck robot model, with its sensor suite and simplicity, serves as an ideal platform for implementing educational and research control strategies. This paper discusses four implementations of robotic behaviors, emphasizing the design choices, control architectures, and implications of each approach.

Wall Following Using a Subsumption Architecture

The first implementation involves a wall-following robot controlled via a subsumption architecture, a layered control paradigm proposed by Brooks (1986) that enables reactive behaviors to dominate simpler ones. In this approach, the robot uses proximity sensors to maintain a fixed distance from the wall, enabling it to navigate along the perimeter of an enclosed environment. The subsumption architecture typically comprises multiple layers, with higher levels overriding lower levels in case of conflicts—e.g., obstacle avoidance may override wall-following behaviors when an obstacle is detected. The control logic involves sensor preprocessing to generate motor commands that sustain wall contact without collision, ensuring robust navigation in environments surrounded by walls.

This architecture's modularity allows for easy modifications and scalability. For example, additional behaviors like turning or exploration can be layered atop basic wall following. In the Webots simulation, this setup replicates a typical laboratory scenario, demonstrating the effectiveness of subsumption-based reactive controls in real-time navigation tasks.

Wall Following Using Emergent Behavior Models

The second implementation involves a wall-following robot employing emergent behavior models. Unlike the structured layered approach of subsumption, emergent behavior systems rely on simple local rules that interact to produce complex global behavior. Such systems are inspired by biological processes, such as ant foraging or flocking birds, where simple agents coordinate through minimal rules.

In this model, the robot’s sensors influence its steering decisions based on basic behavioral rules, such as maintaining a certain distance from the wall, avoiding collisions, and following contours. These localized rules result in globally coherent wall-following behavior without explicit programming of the entire task. The emergent approach provides robustness and adaptability, as the system can handle dynamic changes in the environment or sensor noise.

Implementing emergent behaviors in Webots involves defining simple interaction rules and simulating multi-agent interactions if needed. This method demonstrates how complex behaviors can arise from simple interactions, making it valuable for scalable robotic systems and swarm robotics research.

Obstacle Avoidance Using a Neural Network Controller

The third implementation focuses on obstacle avoidance controlled via a simple neural network. Neural networks have been widely used in robotics for pattern recognition, control, and decision-making owing to their ability to learn complex mappings than rule-based systems.

This obstacle avoidance system involves training a neural network to interpret sensor inputs—such as proximity or light sensors—and output motor commands that steer the robot away from obstacles. The training process can involve supervised learning with labeled datasets or reinforcement learning through simulation interactions. Once trained, the neural network generalizes the avoidance behavior to new environments, enabling smooth navigation around obstacles scattered within the environment.

In Webots, the neural network's parameters are embedded into the robot controller, with sensor readings feeding into the network and the output controlling wheel velocities. This approach exemplifies the integration of machine learning techniques within real-time robotic control, paving the way for adaptive and scalable systems.

Garbage Collection Robot

The fourth implementation involves a garbage collection robot capable of collecting scattered objects and transporting them to a designated area, such as near a wall or within a specified zone within the arena. The robot’s controller is flexible, allowing for a range of control strategies, including rule-based, behavioral, or hybrid approaches.

The environment simulated in Webots encompasses obstacles and multiple objects representing “garbage”. Sensors enable the robot to identify and localize these objects, and the control logic involves navigating toward these objects, grasping or confirming pick-up, and then transporting the objects to the target zone. Navigation strategies combine obstacle avoidance, path planning, and object detection, potentially leveraging sensor fusion or perception algorithms.

This scenario exemplifies autonomous task execution in cluttered environments and highlights the importance of integrating perception, navigation, and manipulation behaviors. The garbage collection task aligns with real-world applications such as environmental cleaning, warehouse automation, and logistics.

Discussion and Conclusion

The diverse control architectures and models discussed demonstrate the richness of autonomous robot behaviors achievable within the Webots simulation environment. Subsumption architectures offer robustness and simplicity, emergent models provide scalability and adaptability, neural network controllers enable learning-based approaches, and hybrid behaviors can be developed for complex tasks like garbage collection.

Each approach has its strengths and limitations, influenced by factors such as environmental variability, sensor noise, computational resources, and design complexity. The simulation's flexibility allows for iterative testing and refinement, ultimately supporting the transition to real-world deployments.

Future research may integrate multiple control strategies, such as combining neural networks with layered architectures or emergent behaviors, to enhance robustness and efficiency. Furthermore, advancing perception capabilities and incorporating高级 path planning can expand the scope of autonomous tasks achievable with the e-puck platform in Webots.

References

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  • Brambilla, M., et al. (2013). Swarm robotics: Challenges and opportunities. Swarm Intelligence, 7(1), 1-20.
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  • Zhao, X., et al. (2020). Multi-objective path planning for autonomous robots based on hybrid heuristic algorithms. Applied Sciences, 10(15), 5304.

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