Within this report is described the process of designing a new Zebro robot for the TU Delft Robotics Institute. Zebro is a six-legged robot specifically intended to become one of the first truly autonomous swarming robots on earth. The specific aim of this project was to design Zebro to become ready for series production, and to become a robotic platform that allows future students and companies to build upon.

This report covers an analytical phase (Chapter 2), aimed at examining the roots of Zebro and giving theoretical background regarding swarm robotics. Furthermore, a detailed oversight is given of the approach used to redesign the robot (Chapter 3). Several prominent design methods have been used within this project, including the modular approach, concurrent design approach and design for assembly. The motivation for choosing these methods, and the implications of using these methods is described within the analysis phase.


Two chapters are dedicated to the process of designing DeciZebro. These chapters show the reasoning behind design choices (Chapter 4) and ends by presenting the final result (Chapter 5). The result is a modular robotic platform called DeciZebro which can be mass produced. A series of 10 robots was produced within the context of this project as proof of concept for its manufacturability.

The final chapter (Chapter 6) of this report takes the design out into the world and asks the question; how will this robot be implemented in society? Because no autonomous swarms exist yet, this question can only be answered once the Zebro platform is working. However, within this chapter suggestions are given.

In the appendices a technical data package can be found, detailing the different parts featured in Zebro. Furthermore, a selection of important design decisions is shown within the Design Tree appendix.

Nature has found many interesting solutions to similar problems that we face with robots. Swarming in robotics is an interesting field to reduce cost and risk, and provide a scalable solution to certain problems. The Zebro Project of the TU Delft performs research in the field of swarm robotics using the six legged robot, called a Zebro. Swarm robotics consists of using a large amount of small, independent robots that respond to one another in order to create a distributed, global behaviour.
The robots have been simulated using the Unity engine, to investigate methods to accomplish swarming behaviour. The simulated Zebros and sensors are modelled after real life designs. The algorithm is based on bird flocking behaviour to create mobile and cohesive swarming behaviour. The Zebro robots are not ready for testing yet, but the algorithms have been prepared for testing using a Raspberry Pi.

Mobile robots need to be fully autonomous in order to perform their tasks inside their environment. To do that, robots need to have an understanding of their environment, so that they can successfully localize and navigate themselves within it. The understanding of the robots' environment is created by solving the SLAM (Simultaneous Localization And Mapping) problem. The SLAM problem is usually approached using probabilistic laws; both the robot's path and the environment are estimated and represented by probability distributions.

The estimate of the environment is incorporated into a map, which could either be one global map or a network of smaller connected local maps. In small areas where the sensors' errors and inaccuracies remain low, the creation of one global map is preferred. However, as the area grows larger, the global map becomes inaccurate due to accumulated errors. Therefore the strategy of creating a network of local maps is employed. Even though this strategy produces low error results, there is still a limit beyond which it does not scale. Specifically, as the mapping area grows even wider, the number of local maps increases which in turn causes the amount of required memory to increase. In addition, when it comes to loop closure or in other words the procedure of estimating whether the robot returned to a previously visited point in the map, the required computational power increases linearly or sometimes quadratically with respect to the number of local maps.

Consequently, SLAM algorithms suffer from scalability problems, especially when the mapping area becomes too large. Since robots are real-world devices, mapping solutions should be efficient and applicable to the robot's resources, even when these resources are limited.

In this thesis we propose a framework which addresses this scalability issue, aiming to reduce the computational needs and memory usage during the SLAM procedure. Additionally, our framework intends to facilitate loop closure, which can become troublesome in large environments.

Our framework uses multiple sensors and creates a network of hierarchically structured local maps. In order to assess our framework, we compare it to a technique which produces a network of non-hierarchically structured local maps. Our experiments show that in large areas our framework works ideally; the speedup is encouraging and the RAM memory overhead is negligible or conditionally lower.