# Framework for Fast Experimental Testing of Autonomous Navigation Algorithms
## 2 Related Work  
Due to the complexity of autonomous navigation systems, each work presenting a complete application has--in one way or another--implemented its own system architecture and navigation framework. Early examples of papers describing a complete system able to navigate outdoors in large environments are found in the eighties, like in [17; 18] where Carnegie Mellon researchers developed a system to navigate autonomously through a network of sidewalks and intersections in the CMU campus. More recently, the two editions of the DARPA grand challenge [19] and the DARPA urban challenge [20] boosted the development of autonomous cars, producing a great number of contributions to the field and several papers describing the architectures and designs developed by the participant teams [11; 21; 22]. Observing these works, one can find that the different architectures share some features--like parallel processes, communications, tasks, etc.--which could be reusable between them, and that was one of the major reasons that motivated the creation of ROS [23]. The ROS navigation stack is the most widely spread and well-known framework to develop autonomous navigation applications. The navigation stack provides a great number of useful tools but has some limitations [24]: it is designed to operate only with differential drive and holonomic robots, it assumes that the robot can be controlled by means of a twist message indicating x, y and theta velocities, it needs a planar laser for localization and mapping and it performs best with nearly square or circular robots. There exist some workarounds like plug-ins to use it with car-like robots [25] and data conversions that can help to incorporate other sensors like the well-known Kinect depth camera [24], however the tightly coupled architecture of the navigation stack results rigid in operation [26]. In contrast, we adopt--as it will be later explained with detail--a clear architecture based on abstraction levels that makes easy to keep the system modular and scalable, so any module can be replaced without affecting the rest of the system. Looking at other recent literature, one can find some complete applications like [27; 28; 29], but these works aim to solve specific problems and are not designed for general purpose. Some efforts in producing more general frameworks for different levels of autonomous system development can also be found, ranging from very high-level project management and agile software development like in [30] to intermediate and low levels, like local trajectory planning and obstacle avoidance in car-like robots [31; 32; 33]. However, frameworks aiming to produce a fully operative \"template system\" to develop and test autonomous navigation algorithms are less common. In [12] a framework for fast designing and prototyping of autonomous multi-robot systems for unmanned aerial vehicles (UAV) is presented. This work shares several aspects with our approach, but it is focused exclusively on aerial multi-agent systems. A whole system architecture for autonomous surface vehicles (ASV) can be found in [34], while in [35] a generic framework is presented as an alternative to the navigation stack, but it focuses in planning and control of wheeled robots with different kinematic constraints rather than covering the whole navigation problem.