Duckietown: An Innovative Way to Teach Autonomy

Teaching robotics is challenging because it is a multidisciplinary, rapidly evolving and experimental discipline that integrates cutting-edge hardware and software. This paper describes the course design and first implementation of Duckietown, a vehicle autonomy class that experiments with teaching innovations in addition to leveraging modern educational theory for improving student learning. We provide a robot to every student, thanks to a minimalist platform design, to maximize active learning; and introduce a role-play aspect to increase team spirit, by modeling the entire class as a fictional start-up (Duckietown Engineering Co.). The course formulation leverages backward design by formalizing intended learning outcomes (ILOs) enabling students to appreciate the challenges of: (a) heterogeneous disciplines converging in the design of a minimal self-driving car, (b) integrating subsystems to create complex system behaviors, and (c) allocating constrained computational resources. Students learn how to assemble, program, test and operate a self-driving car (Duckiebot) in a model urban environment (Duckietown), as well as how to implement and document new features in the system. Traditional course assessment tools are complemented by a full scale demonstration to the general public. The “duckie” theme was chosen to give a gender-neutral, friendly identity to the robots so as to improve student involvement and outreach possibilities. All of the teaching materials and code is released online in the hope that other institutions will adopt the platform and continue to evolve and improve it, so to keep pace with the fast evolution of the field.National Science Foundation (U.S.) (Award IIS #1318392)National Science Foundation (U.S.) (Award #1405259

PATH PLANNING OF TRACTOR-TRAILER ROBOT BY FAST MARCHING METHODE (FMM)

This paper deals with motion planning of Tractor-trailer robots, which are car-like robot dragging several trailers with no driving force. Each trailer has a nonholonomic kinematic constraint which increases the complexity of the path planning problem. We solved this problem by implementing the Equivalent Size concept, which depending on the size, number, and link-point positions of trailers, transforms a tractor-trailer path planning problem into a single car-like robot path planning problem. In this paper a new path planning algorithm is proposed for car-like robots which utilizes the Fast Marching Method (FMM), which is a numerical method for solving the Eikonal differential equation, and the concept of Virtual Obstacles. The algorithm is fast, works independent of the shape of obstacles, and is easy to implement. To evaluate the quality of the solutions the algorithm is compared with the grid search and nonholonomic RRT algorithms. The results showed that the new method has by far lower runtime compared to the other algorithms, while producing short and smooth paths

Multi-Objective PSO- and NPSO-based Algorithms for Robot Path Planning

In this paper two novel Particle Swarm Optimization (PSO)-based algorithms are presented for robot path planning with respect to two objectives, the shortest and smoothest path criteria. The first algorithm is a hybrid of the PSO and the Probabilistic Roadmap (PRM) methods, in which the PSO serves as the global planner whereas the PRM performs the local planning task. The second algorithm is a combination of the New or Negative PSO (NPSO) and the PRM methods. Contrary to the basic PSO in which the best position of all particles up to the current iteration is used as a guide, the NPSO determines the most promising direction based on the negative of the worst particle position. The two objective functions are incorporated in the PSO equations, and the PSO and PRM are combined by adding good PSO particles as auxiliary nodes to the random nodes generated by the PRM. Both the PSO+PRM and NPSO+PRM algorithms are compared with the pure PRM method in path length and runtime. The results showed that the NPSO has a slight advantage over the PSO, and the generated paths are shorter and smoother than those of the PRM and are calculated in less time

An Adaptive Sparse Algorithm for Synthesizing Note Specific Atoms by Spectrum Analysis, Applied to Music Signal Separation

In this paper, a sparse method is proposed to synthesize the note-specific atoms for musical notes of different instruments, and is applied to separate the sounds of two instruments coexisting in a monaural mixture. The main idea is to explore the inherent time structures of the musical notes by a novel adaptive method. These structures are used to synthesize some time-domain functions called note-specific atoms. The note-specific atoms of different instruments are integrated in a global dictionary. In this dictionary, there is only one note-specific atom for each note of any instrument, resulting in a sparse space for each instrument. The signal separation is done by mapping the mixture signal to the global dictionary. The signal related to each instrument is estimated by a summation of the mapped note-specific atoms tagged for that instrument. Experimental results demonstrate that the proposed method improves the quality of signal separation compared to a recently proposed method

Acceleration of path planning computation based on evolutionary artificial potential field for non-static environments

In this work, a mobile robot path-planning algorithm based on the evolutionary artificial potential field (EAPF) for non-static environments is presented. With the aim to accelerate the path planning computation, the EAPF algorithm is implemented employing novel parallel computing architectures. The EAPF algorithm is capable of deriving optimal potential field functions using evolutionary computation to generate accurate and efficient paths to drive a mobile robot from the start point to the goal point without colliding with obstacles in static and non-static environments. The algorithm allows parallel implementation to accelerate the computation to obtain better results in a reasonable runtime. Comparative performance analysis in terms of path length and computation time is provided. The experiments were specifically designed to show the effectiveness and the efficiency of the mobile robot path-planning algorithm based on the EAPF in a sequential implementation on CPU, a parallel implementation on CPU, and a parallel implementation on GPU