Duckietown is an open, inexpensive and flexible platform for autonomy education and research. The platform comprises small autonomous vehicles (“Duckiebots”) built from off-the-shelf components, and cities (“Duckietowns”) complete with roads, signage, traffic lights, obstacles, and citizens (duckies) in need of transportation. The Duckietown platform offers a wide range of functionalities at a low cost. Duckiebots sense the world with only one monocular camera and perform all processing onboard with a Raspberry Pi 2, yet are able to: follow lanes while avoiding obstacles, pedestrians (duckies) and other Duckiebots, localize within a global map, navigate a city, and coordinate with other Duckiebots to avoid collisions. Duckietown is a useful tool since educators and researchers can save money and time by not having to develop all of the necessary supporting infrastructure and capabilities. All materials are available as open source, and the hope is that others in the community will adopt the platform for education and research.

This paper investigates motion-planning using formal language specifications for dynamical systems with differential constraints. In particular, we focus on process algebra as a language to specify complex task specifications motivated by autonomous electric vehicles operating in a mobility-on-demand scenario. We use ideas from sampling-based motion-planning algorithms to incrementally construct a finite abstraction of the dynamical system as a Kripke structure. Given a task specification expressed as a process graph, we use model checking techniques to construct a weighted product graph of the specification with the Kripke structure. We then devise an algorithm that provably converges to the optimal trajectory of the dynamical system that satisfies the task specification as the number of the states in the Kripke structure goes to infinity. The algorithm is demonstrated in simulation experiments, viz., charging the electric car at a busy charging station and scheduling pick-ups and drop-offs of passengers.