Unmanned sanitation vehicles (USVs) promise cleaner cities, yet efficiently coordinating multiple USVs in large urban areas remains challenging due to constraints such as limited waste capacity and battery life. In this demo, we present MRTC, a multi-robot task cooperation system. First, Dynamic Task Assignment employs an Actor-Critic policy within a Markov decision framework to allocate cleaning tasks and decide the required number of USVs. Second, Single-USV Path Planning refines each route via a fast two-layer iterative search. Over an eight-month real-world deployment in three urban testbeds, our MRTC system markedly improved cleaning efficiency while lowering operating costs. Operating over a combined 10,775 km of routes per month, the system achieved average monthly savings of 20,575 kWh of energy and 2,744 labour hours. A demonstration video is available at https://llq978.github.io/Demo/.

In recent years, an increasing number of map apps have provided route planning services to their users. However, the quality of route planning services relies heavily on having correct data about transportation infrastructure. As many planned subway lines are being built across cities, there are conflicts between the actual conditions and the data provided by map apps for temporary bus stops, which may result in complaints against public transportation operators. However, it is difficult to tackle these complaints, as public transportation operators can obtain only inaccurate information about the locations of temporary bus stops. To resolve these conflicts, crowd bus sensing (CBS) is proposed in this paper. CBS is a new sensing paradigm that takes advantage of the extensive deployment of GPS trackers and prior knowledge about the transportation infrastructures covered by scheduled bus routes. Extensive experimental evaluations on real-world and synthetic datasets show that the proposed CBS system outperforms state-of-the-art methods.