Robotic simulators play a crucial role in the development and testing of autonomous systems, particularly in the realm of Uncrewed Aerial Vehicles (UAV). However, existing simulators often lack high-level autonomy, hindering their immediate applicability to complex tasks such as autonomous navigation in unknown environments. This limitation stems from the challenge of integrating realistic physics, photorealistic rendering, and diverse sensor modalities into a single simulation environment. At the same time, the existing photorealistic UAV simulators use mostly hand-crafted environments with limited environment sizes, which prevents the testing of long-range missions. This restricts the usage of existing simulators to only low-level tasks such as control and collision avoidance. To this end, we propose the novel FlightForge UAV opensource simulator. FlightForge offers advanced rendering capabilities, diverse control modalities, and, foremost, procedural generation of environments. Moreover, the simulator is already integrated with a fully autonomous UAV system capable of long-range flights in cluttered unknown environments. The key innovation lies in novel procedural environment generation and seamless integration of high-level autonomy into the simulation environment. Experimental results demonstrate superior sensor rendering capability compared to existing simulators, and also the ability of autonomous navigation in almost infinite environments.

Increasing the adoption of cycling is crucial for achieving more sustainable urban mobility. Navigating larger cities on a bike is, however, often challenging due to the cities' fragmented cycling infrastructure and/or complex terrain topology. Cyclists would thus benefit from intelligent route planning that would help them discover routes that best suit their transport needs and preferences. Because of the many factors cyclists consider in deciding their routes, employing a multicriteria route search is vital for properly accounting for cyclists' route-choice criteria. A direct application of optimal multicriteria route search algorithms is, however, not feasible due to their prohibitive computational complexity. In this paper, we formalize a multicriteria bicycle routing problem and propose several heuristics for speeding up the multicriteria route search. We evaluate our method on a real-world cycleway network and show that speedups of up to four orders of magnitude over the standard multicriteria label-setting algorithm are possible with a reasonable loss of solution quality. Our results make it possible to practically deploy bicycle route planners capable of producing diverse high-quality route suggestions respecting multiple real-world route-choice criteria.

Increasing the adoption of cycling is crucial for achieving more sustainable urban mobility. Navigating larger cities on a bike is, however, often challenging due to cities’ fragmented cycling infrastructure and/or complex terrain topology. Cyclists would thus benefit from intelligent route planning that would help them discover routes that best suit their transport needs and preferences. Because of the many factors cyclists consider in deciding their routes, employing multi-criteria route search is vital for properly accounting for cyclists’ route-choice criteria. Direct application of optimal multi-criteria route search algorithms is, however, not feasible due to their prohibitive computational complexity. In this paper, we therefore propose several heuristics for speeding up multi-criteria route search. We evaluate our method on a real-world cycleway network and show that speedups of up to four orders of magnitude over the standard multi-criteria label-setting algorithm are possible with a reasonable loss of solution quality. Our results make it possible to practically deploy bicycle route planners capable of producing high-quality route suggestions respecting multiple real-world route-choice criteria.

Bicycle route planning is a challenging problem because of the diverse set of factors considered by cyclists in choosing their cycling routes. We provide a solution to this problem based on a formal model expressive enough to represent transport network features and cyclists' preferences grounded in the studies of real-world bicycle route choice behaviour. Our solution employs the A* algorithm together with vectors of cost and heuristic functions – able to optimise routes for travel time, comfort, quietness, and flatness. We have implemented, practically deployed and experimentally evaluated our solution in the challenging setting of the city of Prague. The experiments confirmed that the planner is able to return high-quality plans in less than 250 milliseconds per query.

We present an advanced journey planner designed to help travellers to take full advantage of the increasingly rich, and consequently more complex offering of mobility services available in modern cities. In contrast to existing systems, our journey planner is capable of planning with the full spectrum of mobility services; combining individual and collective, fixed-schedule as well as on-demand modes of transport, while taking into account individual user preferences and the availability of transport services. Furthermore, the planner is able to personalize journey planning for each individual user by employing a recommendation engine that builds a contextual model of the user from the observation of user's past travel choices. The planner has been deployed in four large European cities and positively evaluated by hundreds of users in field trials.

We present a web-based tool for a fine-grained analysis of the quality of public transport coverage. Employing an efficient graph-based transport network representation and a fast, modified Dijkstra-based journey planning algorithm, the tool calculates four public transport accessibility indices: journey duration, service frequency, the number of transfers, and a combined, overall index. Together, the indices give an accurate picture of the user-perceived accessibility by public transport in the area and time of interest.