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.

This paper introduces a control architecture for real-time and onboard control of Unmanned Aerial Vehicles (UAVs) in environments with obstacles using the Model Predictive Path Integral (MPPI) methodology. MPPI allows the use of the full nonlinear model of UAV dynamics and a more general cost function at the cost of a high computational demand. To run the controller in real-time, the sampling-based optimization is performed in parallel on a graphics processing unit onboard the UAV. We propose an approach to the simulation of the nonlinear system which respects low-level constraints, while also able to dynamically handle obstacle avoidance, and prove that our methods are able to run in real-time without the need for external computers. The MPPI controller is compared to MPC and SE(3) controllers on the reference tracking task, showing a comparable performance. We demonstrate the viability of the proposed method in multiple simulation and real-world experiments, tracking a reference at up to 44 km h−1and acceleration close to 20 m s−2, while still being able to avoid obstacles. To the best of our knowledge, this is the first method to demonstrate an MPPI-based approach in real flight.

In multi-goal path planning, the task is to find a sequence to visit a set of target locations in an environment using the shortest path. Finding the order can be achieved by solving an instance of the Traveling Salesman Problem (TSP). This requires to determine paths and their lengths between the individual targets, which is solved using robotic path planning. In this paper, we propose a randomized planner for multi-goal path planning for non-holonomic mobile robots. Multiple trees are constructed simultaneously from the targets and expanded by collision-free configurations until they touch each other or obstacles. The trees are expanded using Dubins maneuvers. Therefore, the resulting trajectories satisfy the kinematic constraints of the mobile robots and they can also be used by Unmanned Aerial Vehicles flying at a constant altitude. The efficiency of the proposed planning approach is demonstrated in the multi-goal path planning for environments with tens of targets and compared to state-of-the-art approaches.

Abstract Computation of trajectories for ligand binding and unbinding via protein tunnels and channels is important for predicting possible protein–ligand interactions. These highly complex processes can be simulated by several software tools, which provide biochemists with valuable information for drug design or protein engineering applications. This paper focuses on aiding this exploration process by introducing the DockVis visual analysis tool. DockVis operates with the multivariate output data from one of the latest available tools for the prediction of ligand transport, CaverDock. DockVis provides the users with several linked views, combining the 2D abstracted depictions of ligands and their surroundings and properties with the 3D view. In this way, we enable the users to perceive the spatial configurations of ligand passing through the protein tunnel. The users are initially visually directed to the most relevant parts of ligand trajectories, which can be then explored in higher detail by the follow‐up analyses. DockVis was designed in tight collaboration with protein engineers developing the CaverDock tool. However, the concept of DockVis can be extended to any other tool predicting ligand pathways by the molecular docking. DockVis will be made available to the wide user community as part of the Caver Analyst 3.0 software package ( www.caver.cz ).

In many cases, protein reactions with other small molecules (ligands) occur in a deeply buried active site. When studying these types of reactions, it is crucial for biochemists to examine trajectories of ligand motion. These trajectories are predicted with in-silico methods that produce large ensembles of possible trajectories. In this paper, we propose a novel approach to the interactive visual exploration and analysis of large sets of ligand trajectories, enabling the domain experts to understand protein function based on the trajectory properties. The proposed solution is composed of multiple linked 2D and 3D views, enabling the interactive exploration and filtering of trajectories in an informed way. In the workflow, we focus on the practical aspects of the interactive visual analysis specific to ligand trajectories. We adapt the small multiples principle to resolve an overly large number of trajectories into smaller chunks that are easier to analyze. We describe how drill-down techniques can be used to create and store selections of the trajectories with desired properties, enabling the comparison of multiple datasets. In appropriately designed 2D and 3D views, biochemists can either observe individual trajectories or choose to aggregate the information into a functional boxplot or density visualization. Our solution is based on a tight collaboration with the domain experts, aiming to address their needs as much as possible. The usefulness of our novel approach is demonstrated by two case studies, conducted by the collaborating protein engineers.

Cooperative carrying of large, cable-suspended payloads by a pair of cooperating unmanned aerial vehicles (UAVs) is tackled in this paper. The proposed system, involving a sampling-based motion planning algorithm and a model predictive control-based coordination of UAVs, aims to achieve a smooth and reliable flight performance in environments with obstacles. The motion planning is designed to satisfy constraints on relative positions of UAVs, which are defined from the cooperative transport task and by onboard mutual localization, which is used for real-time estimation of states of neighboring robots carrying the object. Besides, a guiding principle with a cost-driven expansion is employed to steer the growth of a Rapidly-exploring Random Tree (RRT) to keep the coupled system of UAVs and the object as close to desired mutual positions as possible. A significant deviation of the controlled system from the desired configuration, by increasing or decreasing the relative distance between UAVs carrying the cable-suspended object, is achieved only if it is required by environment constraints (e.g. in narrow passages), while the allowed limits are always satisfied. Using the guiding principle enables us to find feasible solutions of the problem in a reasonable short time using onboard computer even in environments with a complicated structure of obstacles. The proposed system was evaluated in numerous simulations, compared with state-of-the-art solutions using statistical sets of results, and its performance and reliability were verified in experiments in real-world conditions.

Path planning of 3D solid objects has many practical applications. Sampling-based planners like Rapidly-exploring Random Tree (RRT) tackle the problem by the randomized sampling of the configuration space. The well-known issue of the sampling-based planners is the narrow passage problem. Narrow passages are small collision-free regions in the configuration space that are, due to their low volume, difficult to cover by the random samples, which prevents the sampling-based planners from finding a path leading through the passages. To increase the success rate of the planners, the search in the configuration space can be guided using an approximate solution. An approximate solution is found, for example, considering a smaller object (robot). The performance of this planning concept depends on the way of reducing the size of the object and the ability to find an initial solution. In this paper, we propose a novel technique to reduce the geometry of the object by a combination of triangulation and iterative removal of surface triangles. This technique is suitable for both convex and non-convex objects. In many real-world applications, narrow passages can also contain the initial or goal configurations. We thus propose the extension of the goal region to increase the probability of finding the initial approximate solution. Experiments have shown that the proposed modification outperforms the simple guiding using approximate solutions, as well as other related state-of-the-art planners.

In multi-goal path planning, the task is to find a sequence to visit a set of target locations in an environment. The combinatorial part of the problem (finding the sequence) can be solved as an instance of Traveling Salesman Problem, which requires knowledge about collision-free paths (and distances) between the individual targets. Finding the collision-free paths between the targets is essential in this task. Sampling-based planners like Probabilistic Roadmaps (PRM) and Rapidly-exploring Random Tree (RRT) can be used to find these paths. However, PRM can be computationally demanding, as it attempts to connect each node in the roadmap to its neighbors, regardless of their later usage in the solution. Contrary, RRT is a tree-based planner, and one run can only provide paths starting in the root of the tree (a single target). In this paper, we propose a novel planner for multi-goal path planning. Multiple trees (forest) are constructed simultaneously from the targets and expanded by collision-free configurations until they touch each other or obstacles. Each tree, therefore, does not explore the whole configuration space (as in the case of RRT), and its construction is faster than PRM, as it uses lower number of edges. The efficiency of this new planning approach is demonstrated in the multi-goal path planning in 2D environment with tens of targets and with narrow passages.

Sampling-based motion planners are often used to solve motion planning problems for robots with many degrees of freedom. These planners explore the related configuration space by random sampling. The well-known issue of the sampling-based planners is the narrow passage problem. Narrow passages are small collision-free regions in the configuration space that are, due to their volume, difficult to cover by the random samples. The volume of the narrow passages can be artificially increased by reducing the size of the robot, e.g., by scaling-down its geometry, which increases the probability of placing the random samples into the narrow passages. This allows us to find an approximate solution (trajectory) and use it as a guide to find the solution for a larger robot. Guiding along an approximate solution may, however, fail if this solution leads through such parts of the configuration space that are not reachable or traversable by a larger robot. To improve this guiding process, we propose to compute several approximate solutions leading through different parts of the configuration space, and use all of them to guide the search for a larger robot. We introduce the concept of disabled regions that are prohibited from the exploration using the sampling process. The disabled regions are defined using trajectories already found in the space being searched. The proposed method can solve planning problems with narrow passages with higher success rate than other state-of-the-art planners.

In the task of cooperative surveillance using Micro Aerial Vehicles (MAVs), MAVs cooperatively observe a given set of Areas of Interest (AoI). The missions are usually prepared in a decoupled manner: first, the sensing locations are found, followed by computations of the trajectories assuming GPS-based localization. The precision of GPS may, however, be insufficient to keep the MAVs in compact groups, which may lead to mutual collisions. To avoid the collisions between MAVs, a camera-based on-board localization has to be used. This however requires to maintain positions of the team members in the given range to enable reliable on-board localization (each MAV has to be visible from other ones). The task of the mission planning is to find an appropriate distribution of MAVs above AoIs together with feasible trajectories from a depot to reach these locations. The on-board localization constraints and MAV motion constraints have to be satisfied during the entire mission. We propose a modification of RRT (Rapidly Exploring Random Tree) for this mission planning. The algorithm first explores the state space to find suitable sensing locations together with feasible trajectories towards them. Then, the sensing locations are optimized using Particle Swarm optimization (PSO). The proposed method has been verified in numerous simulations and outdoor experiments. The achieved results exhibit significantly better performance in terms of lower computational power and complexity of solved scenarios than the state-of-the-art solutions.

Path planning of 3D objects, where the task is to find a collision-free path for a rigid 3D object among obstacles, is studied in this paper. This task has many applications mainly in robotics, but also in other fields, e.g., in computer-aided design and computational biology. Sampling-based approaches like Rapidly Exploring Random Trees (RRT) solve the problem by randomized search in the corresponding configuration space. A well known bottleneck of sampling-based methods is the narrow passage problem. Narrow passages are small regions in the configuration space that are difficult to cover by the random samples, which prevents to find a path leading through them. In this paper, we propose a novel extension to Rapidly Exploring Random Tree (RRT) to cope with the narrow passage problem. The proposed planner first solves a simplified (relaxed) version of the problem which is achieved, e.g., by reducing the geometry of the robot. This approximate solution is then used to guide the search in the configuration space for a less relaxed version of the problem, i.e., for a larger robot. The proposed approach is compared to several state-of-the-art path planners in a set of 3D planning benchmarks. Besides, the method is verified also in the task of computing exit pathways for small molecules (ligand) from a protein.

In the bin picking problem, the task is to automatically unload objects from a container using a robotic manipulator. The task is often approached by organizing the objects into a predictable pattern, e.g., a workpiece carrier, in order to simplify all integral subtasks like object recognition, motion planning and grasping. In such a case, motion planning can even be solved offline as it is ensured that the objects are always at the same positions at known times. However, there is a growing demand for non-structured bin picking, where the objects can be placed randomly in the bins. This arises from recent trends of transforming classical factories into smart production facilities allowing small lot sizes at the efficiency of mass production. The demand for fast and highly flexible handling and manipulation abilities of industrial robots requires to solve all the bin picking methods, including motion planning, online. In this paper, we propose a novel technique for fast sampling-based motion planning of robotic manipulators using motion primitives. Motion primitives are short trajectories that boost search of the configuration space and consequently speed up the planning phase. The proposed work has been verified in a simulation and on a prototype of a bin picking system.

Modular robots are built of many basic robotic modules that can be connected into robots of various shapes. These robots are able to recover from a failure by ejecting and replacing damaged modules. Although this type of failure recovery is usually suggested in literature, it may be precluded due inability of the broken modules to cooperate or if no spare modules are available. In such a case, locomotion of a damaged robot should be adapted to allow the robot to reach a repair station or even to finish its task without the need to exchange the broken modules. In this paper, we investigate how to adapt motions of modular robots with respect to failures using the concept of motion planning with motion primitives. The ability of the proposed system to recover from failures is verified in a simulation and also in a HW experiment.

Modular robots, which are systems made of many robotic modules, can utilize various types of locomotion. Different approaches can be used to generate these basic motion skills — motion primitives. To move in a complex environment, several motion primitives are needed and a mechanism to switch them is required. This can be realized using a high-level motion planning. To enable autonomous operation of modular robots equipped with limited computational resources, it is necessary to generate the motion plans on-board, i.e., without external computers. In this paper, we propose a novel simplified motion model of a modular robot, which allows the robot to employ the motion planner as a fast on-board replanner. The proposed approach has been verified both in simulations as well as with real robots.

The ability to move in complex environments is a key property required for deployment of modular robots in challenging applications like search & rescue missions or space exploration. Wide range of motion types like crawling or walking can be achieved using Central Pattern Generators producing periodic control signals. Although these motions can be very effective to steer robots in their vicinity or in a given direction, they need to be switched to reach a far position in the environment. This paper presents a novel modification of Rapidly Exploring Random Tree (RRT) algorithm for modular robots. For efficient exploration of the configuration space, predefined motion primitives are used. While the motion primitives provide effective local motions, the RRT-based planner switches them in order to reach the desired global goal.

This paper presents a multi-goal path planning framework based on a self-organizing map algorithm and a model of the navigation describing evolution of the localization error. The framework combines finding a sequence of goals' visits with a goal-to-goal path planning considering localization uncertainty. The approach is able to deal with local properties of the environment such as expected visible landmarks usable for the navigation. The local properties affect the performance of the navigation, and therefore, the framework can take the full advantage of the local information together with the global sequence of the goals' visits to find a path improving the autonomous navigation. Experimental results in real outdoor and indoor environments indicate that the framework provides paths that effectively decreases the localization uncertainty; thus, increases the reliability of the autonomous goals' visits.

Several navigation tasks utilizing a low-cost Micro Aerial Vehicle (MAV) platform AR-drone are presented in this paper to show how it can be used in an experimental verification of scientific theories and developed methodologies. An important part of this paper is an attached video showing a set of such experiments. The presented methods rely on visual navigation and localization using on-board cameras of the AR-drone employed in the control feedback. The aim of this paper is to demonstrate flight performance of this platform in real world scenarios of mobile robotics.

A navigation and stabilization scheme for 3D heterogeneous (UAVs and UGVs) formations acting under a hawk-eye like relative localization is presented in this paper. We formulate a novel Model Predictive Control (MPC) based concept for formation driving in a leader-follower constellation into a required target region. The formation to target region problem in 3D is solved using the MPC methodology for both: i) the trajectory planning and control of a virtual leader, and ii) the control and stabilization of followers - UAVs and UGVs. The core of the method lies in a novel avoidance function based on a model of the formation respecting requirements of the direct visibility between the team members in environment with obstacles, which is crucial for the hawk-eye localization.

A system for autonomous roads sweeping by applying formations of mobile robots is presented in this paper. The proposed approach based on Receding Horizon Control solves the formation navigation, planning and stabilization in real-word environments with static and dynamic obstacles. The formations employed for sweeping are built up ad-hoc, taking into account length of robots' effectors (e.g. shovels, sweepers) and width of the working area. Presented method enables to smoothly merge smaller teams with the view of sweeping the larger roads (e.g. runways, highways). The formations can operate in two modes: sweeping and moving. In the sweeping mode, the formations are guided with an aim to effectively cover the cleaning roads, while in the moving mode, the planning system emphasizes the effort to reach a desired target. Furthermore, the moving mode enables to autonomously design complex formation maneuvers, as is reverse driving or turning on spot.

In this paper, we present results of a feasibility study of airport snow shoveling with multiple formations of autonomous snowplow robots. The main idea of the approach is to form temporary coalitions of vehicles, whose size depends on the width of the roads to be cleaned. We propose to divide the problem of snow shoveling into the subproblems of task allocation and motion coordination. For the task allocation we designed a multi-agent method applicable in the dynamic environment of airports. The motion coordination part focuses on generating trajectories for the vehicle formations based on the output of the task allocation module. Furthermore, we have developed a novel approach of formation stabilization into variable shapes depending on the width of runways. The method using a receding horizon control provides optimal trajectories and inputs for robots' actuators during splitting and coupling of formations. The algorithm can be utilized in arbitrary static and dynamic airport assemblage. All components as well as the complete system have been verified in various simulations and hardware experiments in both indoor and outdoor environments, which are presented in the submitted video.

In this paper, we present a framework that applies multiple groups of autonomous snowploughs for efficiently removing the snow from airfields. The proposed approach includes formation stabilization into variable shapes depending on the width of runways. The paper is focused on trajectory planning and control during splitting and coupling of formations for cleaning smaller auxiliary roads surrounding main runways. We propose a general method using a receding horizon control for iterative formation assignment. The algorithm is adapted for the kinematics of car-like robots and can be utilized in arbitrary static and dynamic airport assemblage. The proposed approach has been verified by simulations and by hardware experiments.