COLOS - Control and Localization for Swarms of Low-cost Autonomous Robots

UAV group flying in a compact formation. The method relies on a top-view visual relative localization provided by the micro aerial vehicle flying above the rest of the group. It provides a robust solution applicable in complex environments with static and dynamic obstacles. The core of the leader-follower based formation driving method consists in a representation of the entire 3D formation as a convex hull projected along the trajectory of the group. Such an approach provides non-collision solution and respects requirements of the direct visibility between the team members. The obstacle avoidance ability of the method is demonstrated by the two no-fly zones considered during the planning. Distance between UAVs is kept constant during the experiment and it is measured by the relative localization module. The two UAVs flying in the same altitude are equipped with cameras oriented to the side to observe each other. The UAV flying above may observe both UAVs flying lower.

Swarm coverage with relative visual localization

Deployment of the system in surveillance tasks, where locations of interest with different priorities are covered by a self stabilized swarm of UAVs. The planning approach suited for the swarm particles mutually localized by the on-board camera modules and identification patterns. In the planning, the target shape of the spread swarm is found together with trajectories from initial configurations to this target shape. The method does not guarantee that the found distribution of the swarm and the obtained trajectories from initial positions into the found locations are optimal, but feasibility of the solution is guaranteed. The plan of swarm distribution in the environment satisfies constraints given by range of the relative localization, viewing angle of the on-board cameras and it respects mutual UAVs heading and movement constraints. The feasibility of the plan, obtained based on known sets of areas of interest, no-fly zones and initial positions of UAVs, is verified in the experiment.

PSO searching process modified for an odor source localization with swarms of unmanned helicopters. The entire UAV group is represented by the PSO swarm with fitness function corresponding to a virtual concentration of a simulated smoke plume. Each PSO rule is decomposed to independent motion primitives of separate helicopters. UAVs are subsequently moved into new positions required by the PSO process. In each subsequent movement, a quadrocopter approaches into the new location, while the remaining robots keep constant pose and only their Yaw angle is changed to track the moving UAV and to realize the required relative localization. The advantage of such an approach is the possibility to keep the global position of the swarm during the mission.

UAV group flying in a compact formation. The method relies on a visual relative localization between UAVs. It provides a robust solution applicable in complex environments with static and dynamic obstacles. The core of the leader-follower based formation driving method consists in a representation of the entire 3D formation as a convex hull projected along the trajectory of the group. Such an approach provides non-collision solution and respects requirements of the direct visibility between the team members. The obstacle avoidance ability of the method is demonstrated by the two no-fly zones considered during the planning. Distance between UAVs is kept constant during the experiment and it is measured by the relative localization module. The three UAVs are steered in the same altitude in a line formation. The relative positions between robots are measured by cameras oriented to the side. The cameras are placed onboard of each robot. In the experiment, the formation was repeatedly  navigated through the environment to show robustness of the method.

Tangible Fish School Search

Utilization of Fish School Search (FSS) based method for searching in 3D environment. The FSS based algorithm is modified for control a swarm of quadrocopters and it respects motion constraints and limits of the visual relative localization (each UAV must see at least one neighbouring UAV all the time). The adjustment of the FSS algorithm, originally developed for dimensionless particles, is given by deployment of physical swarm particles (UAVs) and mainly includes restrictions that are caused by movement in real search space (collisions during flight, collisions in the target locations given by the FSS rules, dynamic model of UAVs etc.). In the experiments, UAVs cooperatively search for locations with lowest intensity of a signal transmitted from four transmitters distributed in the environment in different altitude. The intensity of the signal is simulated in the experiment based on known locations and transmission power of virtual transmitters.

This movie presents an investigation of swarm control dealing with an escape behavior, which is important functionality in application with human-swarm coexistence. The escape behavior algorithm was extended for the swarm purposes. The movement strategies originally developed for holonomic point particles were replaced with dynamic models of UAVs. Examples of the swarm movement under the rules of the escape behavior using the dynamic models of UAVs are shown in the movie.

A leader-follower formation driving algorithm is employed for control of heterogeneous groups of unmanned micro aerial and ground vehicles stabilized under a top-view relative localization in this video. The core of the method lies in an avoidance function, in which the entire 3D formation is represented by a convex hull projected along a desired path to be followed by the group. Such a representation of the formation provides non-collision trajectories of the robots and respects requirements of the direct visibility between the team members in environment with static as well as dynamic obstacles, which is crucial for the top-view localization. The algorithm is suited for utilization of a simple yet stable visual based navigation of the group (referred to as GeNav), which together with the on-board relative localization enables deployment of large teams of micro-scale robots in environments without any available global localization system.