Demos of research activities of MRS group members

Quick links: Swarm robotics demos, Motion planning demos, Modular robotics demos

 

Swarm robotics

3D simulation of swarm movement using the escape behavior method. 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.

Swarms of micro aerial vehicles in a former strip mine. Micro aerial vehicles stabilized relatively to their neighbors within a formation or a swarm. Robots employ an onboard visual localization for their relative stabilization. No external localization system, such as Vicon or GPS is used. Experiments are also conducted in challenging outdoor environment of former pit.

Swarm of self-stabilized unmanned helicopters in indoor environment. A stabilization and control technique developed for steering swarms of unmanned micro aerial vehicles. The approach based on a visual relative localization of swarm particles is designed for utilization of multi-robot teams in real-world dynamic environments. The core of the swarming behaviour is inspired by Reynold's BOID model proposed for 2D simulations of schooling behaviour of fish. The proposed method aspires to be an enabling technique for deployment of swarms of micro areal vehicles outside laboratories that are equipped with precise positioning systems.

Formation of micro aerial vehicles using relative visual localization. Formation of relatively stabilized micro aerial vehicles. Robots employ an onboard visual localization for their stabilization in a changing formation. No external localization system, such as Vicon is used.

Autonomous "snow" shoveling of an airport model. A group of autonomous robots cooperatively cleaning a model of airport from artificial snow. The approach is based on a model predictive control technique.

Navigation, localization and stabilization of formations of unmanned aerial and ground vehicles. A leader-follower formation driving algorithm developed for control of heterogeneous groups of unmanned micro aerial and ground vehicles stabilized under a top-view relative localization is presented. The core of the proposed method lies in a novel 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.

Cooperative UAV-UGV inspection. The video demonstrates a heterogenous UAV-UGV system in an autonomous inspection task. The mission is to visit a set of predefined places. The usual problem of inspection tasks is that while ground robots cannot access all areas, small UAVs are limited by their low flying time and payload. The UAV-UGV team is able to overcome these limitations. The UAV is launched whenever the inspected area seems to be inaccessible. The ground robot heliport is able to adjust the quadrotor position after landing, which will allow to recharge the UAV.

 

Motion planning

Example of solution of 3D Bugtrap benchmark probem, where the task is to find path for a stick robot. The problem is difficult due to narrow passage. The video shows a result obtained using RRT-IS (RRT with Iterative scaling): V Vonasek, J Faigl, T Krajnik and L Preucil. A Sampling Schema for Rapidly Exploring Random Trees Using a Guiding Path. In Proceedings of the 5th European Conference on Mobile Robots. 2011, 201–206. See Motion planning page for details.

Example of solution of 3D Hedge in the cage problem, where the task is to remove the spiky robot out of the cage. The cage has five identical windows with width similar to the radius of the robot's sphere. The video shows a result obtained using RRT-IS (RRT with Iterative scaling): V Vonasek, J Faigl, T Krajnik and L Preucil. A Sampling Schema for Rapidly Exploring Random Trees Using a Guiding Path. In Proceedings of the 5th European Conference on Mobile Robots. 2011, 201–206. See Motion planning page for details.

Example of solution of Alpha puzzle benchmark using RRT with RRT-IS: V Vonasek, J Faigl, T Krajnik and L Preucil. A Sampling Schema for Rapidly Exploring Random Trees Using a Guiding Path. In Proceedings of the 5th European Conference on Mobile Robots. 2011, 201–206. See Motion planning page for details.

And of course: the well known Piano Mover's problem!

Modular robotics

Example of motion planning for a modular robot made of five modules. Locomotion of the robot is generated using Central Pattern Generators. The robot is equipped with four CPG-based motion primitives. A sequence of the primitives is found using RRT-MP algorithm (see Modular robotics page for details).

Example of gait optimization for modular robot. The red module is broken (it cannot be controlled). The optimization starts in a simulation, where four best solutions are selected and further optimized on the HW robot. The second part of the video (00:10) shows the progress of the optimization on real robot. The Fitness value shows actual quality of motion (traveled distances in cm in 30 seconds). The final gait has speed 27 cm/30s = 54 cm/min and it was achieved after 2 minutes of the HW experiment. More info is in the paper: Vonasek, Vojtech; Neumann, Sergej; Oertel, David; Worn, Heinz, "Online motion planning for failure recovery of modular robotic systems," Robotics and Automation (ICRA), 2015 IEEE International Conference on , vol., no., pp.1905,1910, 26-30 May 2015. (see Modular robotics page for details).

A caterpillar-like robot made of five CoSMO modules. The red modules is broken and cannot be controlled. The task is to find motion 'move-left' using a Central Pattern Generator. Optimization is realized using Particle Swarm Optimization.More info is in the paper: Vonasek, Vojtech; Neumann, Sergej; Oertel, David; Worn, Heinz, "Online motion planning for failure recovery of modular robotic systems," Robotics and Automation (ICRA), 2015 IEEE International Conference on , vol., no., pp.1905,1910, 26-30 May 2015. (see Modular robotics page for details).