Wolfgang Hönig

Mixed reality for robotics

Mixed Reality can be a valuable tool for research and development in robotics. In this work, we refine the definition of Mixed Reality to accommodate seamless interaction between physical and virtual objects in any number of physical or virtual environments. In particular, we show that Mixed Reality can reduce the gap between simulation and implementation by enabling the prototyping of algorithms on a combination of physical and virtual objects, including robots, sensors, and humans. Robots can be enhanced with additional virtual capabilities, or can interact with humans without sharing physical space. We demonstrate Mixed Reality with three representative experiments, each of which highlights the advantages of our approach. We also provide a testbed for Mixed Reality with three different virtual robotics environments in combination with the Crazyflie 2.0 quadcopter.

Formation change for robot groups in occluded environments

We study formation change for robot groups in known environments. We are given a team of robots partitioned into groups, where robots in the same group are interchangeable with each other. A formation specifies the locations occupied by each group. The objective is to find collision-free paths that move all robots from a given start formation to a given goal formation. Our algorithm TAPF* has the following features: (a) it incorporates kinematic constraints of robots in form of velocity limits; (b) it maintains a user-specified safety distance between robots; (c) it attempts to minimize the makespan; and (d) it runs efficiently for hundreds of robots and dozens of groups even in dense 3D environments with narrow corridors and other occlusions. We demonstrate the efficiency and effectiveness of TAPF* in simulation and on robots.

Crazyswarm: A large nano-quadcopter swarm

We define a system architecture for a large swarm of miniature quadcopters flying in dense formation indoors. The large number of small vehicles motivates novel design choices for state estimation and communication. For state estimation, we develop a method to reliably track many small rigid bodies with identical motion-capture marker arrangements. Our communication infrastructure uses compressed one-way data flow and supports a large number of vehicles per radio. We achieve reliable flight with accurate tracking (< 2 cm mean position error) by implementing the majority of computation onboard, including sensor fusion, control, and some trajectory planning. We provide various examples and empirically determine latency and tracking performance for swarms with up to 49 vehicles.

Summary: Multi-Agent Path Finding with Kinematic Constraints

Multi-Agent Path Finding (MAPF) is well studied in both AI and robotics. Given a discretized environment and agents with assigned start and goal locations, MAPF solvers from AI find collision-free paths for hundreds of agents with user-provided sub-optimality guarantees. However, they ignore that actual robots are subject to kinematic constraints (such as velocity limits) and suffer from imperfect plan-execution capabilities. We therefore introduce MAPF-POST to postprocess the output of a MAPF solver in polynomial time to create a plan-execution schedule that can be executed on robots. This schedule works on non-holonomic robots, considers kinematic constraints, provides a guaranteed safety distance between robots, and exploits slack to avoid time-intensive replanning in many cases. We evaluate MAPF-POST in simulation and on differential-drive robots, showcasing the practicality of our approach.

Flying Multiple UAVs Using ROS

This tutorial chapter will teach readers how to use ROS to fly a small quadcopter both individually and as a group. We will discuss the hardware platform, the Bitcraze Crazyflie 2.0, which is well suited for swarm robotics due to its small size and weight. After first introducing the crazyflie_ros stack and its use on an individual robot, we will extend scenarios of hovering and waypoint following from a single robot to the more complex multi-UAV case. Readers will gain insight into physical challenges, such as radio interference, and how to solve them in practice. Ultimately, this chapter will prepare readers not only to use the stack as-is, but also to extend it or to develop their own innovations on other robot platforms.