31 research outputs found
tinySLAM-based exploration with a swarm of nano-UAVs
This paper concerns SLAM and exploration for a swarm of nano-UAVs. The laser
range finder-based tinySLAM algorithm is used to build maps of the environment.
The maps are synchronized using an iterative closest point algorithm. The UAVs
then explore the map by steering to points selected by a modified dynamic
coverage algorithm, for which we prove a stability result. Both algorithms
inform each other, allowing the UAVs to map out new areas of the environment
and move into them for exploration. Experimental findings using the nano-UAV
Crazyflie 2.1 platform are presented. A key challenge is to implement all
algorithms on the hardware limited experimental platform.Comment: Published at the Sixth International Symposium on Swarm Behavior and
Bio-Inspired Robotics 2023 (SWARM 6th 2023). Pages 899-90
Swarm Bug Algorithms for Path Generation in Unknown Environments
In this paper, we consider the problem of a swarm traveling between two
points as fast as possible in an unknown environment cluttered with obstacles.
Potential applications include search-and-rescue operations where damaged
environments are typical. We present swarm generalizations, called SwarmCom,
SwarmBug1, and SwarmBug2, of the classical path generation algorithms Com,
Bug1, and Bug2. These algorithms were developed for unknown environments and
require low computational power and memory storage, thereby freeing up
resources for other tasks. We show the upper bound of the worst-case travel
time for the first agent in the swarm to reach the target point for SwarmBug1.
For SwarmBug2, we show that the algorithm underperforms in terms of worst-case
travel time compared to SwarmBug1. For SwarmCom, we show that there exists a
trivial scene for which the algorithm will not halt, and it thus has no
performance guarantees. Moreover, by comparing the upper bound of the travel
time for SwarmBug1 with a universal lower bound for any path generation
algorithm, it is shown that in the limit when the number of agents in the swarm
approaches infinity, no other algorithm has strictly better worst-case
performance than SwarmBug1 and the universal lower bound is tight.Comment: Accepted for IEEE Conference on Decision and Control, Dec. 13-15,
2023, Singapor
Intrinsic Reduced Attitude Formation with Ring Inter-Agent Graph
This paper investigates the reduced attitude formation control problem for a
group of rigid-body agents using feedback based on relative attitude
information. Under both undirected and directed cycle graph topologies, it is
shown that reversing the sign of a classic consensus protocol yields
asymptotical convergence to formations whose shape depends on the parity of the
group size. Specifically, in the case of even parity the reduced attitudes
converge asymptotically to a pair of antipodal points and distribute
equidistantly on a great circle in the case of odd parity. Moreover, when the
inter-agent graph is an undirected ring, the desired formation is shown to be
achieved from almost all initial states