225,561 research outputs found
Optimal Spatial-Temporal Triangulation for Bearing-Only Cooperative Motion Estimation
Vision-based cooperative motion estimation is an important problem for many
multi-robot systems such as cooperative aerial target pursuit. This problem can
be formulated as bearing-only cooperative motion estimation, where the visual
measurement is modeled as a bearing vector pointing from the camera to the
target. The conventional approaches for bearing-only cooperative estimation are
mainly based on the framework distributed Kalman filtering (DKF). In this
paper, we propose a new optimal bearing-only cooperative estimation algorithm,
named spatial-temporal triangulation, based on the method of distributed
recursive least squares, which provides a more flexible framework for designing
distributed estimators than DKF. The design of the algorithm fully incorporates
all the available information and the specific triangulation geometric
constraint. As a result, the algorithm has superior estimation performance than
the state-of-the-art DKF algorithms in terms of both accuracy and convergence
speed as verified by numerical simulation. We rigorously prove the exponential
convergence of the proposed algorithm. Moreover, to verify the effectiveness of
the proposed algorithm under practical challenging conditions, we develop a
vision-based cooperative aerial target pursuit system, which is the first of
such fully autonomous systems so far to the best of our knowledge
Beacon-referenced Mutual Pursuit in Three Dimensions
Motivated by station-keeping applications in various unmanned settings, this
paper introduces a steering control law for a pair of agents operating in the
vicinity of a fixed beacon in a three-dimensional environment. This feedback
law is a modification of the previously studied three-dimensional constant
bearing (CB) pursuit law, in the sense that it incorporates an additional term
to allocate attention to the beacon. We investigate the behavior of the
closed-loop dynamics for a two agent mutual pursuit system in which each agent
employs the beacon-referenced CB pursuit law with regards to the other agent
and a stationary beacon. Under certain assumptions on the associated control
parameters, we demonstrate that this problem admits circling equilibria wherein
the agents move on circular orbits with a common radius, in planes
perpendicular to a common axis passing through the beacon. As the common radius
and distances from the beacon are determined by choice of parameters in the
feedback law, this approach provides a means to engineer desired formations in
a three-dimensional setting
Deep Reinforcement Learning for Swarm Systems
Recently, deep reinforcement learning (RL) methods have been applied
successfully to multi-agent scenarios. Typically, these methods rely on a
concatenation of agent states to represent the information content required for
decentralized decision making. However, concatenation scales poorly to swarm
systems with a large number of homogeneous agents as it does not exploit the
fundamental properties inherent to these systems: (i) the agents in the swarm
are interchangeable and (ii) the exact number of agents in the swarm is
irrelevant. Therefore, we propose a new state representation for deep
multi-agent RL based on mean embeddings of distributions. We treat the agents
as samples of a distribution and use the empirical mean embedding as input for
a decentralized policy. We define different feature spaces of the mean
embedding using histograms, radial basis functions and a neural network learned
end-to-end. We evaluate the representation on two well known problems from the
swarm literature (rendezvous and pursuit evasion), in a globally and locally
observable setup. For the local setup we furthermore introduce simple
communication protocols. Of all approaches, the mean embedding representation
using neural network features enables the richest information exchange between
neighboring agents facilitating the development of more complex collective
strategies.Comment: 31 pages, 12 figures, version 3 (published in JMLR Volume 20
Station Keeping through Beacon-referenced Cyclic Pursuit
This paper investigates a modification of cyclic constant bearing (CB)
pursuit in a multi-agent system in which each agent pays attention to a
neighbor and a beacon. The problem admits shape equilibria with collective
circling about the beacon, with the circling radius and angular separation of
agents determined by choice of parameters in the feedback law. Stability of
circling shape equilibria is shown for a 2-agent system, and the results are
demonstrated on a collective of mobile robots tracked by a motion capture
system
Talking Helps: Evolving Communicating Agents for the Predator-Prey Pursuit Problem
We analyze a general model of multi-agent communication in which all agents communicate simultaneously to a message board. A genetic algorithm is used to evolve multi-agent languages for the predator agents in a version of the predator-prey pursuit problem. We show that the resulting behavior of the communicating multi-agent system is equivalent to that of a Mealy finite state machine whose states are determined by the agents’ usage of the evolved language. Simulations show that the evolution of a communication language improves the performance of the predators. Increasing the language size (and thus increasing the number of possible states in the Mealy machine) improves the performance even further. Furthermore, the evolved communicating predators perform significantly better than all previous work on similar preys. We introduce a method for incrementally increasing
the language size which results in an effective coarse-to-fine search that significantly reduces the evolution time required to find a solution. We present some observations on the effects of language size, experimental setup, and prey difficulty on the evolved Mealy machines. In particular, we observe that the start state is often revisited, and incrementally increasing the language size results in smaller Mealy machines. Finally, a simple rule is derived that provides a pessimistic estimate on the minimum language size that should be used for any multi-agent problem
Perception and steering control in paired bat flight
Animals within groups need to coordinate their reactions to perceived environmental features and to each other in order to safely move from one point to another. This paper extends our previously published work on the flight patterns of Myotis velifer that have been observed in a habitat near Johnson City, Texas. Each evening, these bats emerge from a cave in sequences of small groups that typically contain no more than three or four individuals, and they thus provide ideal subjects for studying leader-follower behaviors. By analyzing the flight paths of a group of M. velifer, the data show that the flight behavior of a follower bat is influenced by the
flight behavior of a leader bat in a way that is not well explained by existing pursuit laws, such as classical pursuit, constant bearing and motion camouflage. Thus we propose an
alternative steering law based on virtual loom, a concept we introduce to capture the geometrical configuration of the leader-follower pair. It is shown that this law may be integrated with our
previously proposed vision-enabled steering laws to synthesize trajectories, the statistics of which fit with those of the bats in our data set. The results suggest that bats use perceived information
of both the environment and their neighbors for navigation.2018-08-0
- …