359 research outputs found
Deterministic Rendezvous at a Node of Agents with Arbitrary Velocities
We consider the task of rendezvous in networks modeled as undirected graphs.
Two mobile agents with different labels, starting at different nodes of an
anonymous graph, have to meet. This task has been considered in the literature
under two alternative scenarios: weak and strong. Under the weak scenario,
agents may meet either at a node or inside an edge. Under the strong scenario,
they have to meet at a node, and they do not even notice meetings inside an
edge. Rendezvous algorithms under the strong scenario are known for synchronous
agents. For asynchronous agents, rendezvous under the strong scenario is
impossible even in the two-node graph, and hence only algorithms under the weak
scenario were constructed. In this paper we show that rendezvous under the
strong scenario is possible for agents with restricted asynchrony: agents have
the same measure of time but the adversary can arbitrarily impose the speed of
traversing each edge by each of the agents. We construct a deterministic
rendezvous algorithm for such agents, working in time polynomial in the size of
the graph, in the length of the smaller label, and in the largest edge
traversal time.Comment: arXiv admin note: text overlap with arXiv:1704.0888
Rendezvous of Heterogeneous Mobile Agents in Edge-weighted Networks
We introduce a variant of the deterministic rendezvous problem for a pair of
heterogeneous agents operating in an undirected graph, which differ in the time
they require to traverse particular edges of the graph. Each agent knows the
complete topology of the graph and the initial positions of both agents. The
agent also knows its own traversal times for all of the edges of the graph, but
is unaware of the corresponding traversal times for the other agent. The goal
of the agents is to meet on an edge or a node of the graph. In this scenario,
we study the time required by the agents to meet, compared to the meeting time
in the offline scenario in which the agents have complete knowledge
about each others speed characteristics. When no additional assumptions are
made, we show that rendezvous in our model can be achieved after time in a -node graph, and that such time is essentially in some cases
the best possible. However, we prove that the rendezvous time can be reduced to
when the agents are allowed to exchange bits of
information at the start of the rendezvous process. We then show that under
some natural assumption about the traversal times of edges, the hardness of the
heterogeneous rendezvous problem can be substantially decreased, both in terms
of time required for rendezvous without communication, and the communication
complexity of achieving rendezvous in time
Reinforcement Learning and Planning for Preference Balancing Tasks
Robots are often highly non-linear dynamical systems with many degrees of freedom, making solving motion problems computationally challenging. One solution has been reinforcement learning (RL), which learns through experimentation to automatically perform the near-optimal motions that complete a task. However, high-dimensional problems and task formulation often prove challenging for RL. We address these problems with PrEference Appraisal Reinforcement Learning (PEARL), which solves Preference Balancing Tasks (PBTs). PBTs define a problem as a set of preferences that the system must balance to achieve a goal. The method is appropriate for acceleration-controlled systems with continuous state-space and either discrete or continuous action spaces with unknown system dynamics. We show that PEARL learns a sub-optimal policy on a subset of states and actions, and transfers the policy to the expanded domain to produce a more refined plan on a class of robotic problems. We establish convergence to task goal conditions, and even when preconditions are not verifiable, show that this is a valuable method to use before other more expensive approaches. Evaluation is done on several robotic problems, such as Aerial Cargo Delivery, Multi-Agent Pursuit, Rendezvous, and Inverted Flying Pendulum both in simulation and experimentally. Additionally, PEARL is leveraged outside of robotics as an array sorting agent. The results demonstrate high accuracy and fast learning times on a large set of practical applications
Fast Marching based Rendezvous Path Planning for a Team of Heterogeneous Vehicle
A formulation is developed for deterministically calculating the optimized
paths for a multi-agent system consisting of heterogeneous vehicles. The
essence of this formulation is the calculation of the shortest time for each
agent to reach every grid point from its known initial position. Such arrival
time map can be readily assessed using the Fast Marching Method (FMM), a
computational algorithm originally designed for solving boundary value problems
of the Eikonal equation. Leveraging the FMM method, we demonstrate that the
minimal time rendezvous point and paths for all member vehicles can be uniquely
determined with minimal computational concerns. To showcase the potential of
our method, we use an example of a virtual rendezvous scenario that entails the
coordination of a ship, an underwater vehicle, an aerial vehicle, and a ground
vehicle to converge at the optimal location within the Tampa Bay area in
minimal time. It illustrates the value of the developed framework in
efficiently constructing continuous path planning, while accommodating
different operational constraints of heterogeneous member vehicles
The Total s-Energy of a Multiagent System
We introduce the "total s-energy" of a multiagent system with time-dependent
links. This provides a new analytical lens on bidirectional agreement dynamics,
which we use to bound the convergence rates of dynamical systems for
synchronization, flocking, opinion dynamics, and social epistemology
Deep Reinforcement Learning Applied to Spacecraft Attitude Control and Moment of Inertia Estimation via Recurrent Neural Networks
This study investigated two distinct problems related to unknown spacecraft inertia. The first problem explored the use of a recurrent neural network to estimate spacecraft moments of inertia using angular velocity measurements. Initial results showed that, for the configuration examined, the neural network can estimate the moments of inertia when there is a known external torque. The second problem trained a reinforcement learning agent, via proximal policy optimization, to control the attitude of a spacecraft. The results demonstrated that reinforcement learning may be a viable option for guidance and control solutions where the spacecraft model may be unknown. The trained agents displayed a degree of autonomy with their ability to recover from events never experienced in training
Decentralized formation pose estimation for spacecraft swarms
For spacecraft swarms, the multi-agent localization algorithm must scale well with the number of spacecraft and adapt to time-varying communication and relative sensing networks. In this paper, we present a decentralized, scalable algorithm for swarm localization, called the Decentralized Pose Estimation (DPE) algorithm. The DPE considers both communication and relative sensing graphs and defines an observable local formation. Each spacecraft jointly localizes its local subset of spacecraft using direct and communicated measurements. Since the algorithm is local, the algorithm complexity does not grow with the number of spacecraft in the swarm. As part of the DPE, we present the Swarm Reference Frame Estimation (SRFE) algorithm, a distributed consensus algorithm to co-estimate a common Local-Vertical, Local-Horizontal (LVLH) frame. The DPE combined with the SRFE provides a scalable, fully-decentralized navigation solution that can be used for swarm control and motion planning. Numerical simulations and experiments using Caltechâs robotic spacecraft simulators are presented to validate the effectiveness and scalability of the DPE algorithm
Decentralized Formation Pose Estimation for Spacecraft Swarms
For spacecraft swarms, the multi-agent localization algorithm must scale well with the number of spacecraft and adapt to time-varying communication and relative sensing networks. In this paper, we present a decentralized, scalable algorithm for swarm localization, called the Decentralized Pose Estimation (DPE) algorithm. The DPE considers both communication and relative sensing graphs and defines an observable local formation. Each spacecraft jointly localizes its local subset of spacecraft using direct and communicated measurements. Since the algorithm is local, the algorithm complexity does not grow with the number of spacecraft in the swarm. As part of the DPE, we present the Swarm Reference Frame Estimation (SRFE) algorithm, a distributed consensus algorithm to co-estimate a common Local-Vertical, Local-Horizontal (LVLH) frame. The DPE combined with the SRFE provides a scalable, fully-decentralized navigation solution that can be used for swarm control and motion planning. Numerical simulations and experiments using Caltechâs robotic spacecraft simulators are presented to validate the effectiveness and scalability of the DPE algorithm
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