3,247 research outputs found
Data-Driven Risk-sensitive Model Predictive Control for Safe Navigation in Multi-Robot Systems
Safe navigation is a fundamental challenge in multi-robot systems due to the
uncertainty surrounding the future trajectory of the robots that act as
obstacles for each other. In this work, we propose a principled data-driven
approach where each robot repeatedly solves a finite horizon optimization
problem subject to collision avoidance constraints with latter being formulated
as distributionally robust conditional value-at-risk (CVaR) of the distance
between the agent and a polyhedral obstacle geometry. Specifically, the CVaR
constraints are required to hold for all distributions that are close to the
empirical distribution constructed from observed samples of prediction error
collected during execution. The generality of the approach allows us to
robustify against prediction errors that arise under commonly imposed
assumptions in both distributed and decentralized settings. We derive tractable
finite-dimensional approximations of this class of constraints by leveraging
convex and minmax duality results for Wasserstein distributionally robust
optimization problems. The effectiveness of the proposed approach is
illustrated in a multi-drone navigation setting implemented in Gazebo platform
Learning for Multi-robot Cooperation in Partially Observable Stochastic Environments with Macro-actions
This paper presents a data-driven approach for multi-robot coordination in
partially-observable domains based on Decentralized Partially Observable Markov
Decision Processes (Dec-POMDPs) and macro-actions (MAs). Dec-POMDPs provide a
general framework for cooperative sequential decision making under uncertainty
and MAs allow temporally extended and asynchronous action execution. To date,
most methods assume the underlying Dec-POMDP model is known a priori or a full
simulator is available during planning time. Previous methods which aim to
address these issues suffer from local optimality and sensitivity to initial
conditions. Additionally, few hardware demonstrations involving a large team of
heterogeneous robots and with long planning horizons exist. This work addresses
these gaps by proposing an iterative sampling based Expectation-Maximization
algorithm (iSEM) to learn polices using only trajectory data containing
observations, MAs, and rewards. Our experiments show the algorithm is able to
achieve better solution quality than the state-of-the-art learning-based
methods. We implement two variants of multi-robot Search and Rescue (SAR)
domains (with and without obstacles) on hardware to demonstrate the learned
policies can effectively control a team of distributed robots to cooperate in a
partially observable stochastic environment.Comment: Accepted to the 2017 IEEE/RSJ International Conference on Intelligent
Robots and Systems (IROS 2017
Channel Selection for Network-assisted D2D Communication via No-Regret Bandit Learning with Calibrated Forecasting
We consider the distributed channel selection problem in the context of
device-to-device (D2D) communication as an underlay to a cellular network.
Underlaid D2D users communicate directly by utilizing the cellular spectrum but
their decisions are not governed by any centralized controller. Selfish D2D
users that compete for access to the resources construct a distributed system,
where the transmission performance depends on channel availability and quality.
This information, however, is difficult to acquire. Moreover, the adverse
effects of D2D users on cellular transmissions should be minimized. In order to
overcome these limitations, we propose a network-assisted distributed channel
selection approach in which D2D users are only allowed to use vacant cellular
channels. This scenario is modeled as a multi-player multi-armed bandit game
with side information, for which a distributed algorithmic solution is
proposed. The solution is a combination of no-regret learning and calibrated
forecasting, and can be applied to a broad class of multi-player stochastic
learning problems, in addition to the formulated channel selection problem.
Analytically, it is established that this approach not only yields vanishing
regret (in comparison to the global optimal solution), but also guarantees that
the empirical joint frequencies of the game converge to the set of correlated
equilibria.Comment: 31 pages (one column), 9 figure
Multi-Agent Chance-Constrained Stochastic Shortest Path with Application to Risk-Aware Intelligent Intersection
In transportation networks, where traffic lights have traditionally been used
for vehicle coordination, intersections act as natural bottlenecks. A
formidable challenge for existing automated intersections lies in detecting and
reasoning about uncertainty from the operating environment and human-driven
vehicles. In this paper, we propose a risk-aware intelligent intersection
system for autonomous vehicles (AVs) as well as human-driven vehicles (HVs). We
cast the problem as a novel class of Multi-agent Chance-Constrained Stochastic
Shortest Path (MCC-SSP) problems and devise an exact Integer Linear Programming
(ILP) formulation that is scalable in the number of agents' interaction points
(e.g., potential collision points at the intersection). In particular, when the
number of agents within an interaction point is small, which is often the case
in intersections, the ILP has a polynomial number of variables and constraints.
To further improve the running time performance, we show that the collision
risk computation can be performed offline. Additionally, a trajectory
optimization workflow is provided to generate risk-aware trajectories for any
given intersection. The proposed framework is implemented in CARLA simulator
and evaluated under a fully autonomous intersection with AVs only as well as in
a hybrid setup with a signalized intersection for HVs and an intelligent scheme
for AVs. As verified via simulations, the featured approach improves
intersection's efficiency by up to while also conforming to the
specified tunable risk threshold
Severity-sensitive norm-governed multi-agent planning
This research was funded by Selex ES. The software developed during this research, including the norm analysis and planning algorithms, the simulator and harbour protection scenario used during evaluation is freely available from doi:10.5258/SOTON/D0139Peer reviewedPublisher PD
Review of trends and targets of complex systems for power system optimization
Optimization systems (OSs) allow operators of electrical power systems (PS) to optimally operate PSs and to also create optimal PS development plans. The inclusion of OSs in the PS is a big trend nowadays, and the demand for PS optimization tools and PS-OSs experts is growing. The aim of this review is to define the current dynamics and trends in PS optimization research and to present several papers that clearly and comprehensively describe PS OSs with characteristics corresponding to the identified current main trends in this research area. The current dynamics and trends of the research area were defined on the basis of the results of an analysis of the database of 255 PS-OS-presenting papers published from December 2015 to July 2019. Eleven main characteristics of the current PS OSs were identified. The results of the statistical analyses give four characteristics of PS OSs which are currently the most frequently presented in research papers: OSs for minimizing the price of electricity/OSs reducing PS operation costs, OSs for optimizing the operation of renewable energy sources, OSs for regulating the power consumption during the optimization process, and OSs for regulating the energy storage systems operation during the optimization process. Finally, individual identified characteristics of the current PS OSs are briefly described. In the analysis, all PS OSs presented in the observed time period were analyzed regardless of the part of the PS for which the operation was optimized by the PS OS, the voltage level of the optimized PS part, or the optimization goal of the PS OS.Web of Science135art. no. 107
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