124,530 research outputs found
Safe Sequential Path Planning Under Disturbances and Imperfect Information
Multi-UAV systems are safety-critical, and guarantees must be made to ensure
no unsafe configurations occur. Hamilton-Jacobi (HJ) reachability is ideal for
analyzing such safety-critical systems; however, its direct application is
limited to small-scale systems of no more than two vehicles due to an
exponentially-scaling computational complexity. Previously, the sequential path
planning (SPP) method, which assigns strict priorities to vehicles, was
proposed; SPP allows multi-vehicle path planning to be done with a
linearly-scaling computational complexity. However, the previous formulation
assumed that there are no disturbances, and that every vehicle has perfect
knowledge of higher-priority vehicles' positions. In this paper, we make SPP
more practical by providing three different methods to account for disturbances
in dynamics and imperfect knowledge of higher-priority vehicles' states. Each
method has different assumptions about information sharing. We demonstrate our
proposed methods in simulations.Comment: American Control Conference, 201
Decentralized Connectivity-Preserving Deployment of Large-Scale Robot Swarms
We present a decentralized and scalable approach for deployment of a robot
swarm. Our approach tackles scenarios in which the swarm must reach multiple
spatially distributed targets, and enforce the constraint that the robot
network cannot be split. The basic idea behind our work is to construct a
logical tree topology over the physical network formed by the robots. The
logical tree acts as a backbone used by robots to enforce connectivity
constraints. We study and compare two algorithms to form the logical tree:
outwards and inwards. These algorithms differ in the order in which the robots
join the tree: the outwards algorithm starts at the tree root and grows towards
the targets, while the inwards algorithm proceeds in the opposite manner. Both
algorithms perform periodic reconfiguration, to prevent suboptimal topologies
from halting the growth of the tree. Our contributions are (i) The formulation
of the two algorithms; (ii) A comparison of the algorithms in extensive
physics-based simulations; (iii) A validation of our findings through
real-robot experiments.Comment: 8 pages, 8 figures, submitted to IROS 201
Safe Sequential Path Planning of Multi-Vehicle Systems via Double-Obstacle Hamilton-Jacobi-Isaacs Variational Inequality
We consider the problem of planning trajectories for a group of vehicles,
each aiming to reach its own target set while avoiding danger zones of other
vehicles. The analysis of problems like this is extremely important
practically, especially given the growing interest in utilizing unmanned
aircraft systems for civil purposes. The direct solution of this problem by
solving a single-obstacle Hamilton-Jacobi-Isaacs (HJI) variational inequality
(VI) is numerically intractable due to the exponential scaling of computation
complexity with problem dimensionality. Furthermore, the single-obstacle HJI VI
cannot directly handle situations in which vehicles do not have a common
scheduled arrival time. Instead, we perform sequential path planning by
considering vehicles in order of priority, modeling higher-priority vehicles as
time-varying obstacles for lower-priority vehicles. To do this, we solve a
double-obstacle HJI VI which allows us to obtain the reach-avoid set, defined
as the set of states from which a vehicle can reach its target while staying
within a time-varying state constraint set. From the solution of the
double-obstacle HJI VI, we can also extract the latest start time and the
optimal control for each vehicle. This is a first application of the
double-obstacle HJI VI which can handle systems with time-varying dynamics,
target sets, and state constraint sets, and results in computation complexity
that scales linearly, as opposed to exponentially, with the number of vehicles
in consideration.Comment: European Control Conference 201
Adaptive Governance and Evolving Solutions to Natural Resource Conflicts
New Zealand is facing increasing challenges in managing natural resources (land, freshwater, marine space and air quality) under pressures from domestic (population growth, agricultural intensification, cultural expectations) and international (climate change) sources. These challenges can be described in terms of managing ‘wicked problems’; i.e. problems that may not be understood fully until they have been solved, where stakeholders have different world views and frames for understanding the problem, the constraints affecting the problem and the resources required to solve it change over time, and no complete solution is ever actually found. Adaptive governance addresses wicked problems through a framework to engage stakeholders in a participative process to create a long term vision. The vision must identify competing goals and a process for balancing them over time that acknowledges conflicts cannot always be resolved in a single lasting decision. Circumstances, goals and priorities can all vary over time and by region. The Resource Management Act can be seen as an adaptive governance structure where frameworks for resources such as water may take years to evolve and decades to fully implement. Adaptive management is about delivery through an incremental/experimental approach, limits on the certainty that governments can provide and stakeholders can demand, and flexibility in processes and results. In New Zealand it also requires balancing central government expertise and resources, with local authorities which can reflect local goals and knowledge, but have varying resources and can face quite distinct issues of widely differing severity. It is important to signal the incremental, overlapping, iterative and time-consuming nature of the work involved in developing and implementing adaptive governance and management frameworks. Managing the expectations of those involved as to the nature of the process and their role in it, and the scope and timing of likely outcomes, is key to sustaining participation.Adaptive capacity; governance; resilience
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