4,848 research outputs found
Proximal operators for multi-agent path planning
We address the problem of planning collision-free paths for multiple agents
using optimization methods known as proximal algorithms. Recently this approach
was explored in Bento et al. 2013, which demonstrated its ease of
parallelization and decentralization, the speed with which the algorithms
generate good quality solutions, and its ability to incorporate different
proximal operators, each ensuring that paths satisfy a desired property.
Unfortunately, the operators derived only apply to paths in 2D and require that
any intermediate waypoints we might want agents to follow be preassigned to
specific agents, limiting their range of applicability. In this paper we
resolve these limitations. We introduce new operators to deal with agents
moving in arbitrary dimensions that are faster to compute than their 2D
predecessors and we introduce landmarks, space-time positions that are
automatically assigned to the set of agents under different optimality
criteria. Finally, we report the performance of the new operators in several
numerical experiments.Comment: See movie at http://youtu.be/gRnsjd_ocx
Multi-agent simulation: new approaches to exploring space-time dynamics in GIS
As part of the long term quest to develop more disaggregate, temporally dynamic models of spatial behaviour, micro-simulation has evolved to the point where the actions of many individuals can be computed. These multi-agent systems/simulation(MAS) models are a consequence of much better micro data, more powerful and user-friendly computer environments often based on parallel processing, and the generally recognised need in spatial science for modelling temporal process. In this paper, we develop a series of multi-agent models which operate in cellular space.These demonstrate the well-known principle that local action can give rise to global pattern but also how such pattern emerges as the consequence of positive feedback and learned behaviour. We first summarise the way cellular representation is important in adding new process functionality to GIS, and the way this is effected through ideas from cellular automata (CA) modelling. We then outline the key ideas of multi-agent simulation and this sets the scene for three applications to problems involving the use of agents to explore geographic space. We first illustrate how agents can be programmed to search route networks, finding shortest routes in adhoc as well as structured ways equivalent to the operation of the Bellman-Dijkstra algorithm. We then demonstrate how the agent-based approach can be used to simulate the dynamics of water flow, implying that such models can be used to effectively model the evolution of river systems. Finally we show how agents can detect the geometric properties of space, generating powerful results that are notpossible using conventional geometry, and we illustrate these ideas by computing the visual fields or isovists associated with different viewpoints within the Tate Gallery.Our forays into MAS are all based on developing reactive agent models with minimal interaction and we conclude with suggestions for how these models might incorporate cognition, planning, and stronger positive feedbacks between agents
Human-Centered Autonomy for UAS Target Search
Current methods of deploying robots that operate in dynamic, uncertain
environments, such as Uncrewed Aerial Systems in search \& rescue missions,
require nearly continuous human supervision for vehicle guidance and operation.
These methods do not consider high-level mission context resulting in
cumbersome manual operation or inefficient exhaustive search patterns. We
present a human-centered autonomous framework that infers geospatial mission
context through dynamic feature sets, which then guides a probabilistic target
search planner. Operators provide a set of diverse inputs, including priority
definition, spatial semantic information about ad-hoc geographical areas, and
reference waypoints, which are probabilistically fused with geographical
database information and condensed into a geospatial distribution representing
an operator's preferences over an area. An online, POMDP-based planner,
optimized for target searching, is augmented with this reward map to generate
an operator-constrained policy. Our results, simulated based on input from five
professional rescuers, display effective task mental model alignment, 18\% more
victim finds, and 15 times more efficient guidance plans then current
operational methods.Comment: Extended version to ICRA conference submission. 9 pages, 5 figure
- …