190,340 research outputs found
Efficient approaches for multi-agent planning
Multi-agent planning (MAP) deals with planning systems that reason on long-term goals by multiple collaborative agents which want to maintain privacy on their knowledge. Recently, new MAP techniques have been devised to provide efficient solutions. Most approaches expand distributed searches using modified planners, where agents exchange public information. They present two drawbacks: they are planner-dependent; and incur a high communication cost. Instead, we present two algorithms whose search processes are monolithic (no communication while individual planning) and MAP tasks are compiled such that they are planner-independent (no programming effort needed when replacing the base planner). Our two approaches first assign each public goal to a subset of agents. In the first distributed approach, agents iteratively solve problems by receiving plans, goals and states from previous agents. After generating new plans by reusing previous agents' plans, they share the new plans and some obfuscated private information with the following agents. In the second centralized approach, agents generate an obfuscated version of their problems to protect privacy and then submit it to an agent that performs centralized planning. The resulting approaches are efficient, outperforming other state-of-the-art approaches.This work has been partially supported by MICINN projects TIN2008-06701-C03-03, TIN2011-27652-C03-02 and TIN2014-55637-C2-1-R
Decomposing GR(1) Games with Singleton Liveness Guarantees for Efficient Synthesis
Temporal logic based synthesis approaches are often used to find trajectories
that are correct-by-construction for tasks in systems with complex behavior.
Some examples of such tasks include synchronization for multi-agent hybrid
systems, reactive motion planning for robots. However, the scalability of such
approaches is of concern and at times a bottleneck when transitioning from
theory to practice. In this paper, we identify a class of problems in the GR(1)
fragment of linear-time temporal logic (LTL) where the synthesis problem allows
for a decomposition that enables easy parallelization. This decomposition also
reduces the alternation depth, resulting in more efficient synthesis. A
multi-agent robot gridworld example with coordination tasks is presented to
demonstrate the application of the developed ideas and also to perform
empirical analysis for benchmarking the decomposition-based synthesis approach
CDS-MIP: CDS-based Multiple Itineraries Planning for mobile agents in wireless sensor network
using multi agents in the wireless sensor networks (WSNs) for aggregating data has gained significant attention. Planning the optimal itinerary of the mobile agent is an essential step before the process of data gathering. Many approaches have been proposed to solve the problem of planning MAs itineraries, but all of those approaches are assuming that the MAs visit all SNs and large number of intermediate nodes. This assumption imposed a burden; the size of agent increases with the increase in the visited SNs, therefore consume more energy and spend more time in its migration. None of those proposed approaches takes into account the significant role that the connected dominating nodes play as virtual infrastructure in such wireless sensor networks WSNs. This article introduces a novel energy-efficient itinerary planning algorithmic approach based on the minimum connected dominating sets (CDSs) for multi-agents dedicated in data gathering process. In our proposed approach, instead of planning the itineraries over all sensor nodes SNs, we plan the itineraries among subsets of the MCDS in each cluster. Thus, no need to move the agent in all the SNs, and the intermediate nodes (if any) in each itinerary will be few. Simulation results have demonstrated that our approach is more efficient than other approaches in terms of overall energy consumption and task execution time
MUI-TARE: Multi-Agent Cooperative Exploration with Unknown Initial Position
Multi-agent exploration of a bounded 3D environment with unknown initial
positions of agents is a challenging problem. It requires quickly exploring the
environments as well as robustly merging the sub-maps built by the agents. We
take the view that the existing approaches are either aggressive or
conservative: Aggressive strategies merge two sub-maps built by different
agents together when overlap is detected, which can lead to incorrect merging
due to the false-positive detection of the overlap and is thus not robust.
Conservative strategies direct one agent to revisit an excessive amount of the
historical trajectory of another agent for verification before merging, which
can lower the exploration efficiency due to the repeated exploration of the
same space. To intelligently balance the robustness of sub-map merging and
exploration efficiency, we develop a new approach for lidar-based multi-agent
exploration, which can direct one agent to repeat another agent's trajectory in
an \emph{adaptive} manner based on the quality indicator of the sub-map merging
process. Additionally, our approach extends the recent single-agent
hierarchical exploration strategy to multiple agents in a \emph{cooperative}
manner by planning for agents with merged sub-maps together to further improve
exploration efficiency. Our experiments show that our approach is up to 50\%
more efficient than the baselines on average while merging sub-maps robustly.Comment: 8 pages, 8 figures, Submitted to IEEE RA
Stackelberg Meta-Learning for Strategic Guidance in Multi-Robot Trajectory Planning
Guided cooperation is a common task in many multi-agent teaming applications.
The planning of the cooperation is difficult when the leader robot has
incomplete information about the follower, and there is a need to learn,
customize, and adapt the cooperation plan online. To this end, we develop a
learning-based Stackelberg game-theoretic framework to address this challenge
to achieve optimal trajectory planning for heterogeneous robots. We first
formulate the guided trajectory planning problem as a dynamic Stackelberg game
and design the cooperation plans using open-loop Stackelberg equilibria. We
leverage meta-learning to deal with the unknown follower in the game and
propose a Stackelberg meta-learning framework to create online adaptive
trajectory guidance plans, where the leader robot learns a meta-best-response
model from a prescribed set of followers offline and then fast adapts to a
specific online trajectory guidance task using limited learning data. We use
simulations in three different scenarios to elaborate on the effectiveness of
our framework. Comparison with other learning approaches and no guidance cases
show that our framework provides a more time- and data-efficient planning
method in trajectory guidance tasks
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