Mixed Observable RRT: Multi-Agent Mission-Planning in Partially Observable Environments

10/03/2021
by   Kasper Johansson, et al.
0

This paper considers centralized mission-planning for a heterogeneous multi-agent system with the aim of locating a hidden target. We propose a mixed observable setting, consisting of a fully observable state-space and a partially observable environment, using a hidden Markov model. First, we construct rapidly exploring random trees (RRTs) to introduce the mixed observable RRT for finding plausible mission plans giving way-points for each agent. Leveraging this construction, we present a path-selection strategy based on a dynamic programming approach, which accounts for the uncertainty from partial observations and minimizes the expected cost. Finally, we combine the high-level plan with model predictive controllers to evaluate the approach on an experimental setup consisting of a quadruped robot and a drone. It is shown that agents are able to make intelligent decisions to explore the area efficiently and to locate the target through collaborative actions.

READ FULL TEXT

page 1

page 6

research
08/13/2021

Q-Mixing Network for Multi-Agent Pathfinding in Partially Observable Grid Environments

In this paper, we consider the problem of multi-agent navigation in part...
research
02/06/2023

Leveraging AI to improve human planning in large partially observable environments

AI can not only outperform people in many planning tasks, but also teach...
research
04/09/2022

Path-Tree Optimization in Partially Observable Environments using Rapidly-Exploring Belief-Space Graphs

Robots often need to solve path planning problems where essential and di...
research
03/31/2022

Multi-Agent Spatial Predictive Control with Application to Drone Flocking (Extended Version)

We introduce the novel concept of Spatial Predictive Control (SPC) to so...
research
06/22/2022

POGEMA: Partially Observable Grid Environment for Multiple Agents

We introduce POGEMA (https://github.com/AIRI-Institute/pogema) a sandbox...
research
08/02/2020

Dynamic Discrete Choice Estimation with Partially Observable States and Hidden Dynamics

Dynamic discrete choice models are used to estimate the intertemporal pr...
research
09/26/2013

Qualitative Possibilistic Mixed-Observable MDPs

Possibilistic and qualitative POMDPs (pi-POMDPs) are counterparts of POM...

Please sign up or login with your details

Forgot password? Click here to reset