We consider a planning problem for a robot operating in an information-degraded environment. Our contribution to the state of the art is addressing this problem when robots have limited sensing capabilities, and thus only acquire information in certain locations. We therefore need a method that balances between driving the robot to the goal and toward regions to gain information (or to reduce uncertainty). We present a novel sampling-based planner (Particle Filter based Affine Quadratic Tree --- PF-AQT) that explores the environment, and plans to reach a goal with minimal uncertainty. We then use the output trajectory from PF-AQT to initialize an optimization-based planner that finds a locally optimal trajectory that minimizes control effort and uncertainty. In doing so we reap the exploration benefits of sampling-based methods and exploitation benefits of optimization-based methods for dealing with uncertainty and limited sensing capabilities of the robot. We demonstrate our results using two dynamical systems: double integrator model and a non-holonomic car-like robot.
Opportunistic Multi-Robot Environmental Sampling via Decentralized Markov Decision Processes
We study the problem of information sampling with a group of mobile robots from an unknown environment. Each robot is given a unique region in the environment for the sampling task. The objective of the robots is to visit a subset of locations in the environment such that the collected information is maximized, and consequently, the underlying information model matches as close to reality as possible. The robots have limited communication ranges, and therefore can only communicate when nearby one another. The robots operate in a stochastic environment and their control uncertainty is handled using factored Decentralized Markov Decision Processes (Dec-MDP). When two or more robots communicate, they share their past noisy observations and use a Gaussian mixture model to update their local information models. This in turn helps them to obtain a better Dec-MDP policy. Simulation results show that our proposed strategy is able to predict the information model closer to the ground truth version than compared to other algorithms. Furthermore, the reduction in the overall uncertainty is more than comparable algorithms.
- Award ID(s):
- 1849291
- Publication Date:
- NSF-PAR ID:
- 10333064
- Journal Name:
- International Symposium Distributed Autonomous Robotic Systems
- Sponsoring Org:
- National Science Foundation
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