Predictive Runtime Monitoring for Mobile Robots using Logic-Based Bayesian Intent Inference

08/03/2021
by   Hansol Yoon, et al.
0

We propose a predictive runtime monitoring framework that forecasts the distribution of future positions of mobile robots in order to detect and avoid impending property violations such as collisions with obstacles or other agents. Our approach uses a restricted class of temporal logic formulas to represent the likely intentions of the agents along with a combination of temporal logic-based optimal cost path planning and Bayesian inference to compute the probability of these intents given the current trajectory of the robot. First, we construct a large but finite hypothesis space of possible intents represented as temporal logic formulas whose atomic propositions are derived from a detailed map of the robot's workspace. Next, our approach uses real-time observations of the robot's position to update a distribution over temporal logic formulae that represent its likely intent. This is performed by using a combination of optimal cost path planning and a Boltzmann noisy rationality model. In this manner, we construct a Bayesian approach to evaluating the posterior probability of various hypotheses given the observed states and actions of the robot. Finally, we predict the future position of the robot by drawing posterior predictive samples using a Monte-Carlo method. We evaluate our framework using two different trajectory datasets that contain multiple scenarios implementing various tasks. The results show that our method can predict future positions precisely and efficiently, so that the computation time for generating a prediction is a tiny fraction of the overall time horizon.

READ FULL TEXT
research
09/16/2018

T* : A Heuristic Search Based Algorithm for Motion Planning with Temporal Goals

Motion planning is the core problem to solve for developing any applicat...
research
03/04/2021

DT*: Temporal Logic Path Planning in a Dynamic Environment

Path planning for a robot is one of the major problems in the area of ro...
research
02/18/2020

Spatial Concept-Based Navigation with Human Speech Instructions via Probabilistic Inference on Bayesian Generative Model

Robots are required to not only learn spatial concepts autonomously but ...
research
01/30/2020

Path Planning in Dynamic Environments using Generative RNNs and Monte Carlo Tree Search

State of the art methods for robotic path planning in dynamic environmen...
research
09/14/2023

Efficiently Identifying Hotspots in a Spatially Varying Field with Multiple Robots

In this paper, we present algorithms to identify environmental hotspots ...
research
11/04/2022

Conformal Quantitative Predictive Monitoring of STL Requirements for Stochastic Processes

We consider the problem of predictive monitoring (PM), i.e., predicting ...
research
03/09/2023

Intent-based Deep Reinforcement Learning for Multi-agent Informative Path Planning

In multi-agent informative path planning (MAIPP), agents must collective...

Please sign up or login with your details

Forgot password? Click here to reset