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Real-Time Planning Under Uncertainty for AUVs Using Virtual Maps

Ivana Collado-Gonzalez, John McConnell, Jinkun Wang, Paul Szenher, Brendan Englot

TL;DR

This work proposes a computationally efficient planning under uncertainty framework suitable for large-scale, feature-sparse environments, and implements a receding horizon planning strategy, managing a goal-reaching and uncertainty-reduction tradeoff.

Abstract

Reliable localization is an essential capability for marine robots navigating in GPS-denied environments. SLAM, commonly used to mitigate dead reckoning errors, still fails in feature-sparse environments or with limited-range sensors. Pose estimation can be improved by incorporating the uncertainty prediction of future poses into the planning process and choosing actions that reduce uncertainty. However, performing belief propagation is computationally costly, especially when operating in large-scale environments. This work proposes a computationally efficient planning under uncertainty frame-work suitable for large-scale, feature-sparse environments. Our strategy leverages SLAM graph and occupancy map data obtained from a prior exploration phase to create a virtual map, describing the uncertainty of each map cell using a multivariate Gaussian. The virtual map is then used as a cost map in the planning phase, and performing belief propagation at each step is avoided. A receding horizon planning strategy is implemented, managing a goal-reaching and uncertainty-reduction tradeoff. Simulation experiments in a realistic underwater environment validate this approach. Experimental comparisons against a full belief propagation approach and a standard shortest-distance approach are conducted.

Real-Time Planning Under Uncertainty for AUVs Using Virtual Maps

TL;DR

This work proposes a computationally efficient planning under uncertainty framework suitable for large-scale, feature-sparse environments, and implements a receding horizon planning strategy, managing a goal-reaching and uncertainty-reduction tradeoff.

Abstract

Reliable localization is an essential capability for marine robots navigating in GPS-denied environments. SLAM, commonly used to mitigate dead reckoning errors, still fails in feature-sparse environments or with limited-range sensors. Pose estimation can be improved by incorporating the uncertainty prediction of future poses into the planning process and choosing actions that reduce uncertainty. However, performing belief propagation is computationally costly, especially when operating in large-scale environments. This work proposes a computationally efficient planning under uncertainty frame-work suitable for large-scale, feature-sparse environments. Our strategy leverages SLAM graph and occupancy map data obtained from a prior exploration phase to create a virtual map, describing the uncertainty of each map cell using a multivariate Gaussian. The virtual map is then used as a cost map in the planning phase, and performing belief propagation at each step is avoided. A receding horizon planning strategy is implemented, managing a goal-reaching and uncertainty-reduction tradeoff. Simulation experiments in a realistic underwater environment validate this approach. Experimental comparisons against a full belief propagation approach and a standard shortest-distance approach are conducted.
Paper Structure (19 sections, 10 equations, 6 figures, 1 table, 1 algorithm)

This paper contains 19 sections, 10 equations, 6 figures, 1 table, 1 algorithm.

Table of Contents

  1. Introduction
  2. Related Works
  3. Background and Problem Formulation
  4. Simultaneous Localization And Mapping
  5. Virtual Map
  6. Problem Statement
  7. Methodology
  8. Sonar SLAM
  9. Motion Planning
  10. Utility evaluation
  11. Virtual Map Adaptation
  12. Experiments and Results
  13. Experiment Setup
  14. Results Comparison
  15. Marina Environment
  16. ...and 4 more sections

Figures (6)

  • Figure 1: Planning under uncertainty example.Top: Gazebo environment and planning example. Blue star is start location, red cross shows goal location, pink arrows show candidate place-revisiting actions, and green diamond represents goal-reaching candidate action. Bottom: Virtual map and representative resulting paths using different uncertainty handling strategies. Shortest Distance is shown in red, Expectation Maximization in blue, and our proposed strategy in green. The uncertainty growth from executing each path is illustrated along the path using 95% confidence ellipses.
  • Figure 2: System overview: SLAM information from a prior exploration run is used to initialize the current SLAM instance and build a virtual map offline. The motion planner takes the accompanying occupancy map and searches for candidate paths. The utility of each candidate path is assessed using the virtual map. The chosen path and the current robot pose are then passed to the guidance module, which provides the desired velocity vector to follow the desired path. The low-level PID controller takes the desired velocity values and defines the required thruster forces.
  • Figure 3: Virtual maps for all three simulation environments: including planning start (blue star) and all goal positions (numbered red x's).
  • Figure 4: Marina results: goals 1, 2, 3 shown from top to bottom.
  • Figure 5: Fish farm results: goals 1, 2, 3 shown from top to bottom.
  • ...and 1 more figures