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OA-MPC: Occlusion-Aware MPC for Guaranteed Safe Robot Navigation with Unseen Dynamic Obstacles

Roya Firoozi, Alexandre Mir, Gadi Sznaier Camps, Mac Schwager

TL;DR

This work presents a provably safe motion planning scheme for real-time navigation in an a priori unmapped environment, where occluded dynamic agents are present and forward reachable sets associated with potential occluded agents, such as pedestrians, are computed and incorporated into planning.

Abstract

For safe navigation in dynamic uncertain environments, robotic systems rely on the perception and prediction of other agents. Particularly, in occluded areas where cameras and LiDAR give no data, the robot must be able to reason about potential movements of invisible dynamic agents. This work presents a provably safe motion planning scheme for real-time navigation in an a priori unmapped environment, where occluded dynamic agents are present. Safety guarantees are provided based on reachability analysis. Forward reachable sets associated with potential occluded agents, such as pedestrians, are computed and incorporated into planning. An iterative optimization-based planner is presented that alternates between two optimizations: nonlinear Model Predictive Control (NMPC) and collision avoidance. Recursive feasibility of the MPC is guaranteed by introducing a terminal stopping constraint. The effectiveness of the proposed algorithm is demonstrated through simulation studies and hardware experiments with a TurtleBot robot. A video of experimental results is available at \url{https://youtu.be/OUnkB5Feyuk}.

OA-MPC: Occlusion-Aware MPC for Guaranteed Safe Robot Navigation with Unseen Dynamic Obstacles

TL;DR

This work presents a provably safe motion planning scheme for real-time navigation in an a priori unmapped environment, where occluded dynamic agents are present and forward reachable sets associated with potential occluded agents, such as pedestrians, are computed and incorporated into planning.

Abstract

For safe navigation in dynamic uncertain environments, robotic systems rely on the perception and prediction of other agents. Particularly, in occluded areas where cameras and LiDAR give no data, the robot must be able to reason about potential movements of invisible dynamic agents. This work presents a provably safe motion planning scheme for real-time navigation in an a priori unmapped environment, where occluded dynamic agents are present. Safety guarantees are provided based on reachability analysis. Forward reachable sets associated with potential occluded agents, such as pedestrians, are computed and incorporated into planning. An iterative optimization-based planner is presented that alternates between two optimizations: nonlinear Model Predictive Control (NMPC) and collision avoidance. Recursive feasibility of the MPC is guaranteed by introducing a terminal stopping constraint. The effectiveness of the proposed algorithm is demonstrated through simulation studies and hardware experiments with a TurtleBot robot. A video of experimental results is available at \url{https://youtu.be/OUnkB5Feyuk}.
Paper Structure (11 sections, 2 theorems, 7 equations, 14 figures, 1 table, 2 algorithms)

This paper contains 11 sections, 2 theorems, 7 equations, 14 figures, 1 table, 2 algorithms.

Table of Contents

  1. INTRODUCTION
  2. Related work
  3. Notation
  4. Problem Formulation
  5. Recursive Feasibility Guarantees
  6. Iterative Optimization Scheme
  7. NMPC Optimization
  8. Collision Avoidance Optimization
  9. Iterative Optimization Algorithm
  10. Simulations and Hardware Experiments
  11. Conclusion and Future Work

Key Result

Lemma 1

The predicted safe set at absolute time $\tau$ is non-decreasing as $t$ increases, that is,

Figures (14)

  • Figure 1: The ego vehicle (red) equipped with a range-based sensor scans the visible regions. Top: Pedestrians (small blue circles) who are hidden in an occlusion are not detected by the sensor. Middle: Nested capsules representing the forward reachable sets for all pedestrians potentially hidden in the occlusion are computed. Bottom: The vehicle optimizes its trajectory while avoiding the nested capsules along its prediction horizon, coming to a stop at the end of the horizon.
  • Figure 2: Forward reachable sets of unsafe region $\Omega$ are shown for two consecutive time steps $t=0$ and $t=1$ in the simple case of a single pedestrian based on a top speed bound.
  • Figure 3: LiDAR-based collision avoidance: Blue areas are obstacles; Black dots are point clouds; Green circles are centered at down-sampled point clouds.
  • Figure 4: Boundary of occlusion is detected based on a jump in range values of two consecutive points 1 and 2.
  • Figure 5: The capsule set is constructed by polytope and circles.
  • ...and 9 more figures

Theorems & Definitions (4)

  • Lemma 1
  • proof
  • Theorem 2
  • proof