SICNav: Safe and Interactive Crowd Navigation Using Model Predictive Control and Bilevel Optimization

Robots need to predict and react to human motions to navigate through a crowd without collisions. Many existing methods decouple prediction from planning, which does not account for the interaction between robot and human motions and can lead to the robot getting stuck. In this article, we propose s...

Full description

Saved in:
Bibliographic Details
Published in:IEEE transactions on robotics Vol. 41; pp. 801 - 818
Main Authors: Samavi, Sepehr, Han, James R., Shkurti, Florian, Schoellig, Angela P.
Format: Journal Article
Language:English
Published: IEEE 2025
Subjects:
Autonomous vehicle navigation
Collision avoidance
Navigation
Optimization
optimization and optimal control
Pedestrians
Planning
Robot kinematics
Robot motion
Safety
social navigation
Trajectory
Vectors
ISSN:1552-3098, 1941-0468
Online Access:Get full text
Tags: Add Tag
No Tags, Be the first to tag this record!
Description
Summary:Robots need to predict and react to human motions to navigate through a crowd without collisions. Many existing methods decouple prediction from planning, which does not account for the interaction between robot and human motions and can lead to the robot getting stuck. In this article, we propose safe and interactive crowd navigation (SICNav), a model predictive control (MPC) method that jointly solves for robot motion and predicted crowd motion in closed loop. We model each human in the crowd to be following an optimal reciprocal collision avoidance (ORCA) scheme and embed that model as a constraint in the robot's local planner, resulting in a bilevel nonlinear MPC optimization problem. We use a Karush-Kuhn-Tucker (KKT)-reformulation to cast the bilevel problem as a single level and use a nonlinear solver to optimize. Our MPC method can influence pedestrian motion while explicitly satisfying safety constraints in a single-robot multihuman environment. We analyze the performance of SICNav in two simulation environments and indoor experiments with a real robot to demonstrate safe robot motion that can influence the surrounding humans. We also validate the trajectory forecasting performance of ORCA on a human trajectory dataset.
ISSN:1552-3098
1941-0468
DOI:10.1109/TRO.2024.3484634