Move Beyond Trajectories: Distribution Space Coupling for Crowd Navigation

06/25/2021
by   Muchen Sun, et al.
0

Cooperatively avoiding collision is a critical functionality for robots navigating in dense human crowds, failure of which could lead to either overaggressive or overcautious behavior. A necessary condition for cooperative collision avoidance is to couple the prediction of the agents' trajectories with the planning of the robot's trajectory. However, it is unclear that trajectory based cooperative collision avoidance captures the correct agent attributes. In this work we migrate from trajectory based coupling to a formalism that couples agent preference distributions. In particular, we show that preference distributions (probability density functions representing agents' intentions) can capture higher order statistics of agent behaviors, such as willingness to cooperate. Thus, coupling in distribution space exploits more information about inter-agent cooperation than coupling in trajectory space. We thus introduce a general objective for coupled prediction and planning in distribution space, and propose an iterative best response optimization method based on variational analysis with guaranteed sufficient decrease. Based on this analysis, we develop a sampling-based motion planning framework called DistNav that runs in real time on a laptop CPU. We evaluate our approach on challenging scenarios from both real world datasets and simulation environments, and benchmark against a wide variety of model based and machine learning based approaches. The safety and efficiency statistics of our approach outperform all other models. Finally, we find that DistNav is competitive with human safety and efficiency performance.

READ FULL TEXT

page 1

page 5

research
02/10/2021

Learning Interaction-Aware Trajectory Predictions for Decentralized Multi-Robot Motion Planning in Dynamic Environments

This paper presents a data-driven decentralized trajectory optimization ...
research
09/11/2019

Online Trajectory Generation with Distributed Model Predictive Control for Multi-Robot Motion Planning

We present a distributed model predictive control (DMPC) algorithm to ge...
research
09/10/2019

Force-based Algorithm for Motion Planning of Large Agent Teams

This paper presents a distributed, efficient, scalable and real-time mot...
research
01/28/2022

Machine Learning Based Relative Orbit Transfer for Swarm Spacecraft Motion Planning

In this paper we describe a machine learning based framework for spacecr...
research
09/10/2021

Topology-Informed Model Predictive Control for Anticipatory Collision Avoidance on a Ballbot

We focus on the problem of planning safe and efficient motion for a ball...
research
11/08/2020

Multimodal Trajectory Prediction via Topological Invariance for Navigation at Uncontrolled Intersections

We focus on decentralized navigation among multiple non-communicating ra...
research
02/07/2018

Position-Based Multi-Agent Dynamics for Real-Time Crowd Simulation (MiG paper)

Exploiting the efficiency and stability of Position-Based Dynamics (PBD)...

Please sign up or login with your details

Forgot password? Click here to reset