Multi-Robot Navigation Using Game-Theoretic Crowd Prediction
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Solution Overview
Problem
Social crowd navigation for mobile robots among human pedestrians is challenging due to complex human-robot and human-human interactions, requiring safe, robust, efficient, and socially compatible motion.
Innovation Solution
A computer-implemented method using a potential game and two-player game algorithm to control multiple robots, integrating a social long-short term memory (LSTM) model for human trajectory prediction with model predictive control (MPC) to optimize robot navigation, employing centralized and distributed MPC algorithms for coordinated motion.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If robots navigate through crowded environments with human pedestrians, then navigation efficiency and task completion are improved, but safety and social compatibility deteriorate due to complex human-robot interactions and unpredictability of human behavior
Solution Approach 1:
The system performs preliminary human trajectory prediction using social LSTM models before executing robot navigation. By predicting future human positions and behaviors in advance, the robot can plan collision-free paths proactively rather than reactively, improving both safety and navigation efficiency
Solution Approach 2:
The navigation system continuously monitors human positions, updates trajectory predictions, and adjusts robot paths in real-time based on actual human movements deviating from predictions. This closed-loop feedback ensures ongoing safety and social compatibility while maintaining navigation efficiency
2Productivity
If multiple robots navigate simultaneously in crowded environments, then task completion speed is improved, but coordination complexity and collision risk increase
Solution Approach 1:
The system merges all robot navigation problems into a single unified potential game formulation. By combining individual robot objectives, human interactions, and collision avoidance constraints into one global optimization problem, the system coordinates multiple robots efficiently without requiring complex pairwise negotiations between robots
Solution Approach 2:
The potential game framework acts as an intermediary that mediates between multiple robots and human pedestrians. The potential function serves as a shared objective that all robots optimize simultaneously, coordinating their movements without direct robot-to-robot communication while ensuring collision-free navigation
3Reliability
If robots maintain large safety distances from human pedestrians, then collision avoidance is improved, but navigation efficiency and task completion time deteriorate
Solution Approach 1:
The safety distance between robots and humans is dynamically adjusted based on predicted human trajectories and uncertainty levels. When humans are predicted to move away or maintain stable paths, robots can navigate closer to improve efficiency. When uncertainty increases or collision risk is detected, safety distances automatically expand, balancing efficiency and safety
Data Source
AI summary
Systems and methods for controlling navigation of multiple robots are provided. The robots are configured to move within an environment in which pedestrians are also moving. Centralized and distributed game-theoretical approaches to control of the robots are described.