Model Predictive Path Planning of AGVs: Mixed Logical Dynamical Formulation and Distributed Coordination

Most of the existing path planning methods of automated guided vehicles (AGVs) are static. This paper proposes a new methodology for the path planning of a fleet of AGVs to improve the flexibility, robustness, and scalability of the AGV system. We mathematically describe the transport process as a dynamical system using an ad hoc mixed...

Improving Pedestrian Prediction Models with Self-Supervised Continual Learning

Autonomous mobile robots require accurate human motion predictions to safely and efficiently navigate among pedestrians, whose behavior may adapt to environmental changes. This paper introduces a self-supervised continual learning framework to improve data-driven pedestrian prediction models online across various scenarios continuously. In...

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

This paper presents a data-driven decentralized trajectory optimization approach for multi-robot motion planning in dynamic environments. When navigating in a shared space, each robot needs accurate motion predictions of neighboring robots to achieve predictive collision avoidance. These motion predictions can be obtained among robots by...

Selecting Informative Data Samples for Model Learning Through Symbolic Regression

Continual model learning for nonlinear dynamic systems, such as autonomous robots, presents several challenges. First, it tends to be computationally expensive as the amount of data collected by the robot quickly grows in time. Second, the model accuracy is impaired when data from repetitive motions prevail in the training set and outweigh...