Pedestrian regulation can prevent crowd accidents and improve crowd safety in densely populated areas. Recent studies use mobile robots to regulate pedestrian flows for desired collective motion through the effect of passive human–robot interaction (HRI). This paper formulates a robot motion planning problem for the optimization of two merging pedestrian flows moving through a bottleneck exit. To address the challenge of feature representation of complex human motion dynamics under the effect of HRI, we propose using a deep neural network to model the mapping from the image input of pedestrian environments to the output of robot motion decisions. The robot motion planner is trained end-to-end using a deep reinforcement learning algorithm, which avoids hand-crafted feature detection and extraction, thus improving the learning capability for complex dynamic problems. Our proposed approach is validated in simulated experiments, and its performance is evaluated. The results demonstrate that the robot is able to find optimal motion decisions that maximize the pedestrian outflow in different flow conditions, and the pedestrian-accumulated outflow increases significantly compared to cases without robot regulation and with random robot motion.

A large number of injuries or deaths may occur when an emergency happens in a crowded public place. The congestion at exits may slow down the egress rate due to the effect of “faster-is-slower”. This inspires us to study how human behavior dynamically changes over time at an emergency in a complex indoor environment. In this paper, we refer the panic of evacuees to their perception of the threat and propose a panic propagation model to model how crowd panic changes during evacuation at an emergency. Combined with the existing social force model, our panic model interprets the self-driven force and interactive forces with others in human mobility. To improve evacuation efficiency, robots are introduced to guide evacuees to escape. Using dynamic environment information, we design an evacuation exit selection algorithm where the optimal exit is automatically selected by the robot with the minimum escape time. In our experiments, a real shopping mall is examined, and the dynamic behavior of panicked evacuees is simulated with the proposed panic model. The evacuation performance of using emergency evacuation robots is evaluated. The improvement of evacuation efficiency validates the effectiveness of our robot-assisted evacuation system.

Multi-robot motion planning and control has been investigated for decades and is still an active research area due to the growing demand for both performance optimality and safety assurance. This paper presents an optimization-based method for the coordinated control of multiple robots with optimized control performance and guaranteed collision avoidance. We consider a group of differential drive wheeled robots, and design a distributed model predictive control (DMPC) where the team-level trajectory optimization is decomposed into subproblems solved by individual robots via alternating direction method of multipliers (ADMM). Our DMPC design utilizes a discrete-time control barrier functions (CBF) method to develop control constraints that provide collision avoidance assurance. Compared to existing ADMM-based DMPC methods with Euclidean distance constraint for collision avoidance, our method ensures collision avoidance with minimal compromise of the optimality with respect to primary control objectives. We validated our method in a multi-robot cluster flocking problem. The simulation results show effective coordinated control that achieves improved control performance and safety guarantees.

Due to the fast-is-slower phenomenon in emergency escape, it is desirable to regulate pedestrian flows at the exit or a bottleneck. We propose a new robot-assisted pedestrian regulation and study passive human-robot interaction (HRI). A learning-based motion control approach is presented for a robot to efficiently interact with pedestrians for desirable collective motion. We first formulate the problem into an optimal control framework using the pedestrian dynamics description based on existing social force models with embedded HRI forces. To solve the defined optimal control problem, we propose an adaptive dynamic programming (ADP) approach to provide adjustable motion parameters of the robot to efficiently interact with pedestrians so that the regulated pedestrian flow tracks a desired velocity. The ADP control process only uses observed flow information rather than the models of pedestrians, and the ADP method provides feedback control with online learning and control capability. Simulation results demonstrate that the proposed approach can regulate pedestrian flows to desirable speeds by online learning.

Formation control of multi-robot systems has been extensively studied by model-based methods, where analytic control inputs are constructed based on the kinematics and/or dynamics model and the communication graphs of the multi-robot system. Recently, driven by remarkable advances of robotic learning techniques, emerging studies on learning-based methods for formation control have been developed for adaptive and intelligent control of multi-robot systems. This paper aims to provide a brief overview of our recent development of learning-based formation control, and compare it with a model-based method for a case study of three-robot formation control. Fundamental principles, experimental results and technical challenges are presented, comparing the two different methodologies.

Pedestrian flow in densely populated or congested areas usually presents irregular or turbulent motion state due to competitive behaviors of individual pedestrians, which reduces flow efficiency and raises the risk of crowd accidents. Effective pedestrian flow regulation strategies are highly valuable for flow optimization. Existing studies seek for optimal design of indoor architectural features and spatial placement of pedestrian facilities for the purpose of flow optimization. However, once placed, the stationary facilities are not adaptive to real-time flow changes. In this paper, we investigate the problem of regulating two merging pedestrian flows in a bottleneck area using a mobile robot moving among the pedestrian flows. The pedestrian flows are regulated through dynamic human-robot interaction (HRI) during their collective motion. We adopt an adaptive dynamic programming (ADP) method to learn the optimal motion parameters of the robot in real time, and the resulting outflow through the bottleneck is maximized with the crowd pressure reduced to avoid potential crowd disasters. The proposed algorithm is a data-driven approach that only uses camera observation of pedestrian flows without explicit models of pedestrian dynamics and HRI. Extensive simulation studies are performed in both MATLAB and a robotic simulator to verify the proposed approach and evaluate the performances.