This paper presents an approach for optimizing trajectories of autonomous vehicles in evacuation scenarios, particularly for the transportation of handicapped individuals, animals, and products. First, the hybrid model predictive control (HMPC) technique takes into account partially known infrastructure and dynamic obstacles, and allows for multiple secure destinations. The approach utilizes a platform-specific dynamics model, a global cost-to-go (CTG) function, and contextual awareness. In addition, the CTG function is generated using a Dijkstra algorithm and pseudo priority queues (PPQ), and allows for efficient trajectory planning on large maps. Furthermore, the control actions are generated in real-time using a high-frequency MPC process based on stochastic dynamic programming (SDP), using the most recent CTG information and a model of the vehicle dynamics and the perceived environment, including static and dynamic objects and skid-slip effects. Finally, the effectiveness of the proposed technique is validated through simulations.

In this paper, we explore the application of nonlinear networked predictive control (NNPC) to the unmanned ground vehicle (UGV) that experiences skid-slip, static and moving obstacles, and communication delays in both sensor-to-controller (SC) and controller-to-actuator (CA) channels. Our approach utilizes adaptive dynamic programming (ADP) based model predictive control (MPC) and introduces a delay compensation strategy, effectively mitigating the impact of communication delays on control performance. Specifically, our proposed method generates a control sequence that accounts for delayed control inputs and transmits it to the actuator when the controller node receives a new state measurement. To ensure robustness, our method explicitly considers the presence of collision avoidance and skid-slip and employs an ADP process to develop the control law, which receives state estimation from the Extended Kalman filter (EKF). By integrating these elements, our approach provides an effective and reliable solution for the path-tracking control of the UGV in the presence of communication delays and external disturbances. Finally, comprehensive simulations are performed on a large map and traditional circular trajectory to test and validate the performance of the proposed control scheme.

This paper presents a novel proximal planning and control (PPC) formulation for an unmanned ground vehicle (UGV) affected by skidding and slip disturbances. The control approach also considers the presence of moving and static obstacles in the context of operation. The PPC technique is divided into three parts;  first, a nonlinear model predictive control (NMPC) based path-planner is designed to periodically generate an updated feasible trajectory to prevent the vehicle from colliding with other objects from start to the goal pose. In particular, a proximal averaged Newton-type method for optimal control (PANOC) is used to design NMPC. Second, evolutionary programming (EP) based kinematic control (KC) is designed to generate the velocity commands. Third, a dynamic control law is proposed with an extended state observer (ESO) to estimate the effects of bounded but unknown perturbations. Finally,  simulations are performed in the presence of linear and nonlinear trajectories of moving obstacles (MO) and static obstacles (SO) to verify the performance of the proposed scheme. Additionally, we have investigated and confirmed that the proposed PPC could run in real-time on a CPU with limited resources.