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This paper presents an integrated motion planning system for autonomous vehicle (AV) parking in the presence of other moving vehicles. The proposed system includes 1) a hybrid environment predictor that predicts the motions of the surrounding vehicles and 2) a strategic motion planner that reacts to the predictions. The hybrid environment predictor performs short-term predictions via an extended Kalman filter and an adaptive observer. It also combines short-term predictions with a driver behavior cost-map to make long-term predictions. The strategic motion planner comprises 1) a model predictive control-based safety controller for trajectory tracking; 2) a search-based retreating planner for finding an evasion path in an emergency; 3) an optimization-based repairing planner for planning a new path when the original path is invalidated. Simulation validation demonstrates the effectiveness of the proposed method in terms of initial planning, motion prediction, safe tracking, retreating in an emergency, and trajectory repairing. 

Robot motion planning is one of the important elements in robotics. In environments full of obstacles, it is always challenging to find a collision-free and dynamically feasible path between the robot's initial configuration and goal configuration. While many motion planning algorithms have been proposed in the past, each of them has its pros and cons. This work presents a benchmark which implements and compares existing planning algorithms on a variety of problems with extensive simulation. Based on that, we also propose a hybrid planning algorithm, RRT*-CFS, that combines the merits of sampling-based planning methods and optimization-based planning methods. The first layer, RRT*, quickly samples a semi-optimal path. The second layer, CFS, performs sequential convex optimization given the reference path from RRT*. The proposed RRT*-CFS has feasibility and convergence guarantees. Simulation results show that RRT*-CFS benefits from the hybrid structure and performs robustly in various scenarios including the narrow passage problems. 

Mobile manipulators, constructed by mobile platforms and manipulators, have become a promising solution to future factories for introducing flexibility to manufacturing. This paper presents a method, hierarchical receding horizon control algorithm (HRHC), to assure safety and achieve higher time and space efficiency in robots surrounded by time-varying environments. HRHC contains an optimization based motion planning module that takes account of both the mobile platform and the manipulator to utilize the kinematic redundancy, and a low-level safety controller to deal with fast changes in the environment. With this method, we verify the performance through experiments. The result shows that space efficiency is increased and the HRHC can guarantee local safety in dynamic environments. 

Human motion prediction is non-trivial in modern industrial settings. Accurate prediction of human motion can not only improve efficiency in human-robot collaboration, but also enhance human safety in close proximity to robots. Although many prediction models have been proposed with various parameterization and identification approaches, some fundamental questions remain unclear: what is the necessary parameterization of a prediction model? Is online adaptation of models necessary? Can a prediction model help improve safety and efficiency during human-robot collaboration? These unaddressed questions result from the difficulty of quantitatively evaluating different prediction models in a closed-loop fashion in real human-robot interaction. This paper develops a method to evaluate the closed-loop performance of different prediction models. In particular, we compare models with different parameterizations and models with or without online parameter adaptation. Extensive experiments were conducted on a human-robot collaboration platform. The experimental results demonstrate that human motion prediction significantly enhance the collaboration efficiency and human safety. Adaptable prediction models that are parameterized by neural networks achieve better performance.