This paper presents an efficient, robust, comfortable, and real-time motion planning framework for on-road autonomous vehicles. This proposed framework aims to enhance the performance of motion planning in complex environments such as driving in the urban area. It uses a path velocity decomposition method to separate the motion planning problem into path planning and velocity planning. The novelty lies in the use of Historical data in the$SL$coordinate in the framework of a tree version of Rapidly-exploring Random Graph (RRT*) technique in path planner, called HSL-RRT*, which grows the path tree efficiently by the data from previous planning cycle. The velocity planner uses a Nonlinear Model Predictive Controller (NMPC) to generate optimal velocity along the path generated from the path planner, taking account of vehicle constraints and comfort. Analytic and simulation results are presented to validate the approach, with a special focus on the robustness and efficiency of the algorithm operating in complex scenarios.

This paper presents a novel, decentralized, control-theoretic approach to address collision avoidance for multi-robot systems. We create a virtual obstacle at the mean position of the robots. A control is be designed such that each robot will avoid the closest obstacle when a collision is possible. The closest obstacle can be the virtual obstacle or the nearest robot. We present two such control laws. The first assumes perfect knowledge of the velocities of all nearby robots and can allow a saturated velocity input for each robot. In practice, the velocities of the other robots are hard to measure or estimate precisely. Therefore, the second control law removes the assumption of known velocities based on a high-gain, robust control scheme. We prove the first control scheme is globally asymptotically stable, and the robust control law is globally uniformly ultimately bounded. To verify the effectiveness of the proposed approach, Monte Carlo simulations and experiments have been conducted.

In this paper, we propose a novel approach for obstacle avoidance. The configuration space of a nonholonomic mobile robot in the special Euclidean group SE(2). Therefore, we divide the whole configuration space into two sub-regions (i.e., a safe region and an obstacle avoidance region). We have derived two functions to define the robot's behavior in each region, which generate velocity vectors that drive the robot away from the obstacle and always towards the goal. We consider the existence of a switching surface between these two sub-regions, and specifically define this switching surface as a local subset of SE(2), i.e. the switching surface includes orientation, defined by the relative angle between the robot heading and bearing to the obstacle. Two different switching signals are proposed based on the switching surface. Lyapunov stability analysis proves the robot will converge to a goal position. To verify the effectiveness of the proposed approach, several simulations and experiments have been conducted.

In this paper, we present a novel camera-projector system for assisting robot-human interaction. The system is comprised of a stereo camera pair and a DLP projector. The proposed system provides feedback information indicating the robot's perception of the environment and what action a human user desires. Feedback is delivered by iteratively spotlighting objects in the environment using the projector. When the spotlight lands on an object that the human user wants the robot to retrieve, he or she can confirm the object selection, and the robot will perform a grasping task to retrieve the selected object. In this investigation, the proposed camera-projector performs three tasks: 1) Actively detect visually salient objects in the scene from the two camera views using a visual attention model. 2) Locate the detected objects using a trifocal tensor based object matching method, and project a spot of light on the objects to provide information back to the human user. 3) Reconstruct the 3D position of the target to plan robot motion and conduct vision-based control to move a robot manipulator to grasp the object. Experimental results of human-directed object grasping task are presented to demonstrate the functions of the proposed system.