Kinematics Based Visual Localization for Skid-Steering Robots: Algorithm and Theory

To build commercial robots, skid-steering mechanical design is of increased popularity due to its manufacturing simplicity and unique mechanism. However, these also cause significant challenges on software and algorithm design, especially for pose estimation (i.e., determining the robot's rotation and position), which is the prerequisite of autonomous navigation. While the general localization algorithms have been extensively studied in research communities, there are still fundamental problems that need to be resolved for localizing skid-steering robots that change their orientation with a skid. To tackle this problem, we propose a probabilistic sliding-window estimator dedicated to skid-steering robots, using measurements from a monocular camera, the wheel encoders, and optionally an inertial measurement unit (IMU). Specifically, we explicitly model the kinematics of skid-steering robots by both track instantaneous centers of rotation (ICRs) and correction factors, which are capable of compensating for the complexity of track-to-terrain interaction, the imperfectness of mechanical design, terrain conditions and smoothness, and so on. To prevent performance reduction in robots' lifelong missions, the time- and location- varying kinematic parameters are estimated online along with pose estimation states in a tightly-coupled manner. More importantly, we conduct in-depth observability analysis for different sensors and design configurations in this paper, which provides us with theoretical tools in making the correct choice when building real commercial robots. In our experiments, we validate the proposed method by both simulation tests and real-world experiments, which demonstrate that our method outperforms competing methods by wide margins.Comment: 18 pages in tota

Control of Outdoor Robots at Higher Speeds on Challenging Terrain

This thesis studies the motion control of wheeled mobile robots. Its focus is set on high speed control on challenging terrain. Additionally, it deals with the general problem of path following, as well as path planning and obstacle avoidance in difficult conditions. First, it proposes a heuristic longitudinal control for any wheeled mobile robot, and evaluates it on different kinematic configurations and in different conditions, including laboratory experiments and participation in a robotic competition. Being the focus of the thesis, high speed control on uneven terrain is thoroughly studied, and a novel control law is proposed, based on a new model representation of skid-steered vehicles, and comprising of nonlinear lateral and longitudinal control. The lateral control part is based on the Lyapunov theory, and the convergence of the vehicle to the geometric reference path is proven. The longitudinal control is designed for high speeds, taking actuator saturation and the vehicle properties into account. The complete solution is experimentally tested on two different vehicles on several different terrain types, reaching the speeds of ca. 6 m/s, and compared against two state-of-the-art algorithms. Furthermore, a novel path planning and obstacle avoidance system is proposed, together with an extension of the proposed high speed control, which builds up a navigation system capable of autonomous outdoor person following. This system is experimentally compared against two classical obstacle avoidance methods, and evaluated by following a human jogger in outdoor environments, with both static and dynamic obstacles. All the proposed methods, together with various different state-of-the-art control approaches, are unified into one framework. The proposed framework can be used to control any wheeled mobile robot, both indoors and outdoors, at low or high speeds, avoiding all the obstacles on the way. The entire work is released as open-source software