Randolph Charles Voorhies

An autonomous manipulation system based on force control and optimization

In this paper we present an architecture for autonomous manipulation. Our approach is based on the belief that contact interactions during manipulation should be exploited to improve dexterity and that optimizing motion plans is useful to create more robust and repeatable manipulation behaviors. We therefore propose an architecture where state of the art force/torque control and optimization-based motion planning are the core components of the system. We give a detailed description of the modules that constitute the complete system and discuss the challenges inherent to creating such a system. We present experimental results for several grasping and manipulation tasks to demonstrate the performance and robustness of our approach.

Finding planes in LiDAR point clouds for real-time registration

We present a robust plane finding algorithm that when combined with plane-based frame-to-frame registration gives accurate real-time pose estimation. Our plane extraction is capable of handling large and sparse datasets such as those generated from spinning multi-laser sensors such as the Velodyne HDL-32E LiDAR. We test our algorithm on frame-to-frame registration in a closed-loop indoor path comprising 827 successive 3D laser scans (over 57 million points), using no additional information (e.g., odometry, IMU). Our algorithm outperforms, in both accuracy and time, three state-of-the-art methods, based on iterative closest point (ICP), plane-based randomized Hough transform, and planar region growing.

Beobot 2.0: Cluster architecture for mobile robotics

With the recent proliferation of robust but computationally demanding robotic algorithms, there is now a need for a mobile robot platform equipped with powerful computing facilities. In this paper, we present the design and implementation of Beobot 2.0, an affordable research-level mobile robot equipped with a cluster of 16 2.2-GHz processing cores. Beobot 2.0 uses compact Computer on Module (COM) processors with modest power requirements, thus accommodating various robot design constraints while still satisfying the requirement for computationally intensive algorithms. We discuss issues involved in utilizing multiple COM Express modules on a mobile platform, such as interprocessor communication, power consumption, cooling, and protection from shocks, vibrations, and other environmental hazards such as dust and moisture. We have applied Beobot 2.0 to the following computationally demanding tasks: laser-based robot navigation, scale-invariant feature transform (SIFT) object recognition, finding objects in a cluttered scene using visual saliency, and vision-based localization, wherein the robot has to identify landmarks from a large database of images in a timely manner. For the last task, we tested the localization system in three large-scale outdoor environments, which provide 3,583, 6,006, and 8,823 test frames, respectively. The localization errors for the three environments were 1.26, 2.38, and 4.08 m, respectively. The per-frame processing times were 421.45, 794.31, and 884.74 ms respectively, representing speedup factors of 2.80, 3.00, and 3.58 when compared to a single dual-core computer performing localization.

Centralized server environment for educational robotics

One of the main challenges when creating an undergraduate introduction to robotics course is connecting the theory taught in the lectures with the current practices of research. The primary cause of this difficulty is an inability to find a hardware solution that is powerful enough to run complex cutting-edge algorithms yet inexpensive enough to be purchased by an undergraduate class budget. An ideal system needs to have a gentle learning curve to allow students with minimal background in the field to get a robot up and running. Lastly, a fleet of classroom robots needs to be easy to administrate and maintain given the limited time of a Teaching Assistant. Our approach is to implement a centralized server system. In this system individual robots are inexpensive yet capable of establishing a WiFi link to a main server so that all the compilation and system administration, as well as much of the computationally intensive processing, are done on that server. We find that this solution saves both time and money and provides an effective teaching tool. This paper describes the hardware and software architecture of the system, and example applications implemented by undergraduate students.