Kaustubh Pathak

Velocity and position control of a wheeled inverted pendulum by partial feedback linearization

In this paper, the dynamic model of a wheeled inverted pendulum (e.g., Segway, Quasimoro, and Joe) is analyzed from a controllability and feedback linearizability point of view. First, a dynamic model of this underactuated system is derived with respect to the wheel motor torques as inputs while taking the nonholonomic no-slip constraints into considerations. This model is compared with the previous models derived for similar systems. The strong accessibility condition is checked and the maximum relative degree of the system is found. Based on this result, a partial feedback linearization of the system is obtained and the internal dynamics equations are isolated. The resulting equations are then used to design two novel controllers. The first one is a two-level velocity controller for tracking vehicle orientation and heading speed set-points, while controlling the vehicle pitch (pendulum angle from the vertical) within a specified range. The second controller is also a two-level controller which stabilizes the vehicles position to the desired point, while again keeping the pitch bounded between specified limits. Simulation results are provided to show the efficacy of the controllers using realistic data.

Fast Registration Based on Noisy Planes With Unknown Correspondences for 3-D Mapping

We present a robot-pose-registration algorithm, which is entirely based on large planar-surface patches extracted from point clouds sampled from a three-dimensional (3-D) sensor. This approach offers an alternative to the traditional point-to-point iterative-closest-point (ICP) algorithm, its point-to-plane variant, as well as newer grid-based algorithms, such as the 3-D normal distribution transform (NDT). The simpler case of known plane correspondences is tackled first by deriving expressions for least-squares pose estimation considering plane-parameter uncertainty computed during plane extraction. Closed-form expressions for covariances are also derived. To round-off the solution, we present a new algorithm, which is called minimally uncertain maximal consensus (MUMC), to determine the unknown plane correspondences by maximizing geometric consistency by minimizing the uncertainty volume in configuration space. Experimental results from three 3-D sensors, viz., Swiss-Ranger, University of South Florida Odetics Laser Detection and Ranging, and an actuated SICK S300, are given. The first two have low fields of view (FOV) and moderate ranges, while the third has a much bigger FOV and range. Experimental results show that this approach is not only more robust than point- or grid-based approaches in plane-rich environments, but it is also faster, requires significantly less memory, and offers a less-cluttered planar-patches-based visualization.

Fast plane detection and polygonalization in noisy 3D range images

A fast but nevertheless accurate approach for surface extraction from noisy 3D point clouds is presented. It consists of two parts, namely a plane fitting and a polygonalization step. Both exploit the sequential nature of 3D data acquisition on mobile robots in form of range images. For the plane fitting, this is used to revise the standard mathematical formulation to an incremental version, which allows a linear computation. For the polygonalization, the neighborhood relation in range images is exploited. Experiments are presented using a time-of-flight range camera in form of a Swissranger SR-3000. Results include lab scenes as well as data from two runs of the rescue robot league at the RoboCup German Open 2007 with 1,414, respectively 2,343 sensor snapshots. The 36ldr106, respectively 59ldr106 points from the two point clouds are reduced to about 14ldr103, respectively 23ldr103 planes with only about 0.2 sec of total computation time per snapshot while the robot moves along. Uncertainty analysis of the computed plane parameters is presented as well.

Approaches for a tether-guided landing of an autonomous helicopter

In this paper, we address the design of an autopilot for autonomous landing of a helicopter on a rocking ship, due to rough sea. A tether is used for landing and securing a helicopter to the deck of the ship in rough weather. A detailed nonlinear dynamic model for the helicopter is used. This model is underactuated, where the rotational motion couples into the translation. This property is used to design controllers which separate the time scales of rotation and translation. It is shown that the tether tension can be used to couple the translation of the helicopter to the rotation. Two controllers are proposed in this paper. In the first, the rotation time scale is chosen much shorter than the translation, and the rotation reference signals are created to achieve a desired controlled behavior of the translation. In the second, due to coupling of the translation of the helicopter to the rotation through the tether, the translation reference rates are created to achieve a desired controlled behavior of the attitude and altitude. Controller A is proposed for use when the helicopter is far away from the goal, while Controller B is for the case when the helicopter is close to the ship. The proposed control schemes are proved to be robust to the tracking error of its internal loop and results in local exponential stability. The performance of the control system is demonstrated by computer simulations. Currently, work is in progress to implement the algorithm using an instrumented model of a helicopter with a tether.

An integrated path-planning and control approach for nonholonomic unicycles using switched local potentials

In this paper, navigation and control of an autonomous mobile unicycle robot in an obstacle-ridden environment is considered. The unicycle dynamic model used has two differentially driven wheels, with the motor torques as the system input. Two novel potential-field-based controllers are derived, which stabilize the robot within a surrounding circular area (henceforth called a bubble) of arbitrary size. The first controller takes the unicycle to the center of its bubble, while the second corrects its orientation. The designed potentials also work with a kinematic model. Explicit bounds for permissible initial speeds are derived, such that maximum torque limits and/or maximum speed limits are not violated once the controller is activated. These controllers are then embedded in a navigation framework. An existing global planner is used to first create a string of variable-sized bubbles which connect the start point to the goal point, with each bubbles size indicative of the radial obstacle clearance available from its center. The robot then keeps itself within a fixed-sized bubble, which it then moves in discrete steps, according to the direction provided by the global plan, while repulsively avoiding unexpected obstacles. Hence, the gross movement is created by switching local potential-field-based controllers. This scheme is first verified in computer simulation of a single robot moving in a maze. It is then implemented on an experimental setup of robots equipped with proximity sensors. Results are presented to illustrate the effectiveness of the system.

Fast 3D mapping by matching planes extracted from range sensor point-clouds

This article addresses fast 3D mapping by a mobile robot in a predominantly planar environment. It is based on a novel pose registration algorithm based entirely on matching features composed of plane-segments extracted from point-clouds sampled from a 3D sensor. The approach has advantages in terms of robustness, speed and storage as compared to the voxel based approaches. Unlike previous approaches, the uncertainty in plane parameters is utilized to compute the uncertainty in the pose computed by scan-registration. The algorithm is illustrated by creating a full 3D model of a multi-level robot testing arena.

3D forward sensor modeling and application to occupancy grid based sensor fusion

This paper presents a new technique for the update of a probabilistic spatial occupancy grid map using a forward sensor model. Unlike currently popular inverse sensor models, forward sensor models can be found experimentally and can represent sensor characteristics better. The formulation is applicable to both 2D and 3D range sensors and does not have some of the theoretical and practical problems associated with the current approaches which use forward models. As an illustration of this procedure, a new prototype 3D forward sensor model is derived using a beam represented as a spherical sector. Furthermore, this model is used for fusion of point-clouds obtained from different 3D sensors, in particular, time-of-flight sensors (Swiss-ranger, laser range finders), and stereo vision cameras. Several techniques are described for an efficient data-structure representation and implementation. The range beams from different sensors are fused in a common local Cartesian occupancy map. Experimental results of this fusion are presented and evaluated using Hough-transform performed on the grid.

Hough based Terrain Classification for Realtime Detection of Drivable Ground

The usability of mobile robots for surveillance, search and rescue missions can be significantly improved by intelligent functionalities decreasing the cognitive load on the operator or even allowing autonomous operations, e.g., when communication fails. Mobility in this regard is not only a mechatronic problem but also a perception, modeling and planning challenge. Here, the perception issue of detecting drivable ground is addressed, an important issue for safety, security, and rescue robots, which have to operate in a vast range of unstructured, challenging environments. The simple yet efficient approach is based on the Hough transform of planes. The idea is to design the parameter space such that drivable surfaces can be easily detected by the number of hits in the bins corresponding to drivability. A decision tree on the bin properties increases robustness as it allows to handle uncertainties, especially sensor noise. In addition to the binary distinction of drivable/non-drivable ground, a classification of terrain types is possible. The algorithm is applied to 3D data obtained from two different sensors, namely, a time-of-flight camera and a stereo camera. Experimental results are presented for indoor and outdoor terrains, demonstrating robust realtime detection of drivable ground. Seven datasets recorded under very varying conditions are used. About 6,800 snapshots of range data are processed in total. It is shown that drivability can be robustly detected with success rates ranging between 83% and 100%. Computation is extremely fast in the order of 5 to 50 msec.

Velocity control of a wheeled inverted pendulum by partial feedback linearization

In this paper, the dynamic model of a wheeled inverted pendulum (e.g. Segway (2003), Quasimoro (Salerno and Angeles, 2003), Joe (Grasser et al., 2002)) is analyzed from a controllability and feedback linearizability point of view. First, a dynamic model of this underactuated system is derived with respect to the wheel motor torques as inputs while taking the nonholonomic no-slip constraints into considerations. This model is compared with the previous models derived for similar systems. The strong accessibility condition is checked and the maximum relative degree of the system is found. Based on this result, a partial feedback linearization of the system is obtained and the internal dynamics equations are isolated. The resulting equations are then used to design a two-level controller for tracking vehicle orientation and heading speed set-points, while controlling the vehicle pitch within a specified range. Simulation results are provided to show the efficacy of the controller.

A networking framework for teleoperation in safety, security, and rescue robotics

Safety, security, and rescue robotics is an important application field that can be viewed as a prototypical example of a domain where networked mobile robots are used for the exploration of unstructured environments that are inaccessible to or dangerous for humans. Teleoperation, based on wireless networks, is much more complex than what one might expect at first glance because it goes well beyond mere mappings of low-level user inputs - like joystick commands - to motor activations on a robot. Teleoperation for SSRR must move up to the behavior and mission levels where a single operator triggers short-time, autonomous behaviors, respectively, and supervises a whole team of autonomously operating robots. Consequently, a significant amount of heterogeneous data - video, maps, goal points, victim data, and so on - must be transmitted between robots and mission control. In this article, a networking framework for teleoperation in SSRR is presented. It was evaluated in a series of field tests and competitions, including the European Land Robot Trials and RoboCup events.