TechTalks from event: Technical session talks from ICRA 2012

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Estimation and Control for UAVs

  • Autonomous Indoor 3D Exploration with a Micro-Aerial Vehicle Authors: Shen, Shaojie; Michael, Nathan; Kumar, Vijay
    In this paper, we propose a stochastic differential equation-based exploration algorithm to enable exploration in three-dimensional indoor environments with a payload constrained micro-aerial vehicle (MAV). We are able to address computation, memory, and sensor limitations by considering only the known occupied space in the current map. We determine regions for further exploration based on the evolution of a stochastic differential equation that simulates the expansion of a particle system with Langevin dynamics. The regions of most significant particle expansion correlate to unexplored space. After identifying and processing these regions, the autonomous MAV navigates to these locations to enable fully autonomous exploration. The performance of the approach is demonstrated through numerical simulations and experimental results in single- and multi-floor indoor experiments.
  • Wind Field Estimation for Autonomous Dynamic Soaring Authors: Langelaan, Jack W.; Spletzer, John; Montella, Corey; Grenestedt, Joachim
    A method for distributed parameter estimation of a previously unknown wind field is described. The application is dynamic soaring for small unmanned air vehicles, which severely constrains available computing while simultaneously requiring updates that are fast compared with a typical dynamic soaring cycle. A polynomial parameterization of the wind field is used, allowing implementation of a linear Kalman filter for parameter estimation. Results of Monte Carlo simulations show the effectiveness of the approach. In addition, in-flight measurements of wind speeds are compared with data obtained from video tracking of balloon launches to assess the accuracy of wind field estimates obtained using commercial autopilot modules.
  • Decentralized Formation Control with Variable Shapes for Aerial Robots Authors: Turpin, Matthew; Michael, Nathan; Kumar, Vijay
    We address formation control for a team of quadrotors in which the robots follow a specified group trajectory while safely changing the shape of the formation according to specifications. The formation is prescribed by shape vectors which dictate the relative separations and bearings between the robots, while the group trajectory is specified as the desired trajectory of a leader or a virtual robot in the group. Each robot plans its trajectory independently based on its local information of neighboring robots which includes both the neighbor's planned trajectory and an estimate of its state. We show that the decentralized trajectory planners (a) result in consensus on the planned trajectory for predefined shapes and (b) achieve safe reconfiguration when changing shapes.
  • Versatile Distributed Pose Estimation and Sensor Self-Calibration for an Autonomous MAV Authors: Weiss, Stephan; Achtelik, Markus W.; Chli, Margarita; Siegwart, Roland
    In this paper, we present a versatile framework to enable autonomous flights of a Micro Aerial Vehicle (MAV) which has only slow, noisy, delayed and possibly arbitrar- ily scaled measurements available. Using such measurements directly for position control would be practically impossible as MAVs exhibit great agility in motion. In addition, these measurements often come from a selection of different onboard sensors, hence accurate calibration is crucial to the robustness of the estimation processes. Here, we address these problems using an EKF formulation which fuses these measurements with inertial sensors. Compared to existing approaches we do not only estimate pose and velocity of the MAV, but also states such as sensor biases, scale of the position estimate and self (inter- sensor) calibration in real-time. Furthermore, we show that it is possible to obtain a yaw estimate from position measurements only. We demonstrate that the proposed framework is capable of running entirely onboard a MAV boosting its autonomy, performing state prediction at the rate of 1 kHz. Our results illustrate that this approach is able to handle measurement delays (up to 500ms), noise (std. deviation up to 20 cm) and slow update rates (as low as 1 Hz) while dynamic maneuvers are still possible. We present a detailed quantitative performance evaluation of the real system under the influence of different disturbance parameters and different sensor setups to highlight the versatility of our approach
  • Probabilistic Velocity Estimation for Autonomous Miniature Airships Using Thermal Air Flow Sensors Authors: Mueller, Joerg; Paul, Oliver; Burgard, Wolfram
    Recently, autonomous miniature airships have become a growing research field. Whereas airships are attractive as they can move freely in the three-dimensional space, their high-dimensional state space and the restriction to small and lightweight sensors are demanding constraints with respect to self-localization. Furthermore, their complex second-order kinematics makes the estimation of their pose and velocity through dead reckoning odometry difficult and imprecise. In this paper, we consider the problem of estimating the velocity of a miniature blimp with lightweight air flow sensors. We present a probabilistic sensor model that accurately models the uncertainty of the flow sensors and thus allows for robust state estimation using a particle filter. In experiments carried out with a real airship we demonstrate that our method precisely estimates the velocity of the blimp and outperforms the standard velocity estimates of the motion model as applied in many existent autonomous blimp navigation systems.
  • Efficient Human-Like Walking for the COmpliant Humanoid COMAN Based on Kinematic Motion Primitives (kMPs) Authors: Moro, Federico Lorenzo; Tsagarakis, Nikolaos; Caldwell, Darwin G.
    Research in humanoid robotics in recent years has led to significant advances in terms of the ability to walk and even run. Yet, despite the general achievements in locomotion and control, energy efficiency is still one important area that requires further attention, especially as it is one of the major steeping stones leading to increased autonomy. This paper examines, and quantifies, the energetic benefits of introducing passive compliance into bipedal locomotion using COMAN, an intrinsically COmpliant huMANoid robot. The novelty of the method proposed consists of: i) the use of a method of gait synthesis based on kinematic Motion Primitives (kMPs) extracted from human, ii) the frequency tuning of the resultant trajectories, to excite the physical elasticity of the system, and the subsequent analysis of the energetic performance of the robot. The motivation is to assess the possible effects of using dynamic human-like, and human derived, trajectories, with significant Center of Mass (CoM) vertical displacement, regulated in frequency around the frequency band of the system resonances, on the excitation of the compliant actuators, and subsequently to measure and verify any energetic benefit. Experimental results show that if the gait frequency is close to one of the main resonant frequencies of the robot, then the total work contribution of the elastic compliant element to the overall motion of the robot is positive (15% of the work required is generated by the springs).
  • State Estimation for Aggressive Flight in GPS-Denied Environments Using Onboard Sensing Authors: Bry, Adam (Massachusetts Institute of Technology), Bachrach, Abraham (Massachusetts Institute of Technology,), Roy, Nicholas (Massachusetts Institute of Technology)
    In this paper we present a state estimation method based on an inertial measurement unit (IMU) and a planar laser range finder suitable for use in real-time on a fixed-wing micro air vehicle (MAV). The algorithm is capable of maintaing accurate state estimates during aggressive flight in unstructured 3D environments without the use of an external positioning system. Our localization algorithm is based on an extension of the Gaussian Particle Filter. We partition the state according to measurement independence relationships and then calculate a pseudo-linear update which allows us to use 20x fewer particles than a naive implementation to achieve similar accuracy in the state estimate. We also propose a multi-step forward fitting method to identify the noise parameters of the IMU and compare results with and without accurate position measurements. Our process and measurement models integrate naturally with an exponential coordinates representation of the attitude uncertainty. We demonstrate our algorithms experimentally on a fixed-wing vehicle flying in a challenging indoor environment.