Position-based monocular visual servoing of an unknown target using online self-supervised learning

Visual servoing, i.e. control with visual information, is a valuable capability in many robotic applications. In particular, position based visual servoing (PBVS) estimates position information from the observed image to generate visual servo control. However, the estimation of the position of an unknown target using monocular images is still difficult due to the complexity of the image information. For the target estimation problem, we propose to integrate three complementary techniques for monocular visual servoing. First, to estimate the probability of a target's existence, the learning model with spatial features from convolution neural network is proposed. Second, the extended Kalman filter based on epipolar geometry estimates the 3D position of the target; moreover, from this 3D position, the perception model is trained online by self-generated virtual ground-truth. Finally, visual servo control is generated, and the resulting movement helps to construct epipolar geometry. Finally. the experimental validation is performed in a challenging setting involving occlusion and target's shape change.N

Model Predictive Control for an Aerial Manipulator Opening a Hinged Door

Aerial manipulation has been widely studied to be employed in various tasks such as exploration and transportation. To incorporate aerial manipulation into more sophisticated tasks like pulling or pushing a heavy cargo, an active interaction with surrounding structures should be considered. Unlike physical contact with a static structure which was mainly studied in previous papers, interaction with a movable structure requires a consideration of dynamics of the structure which makes the scenario more complex. In this paper, an aerial manipulator opening a hinged door is presented. Coupled dynamics between an aerial manipulator and a hinged door is derived, and a model predictive control (MPC) algorithm using iterative Linear Quadratic Regulator (iLQR) method for the derived dynamic equation is proposed. Through our proposed control strategy, sub-optimal state and input trajectories robust to model uncertainties while satisfying input constraints are generated. Our dynamic model and control algorithm are validated through simulations.N

Sampling-based Motion Planning for Aerial Pick-and-Place

This paper presents a motion planning approach for an aerial pick-and-place task where an aerial manipulator is supposed to pick up or place an object at locations specified as waypoints. In particular, we focus on situations where such way-point constraints are imposed on certain partial state variables, rather than on full state variables. Our proposed framework, based on rapidly exploring random trees star (RRT*) in a bidirectional manner, enables an aerial manipulator to find an optimal trajectory that satisfies waypoint constraints with only partial specifications. Here, we suggest an extra merging process to integrate the trees, each originated from the start and goal point. In the merging process, we search various candidate points satisfying a given condition that partially constrains state variables, and select a waypoint with full specifications optimal in the perspective of the entire trajectory. Simulation and experiment results are included to validate the proposed framework.N

Establishment of a Simple and Rapid Gene Delivery System for Cucurbits by Using Engineered of Zucchini yellow mosaic virus

The infectious full-length cDNA clone of zucchini yellow mosaic virus (ZYMV) isolate PA (pZYMV-PA), which was isolated from pumpkin, was constructed by utilizing viral transcription and processing signals to produce infectious in vivo transcripts. Simple rub-inoculation of plasmid DNAs of pZYMV-PA was successful to cause infection of zucchini plants (Cucurbita pepo L.). We further engineered this infectious cDNA clone of ZYMV as a viral vector for systemic expression of heterologous proteins in cucurbits. We successfully expressed two reporter genes including gfp and bar in zucchini plants by simple rub-inoculation of plasmid DNAs of the ZYMV-based expression constructs. Our method of the ZYMV-based viral vector in association with the simple rub-inoculation provides an easy and rapid approach for introduction and evaluation of heterologous genes in cucurbits

Robust Trajectory Planning for a Multirotor against Disturbance based on Hamilton-Jacobi Reachability Analysis

Ensuring safety in trajectory planning of multirotor systems is an essential element for risk-free operation. Even if the generated trajectory is known to be safe in the planning phase, unknown disturbance during an actual operation can lead to a dangerous situation. This paper proposes safety-guaranteed receding horizon planning against unknown, but bounded, disturbances. We first characterize forward reachable set (FRS) of the system, the set of states after a certain duration considering all possible disturbances, using Hamilton-Jacobi (HJ) reachability analysis. To compute the FRSs in real-time, we conservatively approximate the true FRS and perform ellipsoidal parameterization on the FRSs. Using the FRSs, we can plan a robust trajectory that avoids risky regions and rapidly re-plan the trajectory when the system encounters sudden disturbance. The proposed method is validated through an experiment of avoiding obstacles in a wind.N

Real-time Optimal Planning and Model Predictive Control of a Multi-rotor with a Suspended Load

This paper presents planning and control algorithms for a multi-rotor with a suspended load. The suspended load cannot be controlled easily by the multi-rotor due to severe dynamic coupling between them. Difficulties are exacerbated by under-actuated, highly nonlinear nature of multi-rotor dynamics. Although many studies have been proposed to plan trajectories and control this system, there exist only a few reports on real-time trajectory generation. With this in mind, we propose a planning method which is capable of generating collision-free trajectories real-time and applicable to a high-dimensional nonlinear system. Using a differential flatness property, the system can be linearized entirely with elaborately chosen flat outputs. Convexification of non-convex constraints is carried out, and concave obstacle-avoidance constraints are converted to convex ones. After that, a convex optimization problem is solved to generate an optimal trajectory, but semi-feasible trajectory which considers only some parts of the initial state. We apply model predictive control with a sequential linear quadratic solver to compute a feasible collision-free trajectory and to control the system. Performance of the algorithm is validated by flight experiment.N