IROS 2015 Papers

The purpose of this research is the development of a soft skin module with a built-in airtight cavity in which air pressure can be sensed. A pressure feedback controller is implemented on a robotic system using this module for contact sensing and gentle grasping. The soft skin module is designed to meet size and safety criteria appropriate for a toy-sized interactive robot. All module prototypes are produced using a muti-material 3D printer. Experimental results from collision tests show that this module significantly reduces the impact forces due to collision. Also, using the measured pressure information from the module, the robotic system to which these modules are attached is capable of very gentle physical interaction with soft objects.

In indoor spaces, plane features are general geometric primitives that are robust to noise. To estimate the relative displacement and orientation between two consecutive 3D poses, at least three-pair correspondences of planes with different normals are needed at both poses. Otherwise, the estimated state falls into degeneracy, which is usually caused by the narrow field-of-view (FoV) of sensors as well as environmental structures. In the case where there are changes in degeneracy, with the exception of rotation, the state can be resolved using existing methods. To compensate for rotation in degeneracy for one-pair correspondences, the main approach is to project the initial prediction of an inertial measurement unit's (IMU) orientation to the normal of the corresponding plane such that it satisfies the shortest arc length. We also propose an algorithm that detects various degeneracy cases through which a seamless implementation becomes feasible, regardless of the number of plane pairs. We show the usefulness of the proposed method by performing a 3D mapping experiment where several degenerate cases arise due to limited FoV.

Path planning is a fundamental task for any kind of autonomous mobile robot. In this paper, we present a motion planning technique on the three-dimensional space considering vehicles with spatial curvature and pitch (climb or dive) angle constraints. Concerning real fixed-wing Unmanned Aerial Vehicles or underwater Remotely Operated Vehicles, we face the problem of calculating paths with continuous acceleration profiles, preventing the robot from abrupt turning or climbing/diving rates during its navigation. We also focus on the generation of paths with reduced length, allowing energy savings and minimizing the time for the task fulfillments. The proposed methodology provides, in a fast and iterative way, near-optimal paths in some scenarios, but with the advantage of continuous curvature and pitch rate profiles, a fundamental issue regarding the dynamics of real-world robots. The methodology was validated through numerous trials, under different scenarios in a simulated environment, and the results compared to the ones obtained by state-of-the-art techniques, providing a thorough evaluation.

Modern plant phenotyping requires active sensing technologies and particular exploration strategies. This article proposes a new method for actively exploring a 3D region of space with the aim of localizing special areas of interest for manipulation tasks over plants. In our method, exploration is guided by a multi-layer occupancy grid map. This map, together with a multiple-view estimator and a maximum-information-gain gathering approach, incrementally provides a better understanding of the scene until a task termination criterion is reached. This approach is designed to be applicable for any task entailing 3D object exploration where some previous knowledge of its general shape is available. Its suitability is demonstrated here for an eye-in-hand arm configuration in a leaf probing application.

We propose a new method for obtaining object candidates in 3D space. Our method requires no learning, has no limitation of object properties such as compactness or symmetry, and therefore produces object candidates using a completely general approach. This method is a simple combination of Selective Search, which is a non-learning-based objectness detector working in 2D images, and a supervoxel segmentation method, which works with 3D point clouds. We made a small but non-trivial modification to supervoxel segmentation; it brings better “seeding” for supervoxels, which produces more proper object candidates as a result. Our experiments using a couple of publicly available RGB-D datasets demonstrated that our method outperformed state-of-the-art methods of generating object proposals in 2D images.

In this paper, we present an efficient 3-DOF registration method to align two overlapping point clouds captured by a range sensor that experiences locally planar motion such as in many robotics applications. The algorithm follows the RANSAC framework and is based on finding a pair of congruent triangles in the source and target clouds. With the assumption of planar sensor motion, corresponding vertices of the congruent triangles must have similar elevation values, allowing our algorithm to identify them efficiently. Specifically, given a triangle in the source cloud, our algorithm first finds two vertices of a candidate congruent triangle in the target cloud and then uses the third vertex to verify, minimizing the complexity of the geometric base as well as the expected time of sampling successfully a matching congruent triangle. To improve the performance of our algorithm further, our verification of a hypothetical alignment transformation proceeds first locally by using only the points near the vertices of the congruent triangles before involving the entire cloud. Our experimental results show that the proposed algorithm outperforms state-of-the-art registration algorithms in the case of 3-DOF planar sensor motion.

Exploration of unknown environments is an important aspect to fielding teams of robots. Without the ability to determine on their own where to go in the environment, the full potential of robotic teams is limited to the abilities of human operators to deploy them for search and rescue, mapping, or other tasks that are predicated on gaining knowledge from the environment. This is of particular importance in real-world 3-Dimensional (3-D) environments where simple planar assumptions can lead to incomplete exploration, for example, real-world environments have areas underneath overhangs or inside caves. As an additional challenge, when the teams of robots have vastly different capabilities, the planning system must take those into account to efficiently utilize the available assets. In this paper, we present a combined air-ground system for conducting 3-D exploration in cluttered environments. We first describe the hardware and software components of the system. We then present our algorithm for planning 3-D goal locations for a heterogeneous team of robots to efficiently explore a previously unknown environment and demonstrate its applicability in real-world experiments.

We introduce a novel approach to simultaneous localization and mapping for robots equipped with a 2D laser scanner. In particular, we propose a fast scan registration algorithm that operates on 2D maps represented as a signed distance function (SDF). Using SDFs as a map representation has several advantages over existing approaches: while classical 2D scan matchers employ brute-force matching to track the position of the robot, signed distance functions are differentiable on large parts of the map. Consequently, efficient minimization techniques such as Gauss-Newton can be applied to find the minimum. In contrast to occupancy grid maps, the environment can be captured with sub-grid cell size precision, which leads to a higher localization accuracy. Furthermore, SDF maps can be triangulated to polygon maps for efficient storage and transfer. In a series of experiments, conducted both in simulation and on a real physical platform, we demonstrate that SDF tracking is more accurate and efficient than previous approaches. We outperform scan matching on occupancy maps in simulation by ~270% in terms of root mean squared deviation (RMSD) with a ~63% lower standard deviation. In the real robot experiments, we obtain a performance advantage of ~14% RMSD with a ~25% lower standard deviation.

This paper presents a mechanism for the robot moving inside of pipelines. The mechanism is composed of the 2-2D differential gear mechanism, the active adhesion mechanism, and the rescue mechanism. The 2-2D differential gear mechanism is designed to be driven with single actuator at any elbow type pipelines without any additional control efforts. The active adhesion mechanism is for providing sufficient adhesion forces of the robot on climbing over the pipelines against the gravity. In addition the rescue mechanism is designed for retrieving the robot safely in emergency situation. In this paper, three proposed mechanisms are explained in details and their effectiveness is experimentally demonstrated.