Evgeni Magid

A comparison of Gaussian and mean curvature estimation methods on triangular meshes of range image data

Estimating intrinsic geometric properties of a surface from a polygonal mesh obtained from range data is an important stage of numerous algorithms in computer and robot vision, computer graphics, geometric modeling, and industrial and biomedical engineering. This work considers different computational schemes for local estimation of intrinsic curvature geometric properties. Four different algorithms and their modifications were tested on triangular meshes that represent tessellations of synthetic geometric models. The results were compared with the analytically computed values of the Gaussian and mean curvatures of the non-uniform rational B-spline (NURBS) surfaces from which these meshes originated. The algorithms were also tested on range images of geometric objects. The results were compared with the analytic values of the Gaussian and mean curvatures of the scanned geometric objects. This work manifests the best algorithms suited for Gaussian and mean curvature estimation, and shows that different algorithms should be employed to compute the Gaussian and mean curvatures.

Spline-Based Robot Navigation

This paper offers a path planning algorithm based on splines. The sought path avoids the obstacles, and is smooth and short. Smoothing is used as an integral part of the algorithm, and not only as a final improvement to a path found by other methods. In order to avoid a very difficult optimization over all the paths points, it is modeled by a sequence of splines defined by a gradually increasing number of knots

CautiousBug: a competitive algorithm for sensory-based robot navigation

Bug algorithms are a class of popular algorithms for autonomous robot navigation in unknown environments with local information. Very natural, with low memory requirements, Bug strategies do not yet allow any competitive analysis. The bound on the robots path changes from scene to scene depending on the obstacles, even though a new obstacle may not alter the length of the shortest path. We propose a new competitive algorithm, CautiousBug, whose competitive factor has an order of O(d/sup m-1/), where d is the length of the optimal path from starting point S to a target point T. m = 2/sup #Min-1/ and #Min denote the number of the distance function isolated local minima points in the given environment. Simulations were performed to study the average competitive factor of the algorithm.

Controlled balance losing in random step environment for path planning of a teleoperated crawler-type vehicle

Rescue robotics is the application of robotics to the search and rescue domain, aimed at extending the capabilities and increasing the safety of the rescuers. Deployed on a site during a rescue mission, a mobile robot is teleoperated by a human operator from a safe place. To suggest to the operator a good direction to traverse the three-dimensional (3D) debris environment, we develop a pilot system, which requires a special path search algorithm on debris and a proper definition of a search tree. Although the main goal of the algorithm is to keep the robot maximally stable at every step of its path, in some cases we need the robot to change a 3D orientation discontinuously through losing its balance. Losing balance on purpose is an essential feature for safe climbing up and going down debris, and it is the central issue of this paper. Exhaustive simulations were used to structure and analyze data. Experiments with a real robot verified our approach to removing unsuitable search directions from the search tree and gave important feedback to the algorithm.

Toward a Human-like Locomotion: Modelling Dynamically Stable Locomotion of an Anthropomorphic Robot in Simulink Environment

In the near future anthropomorphic robots will turn into an important part of our everyday routine. To successfully perform various tasks these robots require stable walking control algorithms, which could guarantee dynamic balance of the biped robot locomotion. Our research is focused on the development of locomotion algorithms which could provide effective anthropomorphic walking of a robot. As a target robotic platform we utilize an experimental model of a human-size robot - a novel Russian robot AR-601M. In this paper we introduce AR-601M robot and present a model of a biped robot with 11 DoF which simulates a simplified AR-601M robot. The simulation model is implemented in Matlab/Simulink environment and uses walking primitives in order to provide a dynamically stable locomotion.

ROS-Based SLAM for a Gazebo-Simulated Mobile Robot in Image-Based 3D Model of Indoor Environment

Nowadays robot simulators have robust physics engines, high-quality graphics, and convenient interfaces, affording researchers to substitute physical systems with their simulation models in order to pre-estimate the performance of theoretical findings before applying them to real robots. This paper describes Gazebo simulation approach to simultaneous localization and mapping SLAM based on Robot Operating System ROS using PR2 robot. The ROS-based SLAM approach applies Rao-Blackwellized particle filters and laser data to locate the PR2 robot in unknown environment and build a map. The real room 3D model was obtained from camera shots and reconstructed with Autodesk 123D Catch and MeshLab software. The results demonstrate the fidelity of the simulated 3D room to the obtained from the robot laser system ROS-calculated map and the feasibility of ROS-based SLAM with a Gazebo-simulated mobile robot to its usage in camera-based 3D environment. This approach will be further extended to ROS-based robotic simulations in Gazebo with a Russian anthropomorphic robot AR-601M.

Comparative analysis of ROS-based monocular SLAM methods for indoor navigation

This paper presents a comparison of four most recent ROS-based monocular SLAM-related methods: ORB-SLAM, REMODE, LSD-SLAM, and DPPTAM, and analyzes their feasibility for a mobile robot application in indoor environment. We tested these methods using video data that was recorded from a conventional wide-angle full HD webcam with a rolling shutter. The camera was mounted on a human-operated prototype of an unmanned ground vehicle, which followed a closed-loop trajectory. Both feature-based methods (ORB-SLAM, REMODE) and direct SLAMrelated algorithms (LSD-SLAM, DPPTAM) demonstrated reasonably good results in detection of volumetric objects, corners, obstacles and other local features. However, we met difficulties with recovering typical for offices homogeneously colored walls, since all of these methods created empty spaces in a reconstructed sparse 3D scene. This may cause collisions of an autonomously guided robot with unfeatured walls and thus limits applicability of maps, which are obtained by the considered monocular SLAM-related methods for indoor robot navigation.

Static balance for rescue robot navigation: Losing balance on purpose within random step environment

The goal of rescue robotics is to extend the capabilities and to increase the safety of human rescue teams. During a rescue mission a mobile robot is deployed on a rescue site and is operated from a safe place by a human operator. The operator can not see the robot and the environment and a decision on the path selection is very complicated. Our goal is to provide a kind of automatic “pilot system” to propose an operator a good direction or several options to traverse the environment, taking into an account the robots static and dynamic properties. To find a good path we need a special path search algorithm on debris and a proper definition of a search tree, which can ensure smooth exploration. While the main goal of the algorithm is to keep the robot maximally stable at every step of its path, in some cases we need the robot to lose its balance and to change a 3D orientation discontinuously. Losing balance on purpose is an important feature for safe climbing up and going down the debris and it is the central issue of this paper. Exhaustive simulations were used to structure and analyze data and experiments were used to verify our approach to removing unsuitable directions of the search from the search tree.

3D Modelling of Biped Robot Locomotion with Walking Primitives Approach in Simulink Environment

In the past decades bipedal robots related research gained significant attention as the technology progresses towards acceptable humanoid robot assistants. Serious challenges of human-like biped robot locomotion include such issues as obtaining a human gait multi-functionality, energy efficiency and flexibility. In this paper we present Russian biped robot AR-601M and its locomotion modelling in Simulink environment using walking primitives approach. We consider two robot models: with 6 and 12 Degrees of Freedom (DoFs) per legs, using the same walking strategies. While the 6-DoF model is constrained to move only in sagittal plan, the 12-DoF model supports 3D motion and precisely reflects the hardware of AR-601M robot legs. The locomotion algorithm utilizes position control and involves inverse kinematics computations for the joints. The resulting simulation of robot locomotion is dynamically stable for both models at a small step length and short step time with relatively long damping pauses between the steps.

Toward a human-like biped robot gait: Biomechanical analysis of human locomotion recorded by Kinect-based Motion Capture system

This paper presents biomechanical analysis of human locomotion recorded by Motion Capture (MoCap) system based on four Kinect 2 sensors and iPi Soft markerless tracking and visualization technology. To analyze multi-depth sensor video recordings we utilize iPi Mocap Studio software and iPi Biomech Add-on plug-in, which provide us visual and biomechanical human gait data: linear and angular joint coordinates, velocity, acceleration, center of mass (CoM) position, skeleton and 3D point cloud. The final analysis was performed in MATLAB environment, calculating zero moment point (ZMP) and ground projection of the CoM (GCoM) trajectories from human body dynamics by considering human body as a single weight point. These were followed by GCoM and ZMP error estimation. The further objective of our research is to reproduce the obtained with our MoCap system human-like gait with Russian biped robot AR-601M.