Krzysztof Walas

Terrain Classification and Negotiation with a Walking Robot

This paper describes a walking robot controller for negotiation of terrains with different traction characteristics. The feedback is based on three perception systems. The purpose of the presented research is to enhance the autonomy of the walking robot. The information about the class of the terrain allows the robot to work in the real world scenarios more efficiently. In the presented work twelve types of the ground were tested. Suitability of each type of the perception system for characterizing the terrain class was checked. Namely, vision, depth and tactile sensors were used. In each case as a classifier the Support Vector Machines were utilized. The separate classification results from each sensor were combined to obtain better precision and recall in the ground classification process. The information about the terrain type was fed into robot controller to adapt the robot gait parameters. The goal was to achieve good balance between the speed of the movement and the vibration caused by the bounciness and the irregularities of the terrain.The paper begins with the description of the experimental setup. Next, the classification results for each sensor used are presented. Then, the rules of combining classifiers were tested. Finally, the robot gait controller was proposed and evaluated.

A Compact Walking Robot – Flexible Research and Development Platform

In the paper new six-legged robot Messor II is described. The new machine is the improved version of the previous robot Messor. The current design has better power to mass ratio. Additionally new servos, which power the joint of the robot, allows for better control and motion execution. The paper contains three main parts. In the first section mechanical design is presented. Then, the electronic part of the robot is described. Next the control system of the robot is outlined.

Distributed control system of DC servomotors for six legged walking robot

The multi-layered drives control for walking robot is discussed. It is a six-legged robot with 18 DOF. There is one DC-Servomotor for each joint. Synchronization of drives is a matter as walking is performed in unpredictable environment, and under high measurement uncertainty. It is done through distributed control of the robot. Each leg has itpsilas separate controller. Leg controllers are connected to robot master controller. The master controller is responsible for communication between legs and host computer. The article also addresses sensing system issues for walking robot and control loops closed through these sensors.

Lightweight RGB-D SLAM System for Search and Rescue Robots

Search and rescue robots ought to be autonomous, as it enables to keep the human personnel out of dangerous areas. To achieve desirable level of the autonomy both environment mapping and reliable self-localization have to be implemented. In this paper we analyse the application of a fast, lightweight RGB-D Simultaneous Localization and Mapping (SLAM) system for robots involved in indoor/outdoor search and rescue missions. We demonstrate that under some conditions the RGB-D sensors provide data reliable enough even for outdoor, real-time SLAM. Experiments are performed on a legged robot and a wheeled robot, using two representative RGB-D sensors: the Asus Xtion Pro Live and the recently introduced Microsoft Kinect ver. 2.

Terrain classification using Laser Range Finder

This paper presents terrain classification method based on the intensity readings from Laser Range Finder. The classification is performed on the feature vectors obtained using statistical descriptors or Fourier Transform computed for the patches of the intensity map for each terrain sample. As a classifier Support Vector Machines were used. For the set of 12 terrains results of classification are reaching the level of 98% of the correctly recognized terrain samples. The proposed approach has a low computational cost, which is required for its real time applications. The article begins with the description of the experimental setup followed by the presentation of the proposed feature vectors for the registered intensity maps. Next, classification results, using introduced features, are given and compared to other approaches found in literature. At the end concluding remarks are given.

The Classification of the Terrain by a Hexapod Robot

This paper presents a new approach to the terrain classification by a hexapod robot using the tactile information. The data was acquired using the force/torque sensor mounted on the walking robot foot. Two types of classifiers were used and compared: the Normal Bayes Classifier (NBC) and the Classification And Regression Tree (CART). The article comprises the description of the experimental setup followed by the presentation of feature selection process and the comparison of the two classifiers’ accuracy. The classification system presented in the article allows the walking robot to recognize the type of the terrain on which it is currently walking on with over 90% accuracy.

Supporting locomotive functions of a six-legged walking robot

Supporting locomotive functions of a six-legged walking robot This paper presents a method for building a foothold selection module as well as methods for the stability check for a multi-legged walking robot. The foothold selection decision maker is shaped automatically, without expert knowledge. The robot learns how to select appropriate footholds by walking on rough terrain or by testing ground primitives. The gathered knowledge is then used to find a relation between slippages and the obtained local shape of the terrain, which is further employed to assess potential footholds. A new approach to function approximation is proposed. It uses the least-squares fitting method, the Kolmogorov theorem and population-based optimization algorithms. A strategy for re-learning is proposed. The role of the decision support unit in the control system of the robot is presented. The importance of the stability check procedure is shown. A method of finding the stability region is described. Further improvements in the stability check procedure due to taking into account kinematic correction are reported. A description of the system for calculating static stability on-line is given. Methods for measuring stance forces are described. The measurement of stance forces facilitates the extended stability check procedure. The correctness of the method is proved by results obtained in a real environment on a real robot.

Autonomous Stair Climbing with Multisensor Feedback

Abstract This article presents an autonomous stair climbing procedure with multisensor feedback. The task is performed by the six-legged robot Messor. The control procedure is performed in a closed-loop, and requires appropriate measurements. The sensory system of the robot consists of a structural light system and a laser range finder. The first one provides information about the dimensions of stairs and the second one measures current pose of the robot on stairs. The execution of the procedure is fully automatic. Once the stairs are found no other external input to the system is required till the top of the stairs. The climbing strategy was developed in simulation and then transferred to the real robot controller. The Messor robot is able to climb stairs of different sizes, and thanks to the sensory feedback copes well with the imperfectness of the environment.

Affordable Multi-legged Robots for Research and STEM Education: A Case Study of Design and Technological Aspects

As much interest in various aspects of legged locomotion arose in the robotics community over the last decade, many custom design walking robots have been demonstrated. However, they are usually very complicated and expensive. Thus, in this paper we present two families of small-to-medium size legged robots, that share the same basic concepts of using inexpensive, off-the-shelf servos as actuators, and the idea of making the mechanical design technologically simple. Although developed with a similar idea in mind, these robots differ with respect to many design choices and the manufacturing technology. In this paper we try to asses critically those differences, formulating some guidelines for future designs.

Hardware implementation of ground classification for a walking robot

The mobile robot requires the knowledge about the ground type in front of it to efficiently negotiate diverse terrain types while working outdoors. This article presents the implementation of ground classification algorithms in a Field Programmable Gate Array structure. The terrain type classification is based on the signals acquired with force/torque sensor mounted on the walking robot foot. The hardware implementation allows for offloading of the resource demanding computations. The paper begins with a short presentation of the experimental setup. Then the classification algorithms are described. Finally the description of hardware implementation of the algorithms is given followed by the test results.