Dominik Belter

A biologically inspired approach to feasible gait learning for a hexapod robot

A biologically inspired approach to feasible gait learning for a hexapod robot The objective of this paper is to develop feasible gait patterns that could be used to control a real hexapod walking robot. These gaits should enable the fastest movement that is possible with the given robots mechanics and drives on a flat terrain. Biological inspirations are commonly used in the design of walking robots and their control algorithms. However, legged robots differ significantly from their biological counterparts. Hence we believe that gait patterns should be learned using the robot or its simulation model rather than copied from insect behaviour. However, as we have found tahula rasa learning ineffective in this case due to the large and complicated search space, we adopt a different strategy: in a series of simulations we show how a progressive reduction of the permissible search space for the leg movements leads to the evolution of effective gait patterns. This strategy enables the evolutionary algorithm to discover proper leg co-ordination rules for a hexapod robot, using only simple dependencies between the states of the legs and a simple fitness function. The dependencies used are inspired by typical insect behaviour, although we show that all the introduced rules emerge also naturally in the evolved gait patterns. Finally, the gaits evolved in simulations are shown to be effective in experiments on a real walking robot.

Precise self‐localization of a walking robot on rough terrain using parallel tracking and mapping

Purpose – The purpose of this paper is to describe a novel application of the recently introduced concept from computer vision to self‐localization of a walking robot in unstructured environments. The technique described in this paper enables a walking robot with a monocular vision system (single camera) to obtain precise estimates of its pose with regard to the six degrees of freedom. This capability is essential in search and rescue missions in collapsed buildings, polluted industrial plants, etc.Design/methodology/approach – The Parallel Tracking and Mapping (PTAM) algorithm and the Inertial Measurement Unit (IMU) are used to determine the 6‐d.o.f. pose of a walking robot. Bundle‐adjustment‐based tracking and structure reconstruction are applied to obtain precise camera poses from the monocular vision data. The inclination of the robots platform is determined by using IMU. The self‐localization system is used together with the RRT‐based motion planner, which allows to walk autonomously on rough, previ...

Integrated Motion Planning for a Hexapod Robot Walking on Rough Terrain

Abstract Missions of walking robots in distant areas require use of the teleoperation mode. However, the capabilities of a human operator to sense the terrain and to control the robot are limited. Thus, a walking robot should have enough autonomy to take an advantage of its high locomotion capabilities in spite of a limited feedback from the remote operator. This paper presents a method for real-time motion planning on a rugged terrain. The proposed method employs several modules for planning the robots path and trajectories of the feet, foothold selection, collision avoidance, and stability analysis. By using this method the robot can autonomously find a path to the desired position and discriminate between traversable and non-traversable areas. The rapidly exploring random trees concept is used as a backbone of the proposed solution. Results of simulations and experiments on the real robot are presented.

On the Performance of Pose-Based RGB-D Visual Navigation Systems

This paper presents a thorough performance analysis of several variants of the feature-based visual navigation system that uses RGB-D data to estimate in real-time the trajectory of a freely moving sensor. The evaluation focuses on the advantages and problems that are associated with choosing a particular structure of the sensor-tracking front-end, employing particular feature detectors/descriptors, and optimizing the resulting trajectory treated as a graph of sensor poses. Moreover, a novel yet simple graph pruning algorithm is introduced, which enables to remove spurious edges from the pose-graph. The experimental evaluation is performed on two publicly available RGB-D data sets to ensure that our results are scientifically verifiable.

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.

Map-based adaptive foothold planning for unstructured terrain walking

This paper presents an adaptive foothold planning method for a hexapod walking robot. A local terrain map acquired with an inexpensive structured light sensor is exploited as the information source for the planning algorithm, which uses a polynomial-based approximation method to create a decision surface. The robot learns from simulations, therefore no a priori knowledge is required. The results show that the method is general enough to work on various types of terrain. The planned footholds enable the robot to walk more stable, avoiding slippages and fall-downs.

Posture optimization strategy for a statically stable robot traversing rough terrain

This paper presents a posture optimization algorithm for a six-legged walking robot. During walking on rough terrain and planning its motion the robot has to determine the horizontal position, distance to the ground, and inclination of the platform. The proposed posture optimization algorithm is based on the Particle Swarm Optimization method. The algorithm increases the stability margin and maximizes the possible motion range of the robot (by maximizing the kinematic margin of each leg). The computation of the kinematic margin is performed by using an analytical function obtained with the Gaussian approximation. The Gaussian-based approximation significantly decreases the time consumed by the algorithm and allows to implement the posture optimization procedure on the real robot. The posture optimization is used as a part of the RRT-based motion planer to find a full-body path while climbing the obstacles.

Estimating terrain elevation maps from sparse and uncertain multi-sensor data

This paper addresses the issues of unstructured terrain modelling for the purpose of motion planning in an insect-like walking robot. Starting from the well-established elevation grid concept adopted to the specific requirements of a small legged robot with limited perceptual capabilities we extend this idea to a multi-sensor system consisting of a 2D pitched laser scanner, and a stereo vision camera. We propose an extension of the usual, ad hoc elevation grid update mechanism by incorporating formal treatment of the spatial uncertainty, and show how data from different sensors can be fused into a consistent terrain model. Moreover, this paper describes a novel method for filling-in missing areas of the elevation grid, which appear due to erroneous data or the line-of-sight constraints of the sensors. This method takes into account the uncertainty of the multi-sensor data collected in the elevation grid.

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.

Improving accuracy of feature-based RGB-D SLAM by modeling spatial uncertainty of point features

Many recent solutions to the RGB-D SLAM problem use the pose-graph optimization approach, which marginalizes out the actual depth measurements. In this paper we employ the same type of factor graph optimization, but we investigate the gains coming from maintaining a map of RGBD point features and modeling the spatial uncertainty of these features. We demonstrate that RGB-D SLAM accuracy can be increased by employing uncertainty models reflecting the actual errors introduced by measurements and image processing. The new approach is validated in simulations and in experiments involving publicly available data sets to ensure that our results are verifiable.