Sensor Preprocessing Research Articles

TALBOT: A Track-Leg Transformable Robot.

This article introduces a tracked-leg transformable robot, TALBOT. The mechanical and electrical design, control method, and environment perception based on LiDAR are discussed. The original tracked-leg transformable structure allows the robot to switch between the tracked and legged mode to achieve all-terrain adaptation. In the tracked mode, TALBOT is controlled by the method of differential speed between the two tracked feet. In the legged mode, TALBOT is controlled based on a bionic control strategy of the central pattern generator to realize the generation and conversion of gait. In addition, the robot is equipped with a LiDAR, through sensor preprocessing and optimization of the slam mapping algorithm, so that the robot achieves a better mapping effect. We tested the robot’s motion performance and the slam mapping effect, including going straight and turning in tracked and legged modes and building a map in an indoor environment.

System Design and Testing of the Hominid Robot Charlie

Current and future application scenarios for mobile robots deal with increased requirements regarding autonomy and flexibility in the locomotor system. To cope with these demands, a high sensor quantity and quality allows us to perform robust locomotion. The authors present a hominid robotic system that is equipped with multi-point-contact feet and an active artificial spine to incorporate extended sensing and locomotion capabilities for walking robots. In the proposed robotic system, the front and rear part are connected via an actuated spinal structure with six degrees of freedom. To increase the robustness of the system's locomotion in terms of traction and stability, a footlike structure equipped with various sensors has been developed. Altogether, the robot embodies more than 330 sensors. In terms of distributed local control, the structures feature their own local intelligence and are as autonomous as possible regarding sensing, sensor preprocessing, control, and communication. This allows the robot to respond to external disturbances with minor latency. Within this paper, the proposed robotic system and its distributed and hierarchical control method are presented. To validate the electromechanical and software approach, the authors present results verified experimentally in different environments (in- and outdoor) with differing walking speeds on various substrates and in varying inclinations from −20∘ to 20°. The results show that the presented approach is viable and improves the flexibility of the locomotor system. A hominid design was chosen in order to perform various types of locomotion. To demonstrate the entire functionality of the developed hardware, two different motion modes (quadrupedal and bipedal locomotion) are investigated. This also includes a stable transition from a four-legged posture to an upright posture and vice versa.

A hybrid approach for Structural Monitoring with self-organizing multi-agent systems and inverse numerical methods in material-embedded sensor networks

One of the major challenges in Structural Monitoring of mechanical structures is the derivation of meaningful information from sensor data. This work investigates a hybrid data processing approach for material-integrated structural health and load monitoring systems by using self-organizing mobile multi-agent systems (MAS), and inverse numerical methods providing the spatial resolved load information from a set of sensors embedded in the technical structure with low-resource agent processing platforms scalable to microchip level, enabling material-integrated real-time sensor systems. The MAS is deployed in a heterogeneous environment and offers event-based sensor preprocessing, distribution, and pre-computation. Inverse numerical approaches usually require a large amount of computational power and storage resources, not suitable for resource constrained sensor node implementations. Instead, the computation is partitioned into spatial off-line (outside the network) and on-line parts, with on-line sensor processing performed by the agent system. A unified multi-domain simulation framework is used to profile and validate the proposed approach.

Distributed Computation in a Quadrupedal Robotic System

Today's and future space missions (will) have to deal with increasing requirements regarding autonomy and flexibility in the locomotor system. To cope with these requirements, a higher bandwidth for sensor information is needed. In this paper, a robotic system is presented that is equipped with artificial feet and a spine incorporating increased sensing capabilities for walking robots. In the proposed quadrupedal robotic system, the front and rear parts are connected via an actuated spinal structure with six degrees of freedom. In order to increase the robustness of the system's locomotion in terms of traction and stability, a foot-like structure equipped with various sensors has been developed. In terms of distributed local control, both structures are as self-contained as possible with regard to sensing, sensor preprocessing, control and communication. This allows the robot to respond rapidly to occurring events with only minor latency.

Novel Wavelet-mFCM Algorithm for Environmental Sensor Drift Correction

A novel hybrid sensor informatics architecture based on discrete wavelet transform (DWT) and multiple fuzzy logic based clustering (m-FCM) is investigated and proposed to estimate sensor drift in a real life oceanic sensor network. DWT is used for sensor pre-processing, data dimension reduction and feature extraction from sensor time series, where as FCM-based approach is used to estimate and correct the cumulative drift in the sensory system. This new drift correction algorithm is tested on a real time estuary sensory platform deployed to monitor the Derwent Estuary in Hobart, Australia, to evaluate the performance. This algorithm outperforms previously reported drift correction paradigms.

Environmental Sensor Drift Correction using Wavelet-mFCM-FIS Architecture: An Unsupervised Machine Learning Approach

novel hybrid sensor informatics architecture based on Discrete Wavelet Transform (DWT), Fuzzy logic based clustering (FCM) and Fuzzy Rule based Inference system (FIS) has been investigated and proposed to estimate dynamic generic sensor drift during physical sampling. DWT has been used for sensor pre-processing; data dimension reduction and feature extraction from sensor time series, where as DWT- FCM based approach has been used to estimate the cumulative drift in our sensory system. An intelligent generalized algorithm using multiple FCM maps (m-FCM) has been proposed to implement the drift correction on any future unknown data sets. In the next stage a newly proposed DWT-FCM-FIS architecture has been tested on multisensory environmental data sets covering three consecutive years (2009 - 11) to predict probable month (environmental time of the year) with up to 93% accuracy. This novel hybrid data processing architecture have been implemented and tested upon a real time estuary sensory platform of temperature, conductivity and salinity sensors that have been deployed to monitor the Derwent Estuary in Hobart, Australia. This newly proposed approach could be an adaptive solution to tackle drifts in the sensor networks and improve overall monitoring quality.

Task modelling in collective robotics

Does coherent collective behaviour require an explicit mechanism of cooperation? In this paper, we demonstrate that a certain class of cooperative tasks, namely coordinated box manipulation, are possible without explicit communication or cooperation mechanisms. The approach relies on subtask decomposition and sensor preprocessing. A framework is proposed for modelling multi-robot tasks which are described as a series of steps with each step possibly consisting of substeps. Finite state automata theory is used to model steps with state transitions specified as binary sensing predicates called perceptual cues. A perceptual cue (Q), whose computation is disjoint from the operation of the automata, is processed by a 3-level finite state machine called a Q-machine. The model is based on entomological evidence that suggests local stimulus cues are used to regulate a linear series of building acts in nest construction. The approach is designed for a redundant set of homogeneous mobile robots, and described is an extension of a previous system of 5 box-pushing robots to 11 identical transport robots. Results are presented for a system of physical robots capable of moving a heavy object collectively to an arbitrarily specified goal position. The contribution is a simple task-programming paradigm for mobile multi-robot systems. It is argued that Q-machines and their perceptual cues offer a new approach to environment-specific task modelling in collective robotics.