Comparative Study of Different Methods in Vibration-Based Terrain Classification for Wheeled Robots with Shock Absorbers

open access articleAutonomous robots that operate in the field can enhance their security and efficiency by accurate terrain classification, which can be realized by means of robot-terrain interaction-generated vibration signals. In this paper, we explore the vibration-based terrain classification (VTC), in particular for a wheeled robot with shock absorbers. Because the vibration sensors are usually mounted on the main body of the robot, the vibration signals are dampened significantly, which results in the vibration signals collected on different terrains being more difficult to discriminate. Hence, the existing VTC methods applied to a robot with shock absorbers may degrade. The contributions are two-fold: (1) Several experiments are conducted to exhibit the performance of the existing feature-engineering and feature-learning classification methods; and (2) According to the long short-term memory (LSTM) network, we propose a one-dimensional convolutional LSTM (1DCL)-based VTC method to learn both spatial and temporal characteristics of the dampened vibration signals. The experiment results demonstrate that: (1) The feature-engineering methods, which are efficient in VTC of the robot without shock absorbers, are not so accurate in our project; meanwhile, the feature-learning methods are better choices; and (2) The 1DCL-based VTC method outperforms the conventional methods with an accuracy of 80.18%, which exceeds the second method (LSTM) by 8.23%

Learning Visual Locomotion with Cross-Modal Supervision

In this work, we show how to learn a visual walking policy that only uses a monocular RGB camera and proprioception. Since simulating RGB is hard, we necessarily have to learn vision in the real world. We start with a blind walking policy trained in simulation. This policy can traverse some terrains in the real world but often struggles since it lacks knowledge of the upcoming geometry. This can be resolved with the use of vision. We train a visual module in the real world to predict the upcoming terrain with our proposed algorithm Cross-Modal Supervision (CMS). CMS uses time-shifted proprioception to supervise vision and allows the policy to continually improve with more real-world experience. We evaluate our vision-based walking policy over a diverse set of terrains including stairs (up to 19cm high), slippery slopes (inclination of 35 degrees), curbs and tall steps (up to 20cm), and complex discrete terrains. We achieve this performance with less than 30 minutes of real-world data. Finally, we show that our policy can adapt to shifts in the visual field with a limited amount of real-world experience. Video results and code at https://antonilo.github.io/vision_locomotion/.Comment: Learning to walk from pixels in the real world by using proprioception as supervision. Project page for videos and code: https://antonilo.github.io/vision_locomotion

A New Terrain Classification Framework Using Proprioceptive Sensors for Mobile Robots

Mobile robots that operate in real-world environments interact with the surroundings to generate complex acoustics and vibration signals, which carry rich information about the terrain. This paper presents a new terrain classification framework that utilizes both acoustics and vibration signals resulting from the robot-terrain interaction. As an alternative to handcrafted domain-specific feature extraction, a two-stage feature selection method combining ReliefF and mRMR algorithms was developed to select optimal feature subsets that carry more discriminative information. As different data sources can provide complementary information, a multiclassifier combination method was proposed by considering a priori knowledge and fusing predictions from five data sources: one acoustic data source and four vibration data sources. In this study, four conceptually different classifiers were employed to perform the classification, each with a different number of optimal features. Signals were collected using a tracked robot moving at three different speeds on six different terrains. The new framework successfully improved classification performance of different classifiers using the newly developed optimal feature subsets. The greater improvement was observed for robot traversing at lower speeds

Rigidity-Based Surface Recognition for a Domestic Legged Robot

Although the infrared (IR) range and motor force sensors have been rarely applied to the surface recognition of mobile robots, they are fused in this paper with accelerometer and ground contact force sensors to distinguish six indoor surface types. Their sensor values are affected by the crawling gait period, therefore, certain components of the fast Fourier transform over these data are included in the feature vectors as well as remarkable discriminative power is observed for the same scalar statistics of different sensing modalities. The machine learning aspects are analyzed with random forests (RF) because of their stable performance and some inherent, beneficial properties for the model development process. The robustness is evaluated with unseen data after the model accuracy is estimated with cross-validation (CV), and regardless whether a Sony ERS-7 walks barefoot or wears socks, the forests achieve 94% accuracy. This result outperforms the state of the art techniques for indoor surfaces in the literature and the classification execution is real-time on the robot. The above mentioned model development process with RF is documented to create new models for other robots more quickly and efficiently

Optimum Pipeline for Visual Terrain Classification Using Improved Bag of Visual Words and Fusion Methods

We propose an optimum pipeline and develop the hybrid representation to produce an effective and efficient visual terrain classification system. The bag of visual words (BOVW) framework has emerged as a promising approach and effective paradigm for visual terrain classification. The method includes four main steps: (1) feature extraction, (2) codebook generation, (3) feature coding, and (4) pooling and normalization. Recent researches have primarily focused on feature extraction in the development of new handcrafted descriptors that are specific to the visual terrain. However, the effects of other steps on visual terrain classification are still unknown. At the same time, fusion methods are often used to boost classification performance by exploring the complementarity of diverse features. We provide a comprehensive study of all steps in the BOVW framework and different fusion methods for visual terrain classification. Then, multiple approaches in each step and their effects are explored on the visual terrain dataset. Finally, the feature preprocessing technique, improved BOVW framework, and fusion method are used to construct an optimum pipeline for visual terrain classification. The hybrid representation developed by the optimum pipeline performs effectively and rapidly for visual terrain classification in the terrain dataset, outperforming those current methods. Furthermore, it is robust to diverse noises and illumination alterations

Path Planning for incline terrain using Embodied Artificial Intelligence

Η Ενσώματη Τεχνητή Νοημοσύνη στοχεύει στο να καλύψει την ανάγκη για την αναπαράσταση ενός προβλήματος αναζήτησης, καθώς και την αναπαράσταση του τι συνιστά “καλή” λύση για το πρόβλημα αυτό σε μια έξυπνη μηχανή. Στην περίπτωση της παρούσας πτυχιακής, αυτή η έξυπνη μηχανή είναι ένα ρομπότ. Συνδυάζοντας την Τεχνητή Νοημοσύνη και την Ρομποτική μπορούμε να ορίσουμε πειράματα των οποίων ο χώρος αναζήτησης είναι ο φυσικός κόσμος και τα αποτελέσματα κάθε πράξης συνιστούν την αξιολόγηση της κάθε λύσης. Στο πλαίσιο της πτυχιακής μου είχα την ευκαιρία να πειραματιστώ με την ανάπτυξη αλγορίθμων Τεχνητής Νοημοσύνης οι οποίοι καθοδηγούν ένα μη επανδρωμένο όχημα εδάφους στην ανακάλυψη μιας λύσης ενός δύσκολου προβλήματος πλοήγησης σε εξωτερικό χώρο, όπως η διάσχιση ενός εδάφους με απότομη κλίση. Επιχείρησα να αντιμετωπίσω το πρόβλημα αυτό με τρεις διαφορετικές προσεγγίσεις, μία με αλγόριθμο Hill Climbing, μία με N-best αναζήτηση και μία με Εξελικτικό Αλγόριθμο, καθεμία με τα δικά της προτερήματα και τις δικές της αδυναμίες. Τελικά, δημιούργησα και αξιολόγησα επίδειξεις, τόσο σε προσομοιωμένα σενάρια όσο και σε ένα σενάριο στον πραγματικό κόσμο. Τα αποτελέσματα αυτών των επιδείξεων δείχνουν μία σαφή πρόοδο στην προσέγγιση του προαναφερθέντος προβλήματος από μία ρομποτική πλαρφόρμα.Embodied Artificial Intelligence aims to cover the need of a search problem’s representation, as well as the representation of what constitutes a “good” solution to this problem in a smart machine. In this thesis’ case, this smart machine is a robot. When we combine Artificial Intelligence and Robotics we can define experiments where the search space is the physical world and the results of each action constitute each solution’s evaluation. In my thesis’ context, I had the opportunity to experiment with the development of artificial intelligence algorithms that guide an unmanned ground vehicle to discover the solution of a tough outdoor navigation problem, like traversing a terrain region of steep incline. I attempted to face the problem with three different approaches. A Hill Climbing algorithm approach, a N-best search approach and an Evolutionary Algorithm approach, each one with its own strengths and weaknesses. In the end, I created and I evaluated demonstrations, both in simulated scenarios and in a real world scenario. The results of these demonstrations show a clear progress in the approach of the aforementioned problem, by the robotic platform

Autonomous Soil Assessment System for Planetary Rovers

Planetary rovers face mobility hazards associated with various classes of terrains they traverse, and hence it is desirable to enable remote prediction of terrain trafficability (ability to traverse) properties. For that reason, the development of algorithms for assessing terrain type and mobility properties, as well as for coupling these data in an online learning framework, represent important capabilities for next-generation rovers. This work focuses mainly on 3-way terrain classification (classifying as one of the types: Sand, Bedrock and Gravel) as well as on the correlation of terrain types and their mobility properties in a framework that enables online learning. For terrain classification, visual descriptors are developed, which are primarily based on visual texture and are captured in form of histograms of edge filter responses at various scales and orientations. The descriptors investigated in this work are HOG (Histogram of Oriented Gradients), GIST, MR8 (Maximum Response) Textons and the classification techniques implemented here are nearest and k-nearest neighbors. Further, monochrome image intensity is used as an additional feature to further distinguish bedrock from the other terrain types. No major differences in performance are observed between the three descriptors, leading to the adoption of the HOG approach due to its lower computational complexity (over 3 orders of magnitude difference in complexity between HOG and Textons) and thus higher applicability to planetary missions. Tests demonstrate an accuracy between 70% and 93% (81% average) for the classification using the HOG descriptor, on images taken by NASA’s Mars rovers. To predict terrain trafficability ahead of the rover, exteroceptive data namely terrain type and slope, are correlated with the trafficability metrics namely slip, sinkage and roughness, in a learning framework. A queue based data structure has been implemented for the correlation, which keeps discarding the older data so as to avoid diminishing the effect of newer data samples, when there is a large amount of data. This also ensures that the rover will be able to adapt to changing terrains responses and predict the risk level (low, medium or high) accordingly. Finally, all the algorithms developed in this work were tested and verified in a field test demo at the CSA (Canadian Space Agency) mars yard. The risk metric in combination with the queue based data structure, can achieve stable predictions in consistent terrains, while also being responsive to sudden changes in terrain trafficability