Safe Adaptive Traversability Learning for Mobile Robots

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Study of Mobile Robot Operations Related to Lunar Exploration

Mobile robots extend the reach of exploration in environments unsuitable, or unreachable, by humans. Far-reaching environments, such as the south lunar pole, exhibit lighting conditions that are challenging for optical imagery required for mobile robot navigation. Terrain conditions also impact the operation of mobile robots; distinguishing terrain types prior to physical contact can improve hazard avoidance. This thesis presents the conclusions of a trade-off that uses the results from two studies related to operating mobile robots at the lunar south pole. The lunar south pole presents engineering design challenges for both tele-operation and lidar-based autonomous navigation in the context of a near-term, low-cost, short-duration lunar prospecting mission. The conclusion is that direct-drive tele-operation may result in improved science data return. The first study is on demonstrating lidar reflectance intensity, and near-infrared spectroscopy, can improve terrain classification over optical imagery alone. Two classification techniques, Naive Bayes and multi-class SVM, were compared for classification errors. Eight terrain types, including aggregate, loose sand and compacted sand, are classified using wavelet-transformed optical images, and statistical values of lidar reflectance intensity. The addition of lidar reflectance intensity was shown to reduce classification errors for both classifiers. Four types of aggregate material are classified using statistical values of spectral reflectance. The addition of spectral reflectance was shown to reduce classification errors for both classifiers. The second study is on human performance in tele-operating a mobile robot over time-delay and in lighting conditions analogous to the south lunar pole. Round-trip time delay between operator and mobile robot leads to an increase in time to turn the mobile robot around obstacles or corners as operators tend to implement a `wait and see\u27 approach. A study on completion time for a cornering task through varying corridor widths shows that time-delayed performance fits a previously established cornering law, and that varying lighting conditions did not adversely affect human performance. The results of the cornering law are interpreted to quantify the additional time required to negotiate a corner under differing conditions, and this increase in time can be interpreted to be predictive when operating a mobile robot through a driving circuit

Adaptive and intelligent navigation of autonomous planetary rovers - A survey

The application of robotics and autonomous systems in space has increased dramatically. The ongoing Mars rover mission involving the Curiosity rover, along with the success of its predecessors, is a key milestone that showcases the existing capabilities of robotic technology. Nevertheless, there has still been a heavy reliance on human tele-operators to drive these systems. Reducing the reliance on human experts for navigational tasks on Mars remains a major challenge due to the harsh and complex nature of the Martian terrains. The development of a truly autonomous rover system with the capability to be effectively navigated in such environments requires intelligent and adaptive methods fitting for a system with limited resources. This paper surveys a representative selection of work applicable to autonomous planetary rover navigation, discussing some ongoing challenges and promising future research directions from the perspectives of the authors

An Intelligent Architecture for Legged Robot Terrain Classification Using Proprioceptive and Exteroceptive Data

In this thesis, we introduce a novel architecture called Intelligent Architecture for Legged Robot Terrain Classification Using Proprioceptive and Exteroceptive Data (iARTEC ) . The proposed architecture integrates different terrain characterization and classification with other robotic system components. Within iARTEC , we consider the problem of having a legged robot autonomously learn to identify different terrains. Robust terrain identification can be used to enhance the capabilities of legged robot systems, both in terms of locomotion and navigation. For example, a robot that has learned to differentiate sand from gravel can autonomously modify (or even select a different) path in favor of traversing over a better terrain. The same knowledge of the terrain type can also be used to guide a robot in order to avoid specific terrains. To tackle this problem, we developed four approaches for terrain characterization, classification, path planning, and control for a mobile legged robot. We developed a particle system inspired approach to estimate the robot footâ ground contact interaction forces. The approach is derived from the well known Bekkerâ s theory to estimate the contact forces based on its point contact model concepts. It is realistically model real-time 3-dimensional contact behaviors between rigid body objects and the soil. For a real-time capable implementation of this approach, its reformulated to use a lookup table generated from simple contact experiments of the robot foot with the terrain. Also, we introduced a short-range terrain classifier using the robot embodied data. The classifier is based on a supervised machine learning approach to optimize the classifier parameters and terrain it using proprioceptive sensor measurements. The learning framework preprocesses sensor data through channel reduction and filtering such that the classifier is trained on the feature vectors that are closely associated with terrain class. For the long-range terrain type prediction using the robot exteroceptive data, we present an online visual terrain classification system. It uses only a monocular camera with a feature-based terrain classification algorithm which is robust to changes in illumination and view points. For this algorithm, we extract local features of terrains using Speed Up Robust Feature (SURF). We encode the features using the Bag of Words (BoW) technique, and then classify the words using Support Vector Machines (SVMs). In addition, we described a terrain dependent navigation and path planning approach that is based on E* planer and employs a proposed metric that specifies the navigation costs associated terrain types. This generated path naturally avoids obstacles and favors terrains with lower values of the metric. At the low level, a proportional input-scaling controller is designed and implemented to autonomously steer the robot to follow the desired path in a stable manner. iARTEC performance was tested and validated experimentally using several different sensing modalities (proprioceptive and exteroceptive) and on the six legged robotic platform CREX. The results show that the proposed architecture integrating the aforementioned approaches with the robotic system allowed the robot to learn both robot-terrain interaction and remote terrain perception models, as well as the relations linking those models. This learning mechanism is performed according to the robot own embodied data. Based on the knowledge available, the approach makes use of the detected remote terrain classes to predict the most probable navigation behavior. With the assigned metric, the performance of the robot on a given terrain is predicted. This allows the navigation of the robot to be influenced by the learned models. Finally, we believe that iARTEC and the methods proposed in this thesis can likely also be implemented on other robot types (such as wheeled robots), although we did not test this option in our work

System of Terrain Analysis, Energy Estimation and Path Planning for Planetary Exploration by Robot Teams

NASA’s long term plans involve a return to manned moon missions, and eventually sending humans to mars. The focus of this project is the use of autonomous mobile robotics to enhance these endeavors. This research details the creation of a system of terrain classification, energy of traversal estimation and low cost path planning for teams of inexpensive and potentially expendable robots. The first stage of this project was the creation of a model which estimates the energy requirements of the traversal of varying terrain types for a six wheel rocker-bogie rover. The wheel/soil interaction model uses Shibly’s modified Bekker equations and incorporates a new simplified rocker-bogie model for estimating wheel loads. In all but a single trial the relative energy requirements for each soil type were correctly predicted by the model. A path planner for complete coverage intended to minimize energy consumption was designed and tested. It accepts as input terrain maps detailing the energy consumption required to move to each adjacent location. Exploration is performed via a cost function which determines the robot’s next move. This system was successfully tested for multiple robots by means of a shared exploration map. At peak efficiency, the energy consumed by our path planner was only 56% that used by the best case back and forth coverage pattern. After performing a sensitivity analysis of Shibly’s equations to determine which soil parameters most affected energy consumption, a neural network terrain classifier was designed and tested. The terrain classifier defines all traversable terrain as one of three soil types and then assigns an assumed set of soil parameters. The classifier performed well over all, but had some difficulty distinguishing large rocks from sand. This work presents a system which successfully classifies terrain imagery into one of three soil types, assesses the energy requirements of terrain traversal for these soil types and plans efficient paths of complete coverage for the imaged area. While there are further efforts that can be made in all areas, the work achieves its stated goals

Autonomic tackling of unknown obstacles in navigation of robotic platform

Σκοπός της παρούσας διπλωματικής είναι η ανάπτυξη μεθόδου ώστε μια ρομποτική πλατφόρμα εξωτερικού χώρου να ανακαλύπτει μόνη της, με βάση τους αισθητήρες της και τη γνώση που έχει αποκτήσει, πώς πρέπει να προσεγγίζει το εκάστοτε εμπόδιο που βρίσκεται μπροστά της, αν μπορεί να το υπερπηδήσει ή αν χρειάζεται να το παρακάμψει. Η αποφυγή εμποδίων εξασφαλίζει την ασφάλεια και ακεραιότητα τόσο της ρομποτικής πλατφόρμας όσο και των ανθρώπων και αντικειμένων που υπάρχουν στον ίδιο χώρο. Αυτός είναι ένας από τους λόγους που οι περισσότερες προσεγγίσεις τέτοιων θεμάτων επικεντρώνονται κυρίως στους ελιγμούς για την αποφυγή εμποδίων αντί για την παραγωγή αυτόνομων συστημάτων με ικανότητα αυτοβελτίωσης. Δεν υπάρχει μεγάλη βιβλιογραφία για ρομπότ που έχουν την περιέργεια να εξερευνήσουν το περιβάλλον τους, για περιπτώσεις δηλαδή που δεν υπάρχει συγκεκριμένος στόχος, αλλά μόνο η αφηρημένη ανάγκη του ρομπότ να εξερευνήσει ένα καινούριο περιβάλλον. Στην παρούσα διατριβή παρουσιάζουμε ένα σύστημα που όχι μόνο κατατάσσει αυτόνομα το περιβάλλον του σε προσπελάσιμες και μη προσπελάσιμες περιοχές, αλλά επίσης έχει την ικανότητα να αυτοβελτιώνεται. Για να το επιτύχουμε, χρησιμοποιούμε ένα προεκπαιδευμένο νευρωνικό δίκτυο που αναπαριστά χρωματικά τα αντικείμενα της σκηνής. Αναπτύσσουμε ένα πρόγραμμα, το οποίο δέχεται ως είσοδο εικόνες που εξάγονται από το προαναφερθέν νευρωνικό δίκτυο και προβλέπει αν το ρομπότ μπορεί να προσπελάσει τα απεικονιζόμενα αντικείμενα. Το πρόγραμμα αυτό εκπαιδεύεται και στη συνέχεια αξιολογείται η αποτελεσματικότητά του. Τα αποτελέσματά μας κρίνουμε ότι είναι αρκετά ικανοποιητικά. Το ποσοστό σφάλματος μπορεί να εξηγηθεί από το γεγονός ότι το περιβάλλον δεν είναι ομοιόμορφα κατανεμημένο σε εμπόδια και προσπελάσιμες περιοχές ενώ παράλληλα δεν είναι πάντοτε σαφές τι από τα δύο υπερισχύει. Τέλος, δείχνουμε ότι είναι εύκολο να μειωθεί το ποσοστό σφάλματος με λίγες μόνο τροποποιήσεις.The goal of the present thesis is to develop a method for a robotic outdoor platform. The robot should discover by itself, based on its sensors and its previous knowledge, how to approach an obstacle that stands in front of it, whether it is capable of driving over the obstacle or should avoid it. Obstacle avoidance ensures the safety and integrity of both the robotic platform and the people and objects present in the same space. That is one of the reasons why current approaches mainly concentrate on maneuver to avoid obstacles rather than yield autonomous systems with the ability to self improve. There is not much work done on curiosity-driven exploration, in which there is no explicit goal, but the abstract need for the robot to learn a new environment. In the current thesis we introduce a system that not only autonomously classifies its environment to areas that can or cannot be driven over, but also has the capacity for selfimprovement. To do so, we use a pre-trained neural network for whole scene semantic segmentation. We implement a program that accepts as input images extracted from the neural network mentioned above and predicts whether the illustrated scenes can be traversed or not. The program trains itself and then evaluates its effectiveness. Our results are quite satisfactory and the error rate can be explained by the fact that the environment is not evenly distributed in obstacles and paths, while at the same time it is not always clear which one is dominant. Furthermore, we show that our model can be easily optimized with just a few modifications