A New Approach to Visual-Based Sensory System for Navigation into Orange Groves

One of the most important parts of an autonomous robot is to establish the path by which it should navigate in order to successfully achieve its goals. In the case of agricultural robotics, a procedure that determines this desired path can be useful. In this paper, a new virtual sensor is introduced in order to classify the elements of an orange grove. This proposed sensor will be based on a color CCD camera with auto iris lens which is in charge of doing the captures of the real environment and an ensemble of neural networks which processes the capture and differentiates each element of the image. Then, the Hough’s transform and other operations will be applied in order to extract the desired path from the classification performed by the virtual sensory system. With this approach, the robotic system can correct its deviation with respect to the desired path. The results show that the sensory system properly classifies the elements of the grove and can set trajectory of the robot

Autonomous Legged Hill and Stairwell Ascent

This paper documents near-autonomous negotiation of synthetic and natural climbing terrain by a rugged legged robot, achieved through sequential composition of appropriate perceptually triggered locomotion primitives. The first, simple composition achieves autonomous uphill climbs in unstructured outdoor terrain while avoiding surrounding obstacles such as trees and bushes. The second, slightly more complex composition achieves autonomous stairwell climbing in a variety of different buildings. In both cases, the intrinsic motor competence of the legged platform requires only small amounts of sensory information to yield near-complete autonomy. Both of these behaviors were developed using X-RHex, a new revision of RHex that is a laboratory on legs, allowing a style of rapid development of sensorimotor tasks with a convenience near to that of conducting experiments on a lab bench. Applications of this work include urban search and rescue as well as reconnaissance operations in which robust yet simple-to-implement autonomy allows a robot access to difficult environments with little burden to a human operator

Sparse robot swarms: Moving swarms to real-world applications

Robot swarms are groups of robots that each act autonomously based on only local perception and coordination with neighbouring robots. While current swarm implementations can be large in size (e.g. 1000 robots), they are typically constrained to working in highly controlled indoor environments. Moreover, a common property of swarms is the underlying assumption that the robots act in close proximity of each other (e.g. 10 body lengths apart), and typically employ uninterrupted, situated, close-range communication for coordination. Many real-world applications, including environmental monitoring and precision agriculture, however, require scalable groups of robots to act jointly over large distances (e.g. 1000 body lengths), rendering the use of dense swarms impractical. Using a dense swarm for such applications would be invasive to the environment and unrealistic in terms of mission deployment, maintenance and post-mission recovery. To address this problem, we propose the sparse swarm concept, and illustrate its use in the context of four application scenarios. For one scenario, which requires a group of rovers to traverse, and monitor, a forest environment, we identify the challenges involved at all levels in developing a sparse swarm—from the hardware platform to communication-constrained coordination algorithms—and discuss potential solutions. We outline open questions of theoretical and practical nature, which we hope will bring the concept of sparse swarms to fruition

Path Planning Algorithm for Autonomous Urban Vehicles from the Viewpoint of Kuhn’s Philosophy and Popper’s Philosophy

Abstrak – Kendaraan otonom dapat bermanfaat untuk meningkatkan keselamatan, efisiensi, aksesibilitas dan kenyamanan transportasi. Salah satu tugas penting yang harus dilakukan oleh kendaraan otonom adalah melakukan perencanaan jalur melalui lingkungan perkotaan yang dinamis dimana terdapat kendaraan lain dan pejalan kaki. Menuruf filosofi Kuhn, sebuah keilmuan memiliki siklus hidupnya sendiri-sendiri. Siklus hidup suatu keilmuan terdiri dari fase kelahiran, fase pembuktian dan fase diterimanya keilmuan baru tersebut menjadi apa yang disebut sebagai ilmu normal. Demikian juga dengan perkembangan keilmuan perencaan jalur, algoritma perencanaan jalur untuk kendaraan otonom masih terus diteliti dan dikembangkan oleh berbagai peneliti. Studi baru terus dilakukan dalam upaya menemukan teknik perencanaan jalur yang dapat diterima secara umum, sehingga algoritma tersebut akan menjadi ilmu normal. Menutur filosofi Popper, setiap algoritma yang diusulkan harus dapat diuji menggunakan prinsip falsifikasi untuk menentukan apakah teknik yang diusulkan tersebut akhirnya bisa menjadi ilmu normal atau tidak. Maka makalah ini bertujuan untuk memberikan gambaran mengenai siklus keilmuan perencanaan jalur kendaraan otonom. Makalah ini juga bertujuan untuk membandingkan penelitian-penelitian terkini mengenai algoritma perencanaan jalur untuk kendaraan otonom di daerah perkotaan. Dalam membandingkan algoritma-algoritma tersebut akan digunakan prinsip falsifikasi Popper. Hasil perbandingan yang disajikan dalam makalah ini akan membantu mendapatkan wawasan mengenai kelebihan dan kekurangan dari masing-masing algoritma, serta akan membantu juga untuk memilih algoritma yang digunakan dalam desain sistem kendaraan otonom.   Kata Kunci : Kendaraan otonom, algoritma perencanaan jalur, lingkungan perkotaan, siklus keilmuan Khun, falsifikasi PopperAutonomous vehicles can be useful to improve safety, efficiency, accessibility and convenience of transportation. One important task that must be carried out by autonomous vehicles is to do path planning through a dynamic urban environment where there are other vehicles and pedestrians. According to Kuhn's philosophy, a science has its own life cycle. The science life cycle consists of the birth phase, the proof phase and the acceptance phase of the new science into what is called normal science. Likewise with the scientific development of path planning algorithms, path planning algorithms for autonomous vehicles are still being researched and developed by various researchers. New studies continue to be conducted in an effort to find pathway planning techniques that can be generally accepted, so that the algorithm will become a normal science. According to Popper's philosophy, each proposed algorithm must be tested using the principle of falsification to determine whether the proposed technique can eventually become a normal science or not. So this paper aims to provide an overview of the scientific cycle of planning autonomous vehicle lanes. This paper also aims to compare the latest research on path planning algorithms for autonomous vehicles in urban areas. In comparing these algorithms, the principle of Popper's falsification will be used. The comparison results presented in this paper will help gain insight into the advantages and disadvantages of each algorithm, and will also help to choose the algorithm used in the design of autonomous vehicle systems.   Keywords ­: Autonomous vehicles, path planning algorithms, urban environment, Khun’s scientific cycles, Popper’s falsificatio

An embarrassingly simple approach for visual navigation of forest environments

Navigation in forest environments is a challenging and open problem in the area of field robotics. Rovers in forest environments are required to infer the traversability of a priori unknown terrains, comprising a number of different types of compliant and rigid obstacles, under varying lighting and weather conditions. The challenges are further compounded for inexpensive small-sized (portable) rovers. While such rovers may be useful for collaboratively monitoring large tracts of forests as a swarm, with low environmental impact, their small-size affords them only a low viewpoint of their proximal terrain. Moreover, their limited view may frequently be partially occluded by compliant obstacles in close proximity such as shrubs and tall grass. Perhaps, consequently, most studies on off-road navigation typically use large-sized rovers equipped with expensive exteroceptive navigation sensors. We design a low-cost navigation system tailored for small-sized forest rovers. For navigation, a light-weight convolution neural network is used to predict depth images from RGB input images from a low-viewpoint monocular camera. Subsequently, a simple coarse-grained navigation algorithm aggregates the predicted depth information to steer our mobile platform towards open traversable areas in the forest while avoiding obstacles. In this study, the steering commands output from our navigation algorithm direct an operator pushing the mobile platform. Our navigation algorithm has been extensively tested in high-fidelity forest simulations and in field trials. Using no more than a 16 × 16 pixel depth prediction image from a 32 × 32 pixel RGB image, our algorithm running on a Raspberry Pi was able to successfully navigate a total of over 750 m of real-world forest terrain comprising shrubs, dense bushes, tall grass, fallen branches, fallen tree trunks, small ditches and mounds, and standing trees, under five different weather conditions and four different times of day. Furthermore, our algorithm exhibits robustness to changes in the mobile platform’s camera pitch angle, motion blur, low lighting at dusk, and high-contrast lighting conditions

Entwicklung eines Systems zur kontinuierlichen Integration für autonome Roboter

Autonome Roboter basieren auf dem komplexen Zusammenspiel vieler Sensoren. Dieses Zusammenspiel muss durch Software beobachtet und geregelt werden. Damit Roboter sich autonom - ohne ständige Überwachung - bewegen können, muss die Software ihre Funktion fehlerfrei ausführen. Um dies zu unterstützen, wurde im Rahmen dieser Arbeit ein Continuous Delivery-Prozess entwickelt. Dieser Prozess sieht vor, dass die Software des Roboters "ständig" und automatisiert geprüft wird. Ein besonderer Fokus lag dabei auf der Entwicklung eines Funktionstestsystems für Robotersoftware. Dieses Testsystem führt Testfälle aus, die auf Basis von Szenarien, bestehend aus einer Aufgabe, einem Kontext und mehreren Metriken, modelliert werden. Am Ende wurde der Nutzen des Testsystems durch Robotersoftware-Entwickler evaluiert.The behavior of an autonomous robot is determined by many sensors that scan the robot's environment. Data produced by these sensors needs to be accessed by complex software. Testing software is a very important aspect - especially when its target is an autonomous interacting device - to verify if the robot's software behaves in the right manner. Continuous Delivery is a process which tries to improve the procedure of writing and verifing the functionality of software. In this thesis, a test process - based on Continuous Delivery - is presented that enables developers to test their software automatically on a regular basis. The functionality of software for autonomous robots can be tested by providing a scenario consisting of a task, a context and some metrics. In the end, the whole process was evaluated by developers

Autonomous Behaviors With A Legged Robot

Over the last ten years, technological advancements in sensory, motor, and computational capabilities have made it a real possibility for a legged robotic platform to traverse a diverse set of terrains and execute a variety of tasks on its own, with little to no outside intervention. However, there are still several technical challenges to be addressed in order to reach complete autonomy, where such a platform operates as an independent entity that communicates and cooperates with other intelligent systems, including humans. A central limitation for reaching this ultimate goal is modeling the world in which the robot is operating, the tasks it needs to execute, the sensors it is equipped with, and its level of mobility, all in a unified setting. This thesis presents a simple approach resulting in control strategies that are backed by a suite of formal correctness guarantees. We showcase the virtues of this approach via implementation of two behaviors on a legged mobile platform, autonomous natural terrain ascent and indoor multi-flight stairwell ascent, where we report on an extensive set of experiments demonstrating their empirical success. Lastly, we explore how to deal with violations to these models, specifically the robot\u27s environment, where we present two possible extensions with potential performance improvements under such conditions