Advanced robots are desired to improve their reasoning ability in increasingly practical situations to quickly respond to urgent challenges such as search and rescue, public security, and autonomous driving. Advances in sensor technology and intelligent algorithms are accelerating the development of autonomous systems, allowing them to interact with dynamic environments more independently and intelligently, completing relatively complicated tasks. Nevertheless, the safety and security problems are yet to be comprehensively addressed. Currently, many efforts have been made to solve this problem by improving the unimodal perception ability of intelligent systems. Methods using surveillance cameras to collect large amounts of visual data and process them using deep networks. Various types of range scanner can sense and reconstruct the surrounding environment through precise distance measurements. However, in the above cases, there may be a lack of training data, fixed cameras cannot adequately and dynamically model the surroundings, unimodal analysis is hard to capture both appearance and depth information. Therefore, higher levels of perception and reasoning abilities are required to handle unexpected conditions and scenarios efficiently. In such cases, mobile robots should be fully aware of the dynamic environment, prioritize, and continuously analyze critical situations, share information, and collaboration under limited computing power and resources. Furthermore, considering the robots are operating alongside humans, reliable and interpretable robot behavior is highly preferred for more natural interactions. \n \nThis study aims to develop reasoning strategies for autonomous robots to improve robotic intelligence under different assumptions and conditions. The framework allows one or several robots to address safety or security problems in a targeted manner in partially known environments, where time, dynamics, and interactions play a significant role. The objective is to identify the unique individual whose activity, in space and time, appear different from the other people in the scene. The activities considered not only the motion pattern, but also the object associated with this person. Unlike traditional abnormal detection, the uniqueness reasoning in this work does not require a predefined expected pattern. A modular analysis is also applied to decompose the entire reasoning process into submodules such as perception, analysis, and inference, to achieve algorithmic flexibility and interpretability. Probabilistic modeling is used to associate spatial relationships between semantic labels and their temporal changes. The relationships are described in either instant spatial spaces (semantic-interaction feature) or spatiotemporal spaces (spatiotemporal feature), and it provides essential information for robot navigation, situation awareness, and task planning. Unlike existing research, this is the first time that such a mission is implemented on mobile robot platforms by using the probabilistic fusion of multimodal information. \n \nConsidering how evidence information is obtained and linked in the process of reasoning, we study the problem of uniqueness reasoning at three levels. The first level is called knowledge-based single robot uniqueness reasoning and runs on a single robot platform. At this level, we focus on a knowledge-based approach, where knowledge is provided in terms of how motion and an object associated with a person can be used to infer his/her uniqueness, as determined by human expertise. Because such a method has advantages in dealing with structured missions and with limited training data. Qualitative rules are applied to extract human activities from observations, and then the uniqueness of each person is judged by associating the prior knowledge with the performed activities. Experiments were conducted in indoor environments with various degrees of occlusion, and the experimental results showed the rationality and accuracy of the reasoning. \n \nHowever, though the knowledge-based method can successfully address specific tasks, it lacks flexibility and generality to adapt to different situations. The assumption of prior knowledge should be replaced by general analytical modeling. Thus, the second level is analytics-based single robot uniqueness reasoning. For one thing, it applies quantitative analysis to model human activities from observations rather than using qualitative rules. For another, instead of leveraging prior knowledge, the distribution of the features characterizing a human activity is constructed and the activity of an individual is analyzed within the distribution to determine if it is unique. Experiments were conducted in both indoor and urban outdoor environments. The experimental results demonstrated the rationality, accuracy, and efficiency of the reasoning. \n \nMoreover, since many applications require to work in an unstructured and large-scale environment as well as a long-term operation, the sensing and reasoning capabilities of a single robot are limited by occlusion, sensor failure, and narrow field of view. Robots are desired to share information and perform collaborative reasoning. Therefore, if say the first two levels are mainly using sensory data to implement the algorithm, the third level is more focused on applying the algorithm to the robot platform. The third level is analytics-based multi-robot uniqueness reasoning, in which a multi-robot system is adopted as the platform. By obtaining the relative pose between robots, local observations can be transformed and transmitted among the multi-robot system. Since it is a distributed system, each robot still first performs its local uniqueness reasoning in the case of long-distance and poor communication. Meanwhile, the system performs a re-judgment based on the shared global observation and then shares the judgment results back to each robot. Experiments were carried out in various unstructured environments and contained day and night situations. The experimental results showed the rationality, accuracy, and robustness of the reasoning. This is the first time that reasoning is categorized by considering the way of obtaining and linking evidence information. This study has potential applications in a variety of situations, and it can serve as a guide for further robotic motion planning and human-robot interaction.
49Towards a systematic understanding of the dynamic fate of polychlorinated biphenyls in indoor, urban and rural environments
Towards a systematic understanding of the dynamic fate of polychlorinated biphenyls in indoor, urban and rural environments
Read more4Transient stenotic-like occlusions as a possible mechanism for renovascular hypertension due to aneurysm
Transient stenotic-like occlusions as a possible mechanism for renovascular hypertension due to aneurysm
46Autonomous Sonar Navigation in Indoor, Unknown and Unstructured Environments
Autonomous Sonar Navigation in Indoor, Unknown and Unstructured Environments
50A type-2 fuzzy logic controller for autonomous mobile robots
There are many sources of uncertainty facing the fuzzy logic controller (FLC) for autonomous mobile robots navigating in changing and dynamic unstructured environments. The traditional type-1 FLC using precise type-1 fuzzy sets cannot fully handle such uncertainties. A type-2 fuzzy logic controller (FLC) using type-2 fuzzy sets can handle such uncertainties to produce a better performance. In this paper, we present the type-2 FLC and its novel application for the real time control of mobile robots. We used the type-2 FLC to implement different robotic behaviours on different robotic platforms in indoor and outdoor unstructured and challenging environments. The type-2 FLCs dealt with the uncertainties facing mobile robots in unstructured environments and resulted in a very good performance that outperformed the type-1 FLCs whilst using smaller rule bases.
Read more27Continuous indoor navigation with RFID and INS
Some navigation applications, such as the navigation of blind users, require that a continuous positioning is performed in combined outdoor urban and indoor environments with a certain positioning accuracy. For outdoor urban environments usually GNSS and dead reckoning are employed and has proven to be satisfactory. In indoor environments, however, absolute position determination with an accuracy in the range of 1 to 2 meters is still very challenging. For indoor location determination, a number of different positioning methods have been developed. In our approach, Radio Frequency Identification (RFID) has been selected and investigated. Using RFID most commonly cell-based positioning is performed. Apart from that we have also investigated trilateration and location fingerprinting based on signal strength measurements (i.e., RSSI short for Received Signal Strength Indication) from the RFID tags in the surrounding environment. The disadvantage of these two positioning methods, however, is the required calibration in the off-line or training phase to deduce ranges to the tags from the RSSI measurements in the case of trilateration or the establishment of the RSSI database at known locations throughout the building in the case of location fingerprinting. Therefore we have developed a new location method based on cell-based positioning which makes use of the measured RSSI in the on-line or positioning phase, i.e., the so-called time-based Cell-of-Origin (CoO). Two modifications have been implemented in comparison to common CoO and will be discussed in the paper. The new approach was then be tested in an indoor environment in an office building of the Vienna University of Technology. It could be seen that for a combined positioning of RFID time-based CoO and a low-cost MEMS-based INS positioning accuracies on the 1 to 2 meter level can be achieved. The different experiments performed in the test bed are described and discussed in this contribution.
Read more12Middleware for Multi-robot Systems
Recent advances in robotics technology have made it viable to assign complex tasks to large numbers of inexpensive robots. The robots as an ensemble form into a multi-robot system (MRS), which can be utilized for many applications where a single robot is not efficient or feasible. MRS can be used for a wide variety of application domains such as military, agriculture, smart home, disaster relief, etc. It offers higher scalability, reliability, and efficiency as compared to single-robot system. However, it is nontrivial to develop and deploy MRS applications due to many challenging issues such as distributed computation, collaboration, coordination, and real-time integration of robotic modules and services. To make the development of multi-robot applications easier, researchers have proposed various middleware architectures to provide programming abstractions that help in managing the complexity and heterogeneity of hardware and applications. With the help of middleware, an application developer can concentrate on the high-level logic of applications instead of worrying about low-level hardware and network details. In this chapter, we survey state of the art in both distributed MRS and middleware being used for developing their applications. We provide a taxonomy that can be used to classify the MRS middleware and analyze existing middleware functionalities and features. Our work will help researchers and developers in the systematic understanding of middleware for MRS and in selecting or developing the appropriate middleware based on the application requirements.
Read more5ROSANA: Robot for Social Interaction in Unstructured Dynamic Environments
Socially Interactive Robots must have the ability to interact with humans in a socially appropriate manner. Such a task requires a high level of perception of the robotic agent. A high degree of perception requires considerable computational power, something challenging to achieve in mobile robotics. Robots need to balance operating time, batteries (autonomy and weight), embedded sensors, and processing hardware. One way to overcome the mobile agents' perception limitations is to move the data processing capabilities to external processing units. In this article, we present the initial results of the ROSANA platform, a socially interactive robot for public environments capable of interacting with humans in the wild. ROSANA has a high level of visual perception, thanks to an architecture based on cloud services. We named this approach as Perception as a Service - PPaaS. Case studies exemplify the integration between the mobile agent and the cloud in the ROSANA architecture like detection, interaction with humans, and route planning considering humans in unstructured and dynamic environments.
Read more26Distributed Amorphous Ramp Construction in Unstructured Environments
We present a model of construction using iterative amorphous depositions and give a distributed algorithm to reliably build ramps in unstructured environments. The relatively simple local strategy for interacting with irregularly shaped, partially built structures gives rise robust adaptive global properties. We illustrate the algorithm in both the single robot and multi-robot case via simulation and describe how to solve key technical challenges to implementing this algorithm via a robotic prototype.KeywordsPoint PairDeposition MechanismDeposition LocationMultiple RobotSingle RobotThese keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.
Read moreMobility for a special configuration of a shape-shifting robot in urban terrain
The work in the complicated environment requires the robot to possess the better mobility, which is the basis that the robot can complete the task successfully and ensure itself security. To improve the adaptability in the unstructured environments, the aim is the mobility of a special d-configuration of AMOEBA-I, and the cooperative turning and cooperative negotiation of the d-configuration is presented. The capability on the turning and traversing the channel are analyzed theoretically. Also the relationship between the climb height and the location of center of gravity is discussed. The maximum climb heights respectively in the normal negotiation method and cooperative negotiation are compared theoretically. Finally, the experiment in the indoor and outdoor relic environment verifies the mobility of the d-configuration, which satisfies the requirement that the robot can perform the task in the unstructured environments.
Read more7Autonomous Intruder Detection Using a ROS-Based Multi-Robot System Equipped with 2D-LiDAR Sensors
The application of autonomous mobile robots in robotic security platforms is\nbecoming a promising field of innovation due to their adaptive capability of\nresponding to potential disturbances perceived through a wide range of sensors.\nResearchers have proposed systems that either focus on utilizing a single\nmobile robot or a system of cooperative multiple robots. However, very few of\nthe proposed works, particularly in the field of multi-robot systems, are\ncompletely dependent on LiDAR sensors for achieving various tasks. This is\nessential when other sensors on a robot fail to provide peak performance in\nparticular conditions, such as a camera operating in the absence of light. This\npaper proposes a multi-robot system that is developed using ROS (Robot\nOperating System) for intruder detection in a single-range-sensor-per-robot\nscenario with centralized processing of detections from all robots by our\ncentral bot MIDNet (Multiple Intruder Detection Network). This work is aimed at\nproviding an autonomous multi-robot security solution for a warehouse in the\nabsence of human personnel.\n
Read moreAn Improved Multi-Robot Task Allocation Algorithm Based on the Hungarian Algorithm
When it comes to Multi-Robot Systems (MRS), Multi-Robot System Task Allocation (MRTA) is one of the key issues in realizing multi-robot systems for real-world applications. In this paper, we propose an optimization-based Hungarian algorithm to solve the task allocation problem of multi-robot systems. Firstly, each robot is allowed to autonomously evaluate the task cost and send it to the central processor and an equilibrium compensation approach is proposed to compensate the constraints of the algorithm for the cost matrix to be square. Then a task cache pool is used to avoid the problem from degenerating into Single-Robot (SR) task allocation, and a prioritization strategy considering time is also designed to prevent the task points with lower generation values from lingering for a long time in the dynamic task allocation. Through simulation experiments, we demonstrate that the algorithm has a positive effect on the efficiency of assigned tasks in both dynamic and static situations.
Read more4Mobile robot navigation by image classification using a neural network
Mobile robot navigation by image classification using a neural network
30English
This paper presents a novel technique for the estimation of the height of an object using a single camera view. In the proposed method, the only information required is the knowledge about the pose of the camera with respect to the world (i.e., height and pitch angle of the camera with respect to the ground) and a vanishing point. In the developed theory, the focal length may also be known, but in the proposed experiments it has not been employed: an approximation for small pitch angles has been taken into account and the consequent committed error has been then analysed. The presented method gives accurate results for any object placed in unstructured environments, regardless of the relative distance from the camera. The method has been tested in a series of outdoor and indoor environments, and the experimental results are presented in this paper.
Read more250Sequential Updating of Projective and Affine Structure from Motion
A structure from motion algorithm is described which recovers structure and camera position, modulo a projective ambiguity. Camera calibration is not required, and camera parameters such as focal length can be altered freely during motion. The structure is updated sequentially over an image sequence, in contrast to schemes which employ a batch process. A specialisation of the algorithm to recover structure and camera position modulo an affine transformation is described, together with a method to periodically update the affine coordinate frame to prevent drift over time. We describe the constraint used to obtain this specialisation. Structure is recovered from image corners detected and matched automatically and reliably in real image sequences. Results are shown for reference objects and indoor environments, and accuracy of recovered structure is fully evaluated and compared for a number of reconstruction schemes. A specific application of the work is demonstrated—affine structure is used to compute free space maps enabling navigation through unstructured environments and avoidance of obstacles. The path planning involves only affine constructions.
Read more36A low‐cost and efficient autonomous row‐following robot for food production in polytunnels
In this paper, we present an automatic motion planner for agricultural robots that allows us to set up a robot to follow rows without having to explicitly enter waypoints. In most cases, when robots are used to cover large agricultural areas, they will need waypoints as inputs, either as premeasured coordinates in an outdoor environment, or as positions in a map in an indoor environment. This can be a tedious process as several hundreds or even thousands of waypoints will be needed for large farms. In particular, we find that in unstructured environments such as the ones found on farms, the need for waypoints increases. In this paper, we present an approach that enables robots to safely traverse not only between straight rows but also through curved rows without the need for any predetermined waypoints. Most types of infrastructure found in agriculture, such as polytunnels, are built on uneven terrain, thus containing a mix of straight and curved plant rows, for which traditional methods of row following will fail. Different from traditional approaches of row following that only consider straight‐line‐of‐sight rows, our approach identifies the rows on each side with the goal of staying in the middle of the rows, even if the rows are not straight. Waypoints are only needed on the very extreme of the rows, and these will be automatically generated by the system. With our approach, the robot can just be placed in the corner of the field and will then determine the trajectory without further input from the user. We thus obtain an approach that can reduce the installation time from potentially hours to just a matter of minutes. The final autonomous system is low cost and efficient for various tasks that requires moving between plant rows inside a polytunnel. Several experiments were performed and the robot demonstrates 1.4% position drift over 21 m of navigation path.
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