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<div>We describe a new task called The Amazing Race: Robot Edition. In this task, the robot is placed in a real, unknown environment, without a map, and asked to find a designated location. It will need to explore its surroundings, find and approach people, engage them in a dialogue to obtain directions to the goal, and follow those directions to the hallway with the goal. We describe and implement a variety of robotic behaviors that performs each of these functions. We test these in the real world in test environments that were distinct from the training environments where we developed our methods and trained our models. Additionally, these test environments were completely unmodified and reflect the state of the real world.</div><div>First, we describe how our robotic system solves this problem where the environment is constrained to a single floor or a single building. We demonstrate that we are able to find a goal location in never-before-seen environments. Next, we describe a machine-learned approach to the dialogue and components of our system to make it more robust to the diversity and noisiness of navigational instructions someone may provide.</div>

Disasters especially from natural phenomena are inevitable. The affected areas recover from the aftermath of a natural disaster with the support from various agents participating in humanitarian operations. There are several domains of the operation, and distributing relief aids is one. For distribution, satisfying the demand for relief aid is important since the condition of the environment is unfavorable to affected people and resources needed for the victim’s life are scarce. However, it becomes problematic when the logistic agents believed to be work properly fail to deliver the emergency goods because of the capacity loss induced from the environment after disasters. This study was proposed to address the problem of logistic agents’ unexpected incapacity which hinders scheduled distribution. The decrease in a logistic agent’s supply capability delays<br>achieving the goal of supplying required relief goods to the affected people which further endangers them. Regarding the stated problem, this study explored the importance of<br>setting the profile of logistic agents that can survive for certain duration of times. Therefore, this research defines the “survivability” and the profile of logistic agents for surviving the last mile distribution through agent based modeling and simulation. Through simulations, this study uncovered that the logistic exercise could gain survivability with the certain number and organization of logistic agents. Proper formation of organization establish the logistics’ survivability, but excessive size can threaten the survivability.

<div>Path planning is the generation of paths for the robots to navigate based on some constraints. Coverage path planning is where the robots needs to cover an entire work space for various applications like sensing, inspection and so on. Though there are numerous works on 2D coverage and also coverage using a single robot, the works on 3D coverage and multi-agents are very limited. This thesis makes several contributions to multi-agent path planning for 3D structures.</div><div><br></div><div>Motivated by the inspection of 3D structures, especially airplanes, we present a 3D coverage path planning algorithm for a multi-UAV system. We propose a unified method, where the viewpoints selection and path generation are done simultaneously for multiple UAVs. The approach is scalable in terms of number of UAVs and is also robust to models with variations in geometry. The proposed method also distributes the task uniformly amongst the multiple UAVs involved and hence making the best use of the robotics team. The uniform task distribution is an integral part of the path planner. Various performance measures of the paths generated in terms of coverage, path length and time also has been presented. </div>

There is currently a wide variety of six degree-of-freedom (6-DOF) motion capture technologies available. However, these systems tend to be very expensive and thus cost prohibitive. A software system was developed to provide 6-DOF motion capture using the Nintendo Wii remote’s (wiimote) sensors, an infrared beacon, and a novel hierarchical linear-quaternion Kalman filter. The software is made freely available, and the hardware costs less than one hundred dollars. Using this motion capture software, a robotic control system was developed to teleoperate a 6-DOF robotic manipulator via the operator’s natural hand movements. The teleoperation system requires calibration of the wiimote’s infrared cameras to obtain an estimate of the wiimote’s 6-DOF pose. However, since the raw images from the wiimote’s infrared camera are not available, a novel camera-calibration method was developed to obtain the camera’s intrinsic parameters, which are used to obtain a low-accuracy estimate of the 6-DOF pose. By fusing the low-accuracy estimate of 6-DOF pose with accelerometer and gyroscope measurements, an accurate estimation of 6-DOF pose is obtained for teleoperation. Preliminary testing suggests that the motion capture system has an accuracy of less than a millimetre in position and less than one degree in attitude. Furthermore, whole-system tests demonstrate that the teleoperation system is capable of controlling the end effector of a robotic manipulator to match the pose of the wiimote. Since this system can provide 6-DOF motion capture at a fraction of the cost of traditional methods, it has wide applicability in the field of robotics and as a 6-DOF human input device to control 3D virtual computer environments.

There is currently a wide variety of six degree-of-freedom (6-DOF) motion capture technologies available. However, these systems tend to be very expensive and thus cost prohibitive. A software system was developed to provide 6-DOF motion capture using the Nintendo Wii remote’s (wiimote) sensors, an infrared beacon, and a novel hierarchical linear-quaternion Kalman filter. The software is made freely available, and the hardware costs less than one hundred dollars. Using this motion capture software, a robotic control system was developed to teleoperate a 6-DOF robotic manipulator via the operator’s natural hand movements. The teleoperation system requires calibration of the wiimote’s infrared cameras to obtain an estimate of the wiimote’s 6-DOF pose. However, since the raw images from the wiimote’s infrared camera are not available, a novel camera-calibration method was developed to obtain the camera’s intrinsic parameters, which are used to obtain a low-accuracy estimate of the 6-DOF pose. By fusing the low-accuracy estimate of 6-DOF pose with accelerometer and gyroscope measurements, an accurate estimation of 6-DOF pose is obtained for teleoperation. Preliminary testing suggests that the motion capture system has an accuracy of less than a millimetre in position and less than one degree in attitude. Furthermore, whole-system tests demonstrate that the teleoperation system is capable of controlling the end effector of a robotic manipulator to match the pose of the wiimote. Since this system can provide 6-DOF motion capture at a fraction of the cost of traditional methods, it has wide applicability in the field of robotics and as a 6-DOF human input device to control 3D virtual computer environments.

The thesis introduces an innovative UAV detection system. The commercial UAV market is booming. Meanwhile, the risks and threats from improper UAV usages are also booming. CUAS is to protect the public and facilities. The problem is a lack of an intelligent platform which can adapt many sensors for UAV detection. The hypothesis is that, the system can track the UAV’s movement by applying the multi-agent system (MAS) to UAV route track. The experiment proves that the multi-agent system benefits for the UAV track. <br>

<div>Enhancing the capability to control a tremendous range of physical actuators and sensors, combined with wireless technology and the Internet of Things (IoT), apparel technologies play a significant role in supporting safe, comfortable and healthy living, observing each customer’s conditions. Since apparel technologies have advanced to enable humans to work as a team with the clothing they wear, the interaction between a human and apparel is further enhanced with the introduction of sensors, wireless network, and artificially intelligent techniques. A variety of wearable technologies have been developed and spread to meet the needs of customers, however, some wearable devices are considered as non-practical tech-oriented, not consumer-oriented.</div><div>The purpose of this research is to develop an apparel system which integrates intelligent autonomous agents, human-based sensors, wireless network protocol, mobile application management system and a zipper robot. This research is an augmentation to the existing research and literature, which are limited to the zipping and unzipping process without much built in intelligence. This research is to face the challenges of the elderly and people with self-care difficulties. The intent is to provide a scientific path for intelligent zipper robot systems with potential, not only to help people, but also to be commercialized.</div><div>The research develops an intelligent system to control of zippers fixed on garments, based on the profile and desire of the human. The theoretical and practical elements of developing small, integrated, intelligent zipper robots that interact with an application by using a lightweight MQTT protocol for application in the daily lives of diverse populations of people with physical challenges. The system functions as intelligent automatized garment to ensure users could positively utilize a zipper robot device to assist in putting on garments which also makes them feel comfortable wearing and interacting with the system. This research is an approach towards the “future of fashion”, and the goal is to incentivize and inspire others to develop new instances of wearable robots and sensors that help people with specific needs to live a better life.</div>

A lot of today's data is generated incrementally over time by a large variety of producers. This data ranges from quantitative sensor observations produced by robot systems to complex unstructured human-generated texts on social media. With data being so abundant, making sense of these streams of data through reasoning is challenging. Reasoning over streams is particularly relevant for autonomous robotic systems that operate in a physical environment. They commonly observe this environment through incremental observations, gradually refining information about their surroundings. This makes robust management of streaming data and its refinement an important problem. Many contemporary approaches to stream reasoning focus on the issue of querying data streams in order to generate higher-level information by relying on well-known database approaches. Other approaches apply logic-based reasoning techniques, which rarely consider the provenance of their symbolic interpretations. In this thesis, we integrate techniques for logic-based spatio-temporal stream reasoning with the adaptive generation of the state streams needed to do the reasoning over. This combination deals with both the challenge of reasoning over streaming data and the problem of robustly managing streaming data and its refinement. The main contributions of this thesis are (1) a logic-based spatio-temporal reasoning technique that combines temporal reasoning with qualitative spatial reasoning; (2) an adaptive reconfiguration procedure for generating and maintaining a data stream required to perform spatio-temporal stream reasoning over; and (3) integration of these two techniques into a stream reasoning framework. The proposed spatio-temporal stream reasoning technique is able to reason with intertemporal spatial relations by leveraging landmarks. Adaptive state stream generation allows the framework to adapt in situations in which the set of available streaming resources changes. Management of streaming resources is formalised in the DyKnow model, which introduces a configuration life-cycle to adaptively generate state streams. The DyKnow-ROS stream reasoning framework is a concrete realisation of this model that extends the Robot Operating System (ROS). DyKnow-ROS has been deployed on the SoftBank Robotics NAO platform to demonstrate the system's capabilities in the context of a case study on run-time adaptive reconfiguration. The results show that the proposed system – by combining reasoning over and reasoning about streams – can robustly perform spatio-temporal stream reasoning, even when the availability of streaming resources changes. / <p>The series name <em>Linköping Studies in Science and Technology Licentiate Thesis</em> is inocorrect. The correct series name is <em>Linköping Studies in Science and Technology Thesis</em>.</p> / NFFP6 / CENIIT