The purpose of this thesis is to create an indoor rescue scenario with multiple self-localizing robots that are able to collaborate for a victim search. Victims are represented by RFID tags and detecting them combined with an accurate enough location data is considered as a successful finding. This setup is created for use in a laboratory assignment at Linköping University. We consider the indoor localization problem by trying to use as few sensors as possible and implement three indoor localization methods - odometry based, passive RFID based, and our approach by fusing both sensor data with particle filter.The Results show that particle filter based localization performs the best in comparison to the two other implemented methods and satisfies the accuracy requirements stated for the scenario. The victim search problem is solved by an ant mobility (pheromone-based) approach which integrates our localization method and provides a collaborative navigation through the rescue area. The purpose of the pheromone mobility approach is to achieve a high coverage with an acceptable resource consumption.Experiments show that area is covered with approximately 30-40% overhead in traveled distance comparing to an optimal path.

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As robots continue to enter people's spaces and environments, it will be increasingly important to have effective interfaces for interaction and communication. One such aspect of this communication is people's awareness of the robot's actions and state. We believe that using high-level state representations, as a peripheral awareness channel, will help people to be aware of the robotic states in an easy to understand way. For example, when a robot is boxed in a small area, it can suggest a negative robot state (e.g., not willing to work in a small area as it cannot clean the entire room) by appearing unhappy to people. To investigate this, we built a robotic dog tail prototype and conducted a study to investigate how different tail motions (based on several motion parameters, e.g., speed) influence people’s perceptions of the robot. The results from this study formed design guidelines that Human-Robot Interaction (HRI) designers can leverage to convey robotic states. Further, we evaluated our overall approach and tested these guidelines by conducting a design workshop with interaction designers where we asked them to use the guidelines to design tail behaviors for various robotic states (e.g., looking for dirt) for robots working in different environments (e.g., domestic service). Results from this workshop helped in improving the confusing parts in our guidelines and making them easy to use by the designers. In conclusion, this thesis presents a set of solidified design guidelines that can be leveraged by HRI designers to convey the states of robots in a way that people can readily understand when and how to interact with them.

5th IFAC Symposium on Mechatronic Systems, Marriott Boston Cambridge, Cambridge, MA, USA, Sept 13-15, 2010

With the advancement of automated control systems in the past few years, the focus has also been moved to safer, more reliable systems with less harmful effects on the environment. With increased job mobility, less experienced operators could cause more damage by incorrect identification and handling of plant faults, often causing faults to progress to failures. The development of an automated fault detection and diagnostic system can reduce the number of failures by assisting the operator in making correct decisions. By providing information such as fault type, fault severity, fault location and cause of the fault, it is possible to do scheduled maintenance of small faults rather than unscheduled maintenance of large faults. Different fault detection and diagnostic systems have been researched and the best system chosen for implementation as a distributed fault detection and diagnostic architecture. The aim of the research is to develop a distributed fault detection and diagnostic system. Smaller building blocks are used instead of a single system that attempts to detect and diagnose all the faults in the plant. The phases that the research follows includes an in-depth literature study followed by the creation of a simplified fault detection and diagnostic system. When all the aspects concerning the simple model are identified and addressed, an advanced fault detection and diagnostic system is created followed by an implementation of the fault detection and diagnostic system on a physical system. / Thesis (M.Ing. (Computer and Electronic Engineering))--North-West University, Potchefstroom Campus, 2009.

In Wireless Sensor and Robot Networks (WSRNs), static sensors report event information to one of the robots. In the k nearest neighbour query processing problem in WSRNs, the robot receives event report needs to find exact k nearest robots (KNN) to react to the event, among those connected to it. We are interested in localized solutions, which avoid message flooding to the whole network. Several existing methods restrict the search within a predetermined boundary. Some network density-based estimation algorithms were proposed but they either result in large message transmission or require the density information of the whole network in advance which is complex to implement and lacks robustness. Algorithms with tree structures lead to the excessive energy consumption and large latency caused by structural construction. Itinerary based approaches generate large latency or unsatisfactory accuracy. In this thesis, we propose a new method to estimate a search boundary, which is a circle centred at the query point. Two algorithms are presented to disseminate the message to robots of interest and aggregate their data (e.g. the distance to query point). Multiple Auction Aggregation (MAA) is an algorithm based on auction protocol, with multiple copies of query message being disseminated into the network to get the best bidding from each robot. Partial Depth First Search (PDFS) attempts to traverse all the robots of interest with a query message to gather the data by depth first search. This thesis also optimizes a traditional itinerary-based KNN query processing method called IKNN and compares this algorithm with our proposed MAA and PDFS algorithms. The experimental results followed indicate that the overall performance of MAA and PDFS outweighs IKNN in WSRNs.

With the advancement of automated control systems in the past few years, the focus has also been moved to safer, more reliable systems with less harmful effects on the environment. With increased job mobility, less experienced operators could cause more damage by incorrect identification and handling of plant faults, often causing faults to progress to failures. The development of an automated fault detection and diagnostic system can reduce the number of failures by assisting the operator in making correct decisions. By providing information such as fault type, fault severity, fault location and cause of the fault, it is possible to do scheduled maintenance of small faults rather than unscheduled maintenance of large faults. Different fault detection and diagnostic systems have been researched and the best system chosen for implementation as a distributed fault detection and diagnostic architecture. The aim of the research is to develop a distributed fault detection and diagnostic system. Smaller building blocks are used instead of a single system that attempts to detect and diagnose all the faults in the plant. The phases that the research follows includes an in-depth literature study followed by the creation of a simplified fault detection and diagnostic system. When all the aspects concerning the simple model are identified and addressed, an advanced fault detection and diagnostic system is created followed by an implementation of the fault detection and diagnostic system on a physical system. / Thesis (M.Ing. (Computer and Electronic Engineering))--North-West University, Potchefstroom Campus, 2009.

This thesis focuses on implementing a program, using Python and the symbolic package SymPy, to evaluate balancing of a humanoid robot modelled as inverted pendulums. The balancing algorithm used to evaluate the program is the feedback controller LQR. The program has successfully implemented a working LQR algorithm together with features such as underactuation and a tilting plane as disturbance. We have shown that the energy is conserved for the falling pendulums and that it is possible to predict the behavior for certain parameter values of the pendulums, thus confirming that the program is working correctly. Furthermore we have shown that a fully-actuated system is more controllable than an under-actuated system, and for each actuator that is removed, the system becomes less controllable. Finally we discuss the program performance where some concern is given toward the seemingly poor execution time of the program. The program has been tested for up to five pendulums with successful results. Most of the results however, are revolving around three pendulum systems.