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.

No description available.

No description available.

Estimation theory is a branch of statistics and probability that derives information about random variables based on known information. In process engineering, state estimation is used for a variety of purposes, such as: soft sensing, digital filter design, model predictive control and performance monitoring. In literature, there exist numerous estimation algorithms. In this study, we provide guidelines for choosing the appropriate estimator for a system under consideration. Various estimators are compared and their advantages and disadvantages are highlighted. This has been done through case studies which use examples from process engineering. We also address certain robustness issues of application of estimation techniques to chemical processes. Choice of estimator in case of high plant-model mismatch has also been discussed. The study is restricted to unconstrained nonlinear estimators. / Process Control

The effects of wind farm installation on the conventional monostatic radar operation have been investigated in previous studies. The interference on radar operation is due to the complex scattering characteristics from the wind turbine structure. This research considers alternative approach for studying and potentially mitigating these negative impacts by adapting the multistatic radar system technique. This radar principle is well known and it is attracting research interest recently, but has not been applied in modelling the wind farm interference on multistatic radar detection and tracking of multiple targets. The research proposes two areas of novelties. The first area includes the simulation tool development of multistatic radar operation near a wind farm environment. The second area includes the adaptation of Range-Only target detection approach based on mathematical and/or statistical methods for target detection and tracking, such as Interval Analysis and Particle Filter. These methods have not been applied against such complex detection scenario of large number of targets within a wind farm environment. Range-Only target detection approach is often considered to achieve flexibility in design and reduction in cost and complexity of the radar system. However, this approach may require advanced signal processing techniques to effectively associate measurements from multiple sensors to estimate targets positions. This issue proved to be more challenging for the complex detection environment of a wind farm due to the increase in number of measurements from the complex radar scattering of each turbine. The research conducts a comparison between Interval Analysis and Particle Filter. The comparison is based on the performance of the two methods according to three aspects; number of real targets detected, number of ghost targets detected and the accuracy of the estimated detections. Different detection scenarios are considered for this comparison, such as single target detection, wind farm detection, and ultimately multiple targets at various elevations within a wind farm environment.

This dissertation describes the development of a method for simultaneous localization and mapping (SLAM)algorithm which is suitable for high dimensional vehicle and map states. The goal of SLAM is to be able to navigate autonomously without the use of external aiding sources for vehicles. SLAM's combination of the localization and mapping problems makes it especially difficult to solve accurately and efficiently, due to the shear size of the unknown state vector. The vehicle states are typically constant in number while the map states increase with time. The increasing number of unknowns in the map state makes it impossible to use traditional Kalman filters to solve the problem- the covariance matrix grows too large and the computational complexity becomes too overwhelming. Particle filters have proved beneficial for alleviating the complexity of the SLAM problem for low dimensional vehicle states, but there is little work done for higher dimensional states. This research provides an Gaussian Mixture Model based alternative to the particle filtering SLAM methods, and provides a further partition that alleviates the vehicle state dimensionality problem with the standard particle filter. A SLAM background and basic theory is provided in the early chapters. A description of the new algorithm is provided in detail. Simulations are run demonstrating the performance of the algorithm, and then an aerial SLAM platform is developed for further testing. The aerial SLAM system uses a RGBD camera as well as an inertial measurement unit to collect SLAM data, and the ground truth is captured using an indoor optical motion capture system. Details on image processing and specifics on the inertial integration are provided. The performance of the algorithm is compared to a state of the art particle filtering based SLAM algorithm, and the results are discussed. Further work performed while working in the industry is described, which involves SLAM for adding transponders onto long-baseline acoustic arrays and stereo-inertial SLAM for 3D reconstruction of deep-water sub-sea structures. Finally, a neatly packaged production line version of the stereo-inertial SLAM system presented.

This paper presents a new method for constructing 2D maps of enclosed un- derwater structures using an underwater robot equipped with only a 2D scanning sonar, compass and depth sensor. In particular, no motion model or odometry is used. To accomplish this, a two step offline SLAM method is applied to a set of stationary sonar scans. In the first step, the change in position of the robot between each consecutive pair of stationary sonar scans is estimated using a particle filter. This set of pair wise relative scan positions is used to create an estimate of each scan’s position within a global coordinate frame using a weighted least squares fit that optimizes consistency between the relative positions of the entire set of scans. In the second step of the method, scans and their estimated positions act as inputs to a mapping algorithm that constructs 2D octree-based evidence grid maps of the site. This work is motivated by a multi-year archaeological project that aims to construct maps of ancient water storage systems, i.e. cisterns, on the islands of Malta and Gozo. Cisterns, wells, and water galleries within fortresses, churches and homes operated as water storage systems as far back as 2000 B.C. Using a Remotely Operated Vehicle (ROV) these water storage systems located around the islands were explored while collecting video, still images, sonar, depth, and compass measurements. Data gathered from 5 different expeditions has produced maps of over 90 sites. Presented are results from applying the new mapping method to both a swimming pool of known size and to several of the previously unexplored water storage systems.

For this thesis the algorithm of a Particle Filter has been partly implemented on a Zynq All-programmable SoC allowing for speed ups of up to 12 times in parts of the code. The implementation is used to track a robot in an environment known by a 2D map. Since some parts have not been implemented in hardware lots of communication is done between the hardware and software parts of the code which creates a performance bottleneck.

No description available.

<p> Particle filters are being used in a number of state estimation applications because of their capability to effectively solve nonlinear and non-Gaussian problems. However, they have high computational requirements and this becomes even more so in the case of multitarget tracking, where data association is the bottleneck. In order to perform data association and estimation jointly, typically an augmented state vector, whose dimensions depend on the number of targets, is used in particle filters. As the number of targets increases, the corresponding computational load increases exponentially. In this case, parallelization is a possibility for achieving real-time feasibility in large-scale multitarget tracking applications. In this paper, we present an optimization-based scheduling algorithm that minimizes the total computation time for the bus-connected heterogeneous primary-secondary architecture. This scheduler is capable of selecting the optimal number of processors from a large pool of secondary processors and mapping the particles among the selected ones. A new distributed resampling algorithm suitable for parallel computing is also proposed. Furthermore, a less communication intensive parallel implementation of the particle filter without sacrificing tracking accuracy using an efficient load balancing technique, in which optimal particle migration among secondary processors is ensured, is presented. Simulation results demonstrate the tracking effectiveness of the new parallel particle filter and the speedup achieved using parallelization.</p> / Thesis / Master of Applied Science (MASc)