On Consistent Mapping in Distributed Environments using Mobile Sensors

The problem of robotic mapping, also known as simultaneous localization and mapping (SLAM), by a mobile agent for large distributed environments is addressed in this dissertation. This has sometimes been referred to as the holy grail in the robotics community, and is the stepping stone towards making a robot completely autonomous. A hybrid solution to the SLAM problem is proposed based on "first localize then map" principle. It is provably consistent and has great potential for real time application. It provides significant improvements over state-of-the-art Bayesian approaches by reducing the computational complexity of the SLAM problem without sacrificing consistency. The localization is achieved using a feature based extended Kalman filter (EKF) which utilizes a sparse set of reliable features. The common issues of data association, loop closure and computational cost of EKF based methods are kept tractable owing to the sparsity of the feature set. A novel frequentist mapping technique is proposed for estimating the dense part of the environment using the sensor observations. Given the pose estimate of the robot, this technique can consistently map the surrounding environment. The technique has linear time complexity in map components and for the case of bounded sensor noise, it is shown that the frequentist mapping technique has constant time complexity which makes it capable of estimating large distributed environments in real time. The frequentist mapping technique is a stochastic approximation algorithm and is shown to converge to the true map probabilities almost surely. The Hybrid SLAM software is developed in the C-language and is capable of handling real experimental data as well as simulations. The Hybrid SLAM technique is shown to perform well in simulations, experiments with an iRobot Create, and on standard datasets from the Robotics Data Set Repository, known as Radish. It is demonstrated that the Hybrid SLAM technique can successfully map large complex data sets in an order of magnitude less time than the time taken by the robot to acquire the data. It has low system requirements and has the potential to run on-board a robot to estimate large distributed environments in real time

A comparison study of search heuristics for an autonomous multi-vehicle air-sea rescue system

The immense power of the sea presents many life-threatening dangers to humans, and many fall foul of its unforgiving nature. Since manned rescue operations at sea (and indeed other search and rescue operations) are also inherently dangerous for rescue workers, it is common to introduce a level of autonomy to such systems. This thesis investigates via simulations the application of various search algorithms to an autonomous air-sea rescue system, which consists of an unmanned surface vessel as the main hub, and four unmanned helicopter drones. The helicopters are deployed from the deck of the surface vessel and are instructed to search certain areas for survivors of a stricken ship. The main aim of this thesis is to investigate whether common search algorithms can be applied to the autonomous air-sea rescue system to carry out an efficient search for survivors, thus improving the present-day air-sea rescue operations. Firstly, the mathematical model of the helicopter is presented. The helicopter model consists of a set of differential equations representing the translational and rotational dynamics of the whole body, the flapping dynamics of the main rotor blades, the rotor speed dynamics, and rotational transformations from the Earth-fixed frame to the body frame. Next, the navigation and control systems are presented. The navigation system consists of a line-of-sight autopilot which points each vehicle in the direction of its desired waypoint. Collision avoidance is also discussed using the concept of a collision cone. Using the mathematical models, controllers are developed for the helicopters: Proportional-Integral-Derivative (PID) and Sliding Mode controllers are designed and compared. The coordination of the helicopters is carried out using common search algorithms, and the theory, application, and analysis of these algorithms is presented. The search algorithms used are the Random Search, Hill Climbing, Simulated Annealing, Ant Colony Optimisation, Genetic Algorithms, and Particle Swarm Optimisation. Some variations of these methods are also tested, as are some hybrid algorithms. As well as this, three standard search patterns commonly used in maritime search and rescue are tested: Parallel Sweep, Sector Search, and Expanding Square. The effect of adding to the objective function a probability distribution of target locations is also tested. This probability distribution is designed to indicate the likely locations of targets and thus guide the search more effectively. It is found that the probability distribution is generally very beneficial to the search, and gives the search the direction it needs to detect more targets. Another interesting result is that the local algorithms perform significantly better when given good starting points. Overall, the best approach is to search randomly at the start and then hone in on target areas using local algorithms. The best results are obtained when combining a Random Search with a Guided Simulated Annealing algorithm