Bayesian Optimisation for Planning in Dynamic Environments

This thesis addresses the problem of trajectory planning for monitoring extreme values of an environmental phenomenon that changes in space and time. The most relevant case study corresponds to environmental monitoring using an autonomous mobile robot for air, water and land pollution monitoring. Since the dynamics of the phenomenon are initially unknown, the planning algorithm needs to satisfy two objectives simultaneously: 1) Learn and predict spatial-temporal patterns and, 2) find areas of interest (e.g. high pollution), addressing the exploration-exploitation trade-off. Consequently, the thesis brings the following contributions: Firstly, it applies and formulates Bayesian Optimisation (BO) to planning in robotics. By maintaining a Gaussian Process (GP) model of the environmental phenomenon the planning algorithms are able to learn the spatial and temporal patterns. A new family of acquisition functions which consider the position of the robot is proposed, allowing an efficient trajectory planning. Secondly, BO is generalised for optimisation over continuous paths, not only determining where and when to sample, but also how to get there. Under these new circumstances, the optimisation of the acquisition function for each iteration of the BO algorithm becomes costly, thus a second layer of BO is included in order to effectively reduce the number of iterations. Finally, this thesis presents Sequential Bayesian Optimisation (SBO), which is a generalisation of the plain BO algorithm with the goal of achieving non-myopic trajectory planning. SBO is formulated under a Partially Observable Markov Decision Process (POMDP) framework, which can find the optimal decision for a sequence of actions with their respective outcomes. An online solution of the POMDP based on Monte Carlo Tree Search (MCTS) allows an efficient search of the optimal action for multistep lookahead. The proposed planning algorithms are evaluated under different scenarios. Experiments on large scale ozone pollution monitoring and indoor light intensity monitoring are conducted for simulated and real robots. The results show the advantages of planning over continuous paths and also demonstrate the benefit of deeper search strategies using SBO

Swarming Reconnaissance Using Unmanned Aerial Vehicles in a Parallel Discrete Event Simulation

Current military affairs indicate that future military warfare requires safer, more accurate, and more fault-tolerant weapons systems. Unmanned Aerial Vehicles (UAV) are one answer to this military requirement. Technology in the UAV arena is moving toward smaller and more capable systems and is becoming available at a fraction of the cost. Exploiting the advances in these miniaturized flying vehicles is the aim of this research. How are the UAVs employed for the future military? The concept of operations for a micro-UAV system is adopted from nature from the appearance of flocking birds, movement of a school of fish, and swarming bees among others. All of these natural phenomena have a common thread: a global action resulting from many small individual actions. This emergent behavior is the aggregate result of many simple interactions occurring within the flock, school, or swarm. In a similar manner, a more robust weapon system uses emergent behavior resulting in no weakest link because the system itself is made up of simple interactions by hundreds or thousands of homogeneous UAVs. The global system in this research is referred to as a swarm. Losing one or a few individual unmanned vehicles would not dramatically impact the swarms ability to complete the mission or cause harm to any human operator. Swarming reconnaissance is the emergent behavior of swarms to perform a reconnaissance operation. An in-depth look at the design of a reconnaissance swarming mission is studied. A taxonomy of passive reconnaissance applications is developed to address feasibility. Evaluation of algorithms for swarm movement, communication, sensor input/analysis, targeting, and network topology result in priorities of each model\u27s desired features. After a thorough selection process of available implementations, a subset of those models are integrated and built upon resulting in a simulation that explores the innovations of swarming UAVs