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Algorithms for Motion Planning and Target Capturing

By Mangal Kothari


This thesis addresses the development and implementation of algorithms for unmanned air\ud vehicles (UAVs). With advances in technology, it is relatively easy to manufacture and operate\ud UAVs that are particularly useful for dull, dirty and dangerous operations. The success of such\ud autonomous missions depends heavily on the planning algorithms used. A key consideration\ud in the thesis is the path planning problem for single and multiple UAV systems in obstacle rich\ud environments in the presence of uncertainty. Recently, rapidly-exploring random trees (RRTs)\ud have been applied to find feasible trajectories quickly in complex motion planning problems.\ud We use RRTs to construct trees of kinematically feasible trajectories made of waypaths, and\ud feasibility is evaluated by checking for collisions with the predicted trajectories. When there\ud are uncertainties acting on the system, we can identify probabilistic feasible paths by growing\ud trees of state distributions and ensuring that the probability of constraint violation is below a\ud pre-defined value. In addition to this, a guidance law is designed combining a pursuit law with\ud a line-of-sight law, to track the path generated by the path planner with minimum deviation.\ud In the penultimate chapter, an application is presented where a multi-UAV system captures a\ud more capable target by forming a target centred formation around it. The approach combines a\ud consensus algorithm with a controller to develop a robust distributed control law for formation\ud control. Under certain conditions, it is shown that a set of UAVs can form a target centred\ud formation even when target information is not known. The effectiveness of this algorithm is\ud demonstrated using numerical results. In the appendix, a decentralised scheme is described\ud for target tracking using consensus theory in conjunction with a data fusion algorithm which\ud guarantees perfect fault detection and isolation. This scheme was developed by the Leicester\ud team. As part of this thesis the theoretical algorithm was tested experimentally on a set of real\ud robots

Publisher: University of Leicester
Year: 2011
OAI identifier:

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