Feedback motion planning of unmanned underwater vehicles via random sequential composition

Ege, Emr
In this thesis, we propose a new motion planning method to robustly and computationally efficiently solve (probabilistic) coverage, path planning, and navigation problems for unmanned underwater vehicles (UUVs). Our approach is based on synthesizing two existing methodologies: sequential decomposition of dynamic behaviors and rapidly exploring random trees. The main motivation for this integrated solution is a robust feed-back based and computationally feasible motion planning and navigation algorithm that takes advantage of these two planning approaches. To illustrate the main approach and show the feasibility of the method, we first performed 2D simulations in MATLAB. We then implemented our method using a realistic fully dynamic 3D UUV simulation environment based on a platform built on the Robot Operating System (ROS)/Gazebo interface to test the overall performance and applicability for real applications. We also tested the robustness of the method under extreme environmental uncertainty (water current that is half the maximum speed of the UUV). 2D and realistic 3D simulation results indicate that our method can produce robust and computationally feasible solutions for a broad class of UUVs and Unmanned Surface Vehciles (USVs).


Feedback motion planning of unmanned surface vehicles via random sequential composition
Ege, Emre; Ankaralı, Mustafa Mert (SAGE Publications, 2019-08-01)
In this paper, we propose a new motion planning method that aims to robustly and computationally efficiently solve path planning and navigation problems for unmanned surface vehicles (USVs). Our approach is based on synthesizing two different existing methodologies: sequential composition of dynamic behaviours and rapidly exploring random trees (RRT). The main motivation of this integrated solution is to develop a robust feedback-based and yet computationally feasible motion planning algorithm for USVs. In ...
Nonlinear guidance and control of leader-follower UAV formations
Kumbasar, Sarper; Tekinalp, Ozan; Department of Aerospace Engineering (2015)
In this thesis work, two nonlinear guidance methods are proposed to control the autonomous formation flight: State Dependent Riccati Equation method and Lyapunov function method. Leader-Follower formation scheme is chosen and a pair of fighter aircrafts are used in simulations. One of them is chosen as the leader and it carries out the commanded maneuvers. Other aircraft is the follower and it follows the leader keeping the prescribed formation structure. Both aircraft models are nonlinear. In the inner loo...
Broadband One Way Propagation via Dielectric Waveguides with Unequal Effective Index
Oner, B. B.; Ustun, K.; KURT, HAMZA; OKYAY, Ali Kemal; Sayan, Gönül (2014-10-16)
We present an efficient approach for broad band one way propagation of light by parallel and unequal dielectric waveguides leading different effective phase shifts. Three dimensional numerical simulations show that 30% operating bandwidth is achieved.
Domain-Structured Chaos in a Hopfield Neural Network
Akhmet, Marat (World Scientific Pub Co Pte Lt, 2019-12-30)
In this paper, we provide a new method for constructing chaotic Hopfield neural networks. Our approach is based on structuring the domain to form a special set through the discrete evolution of the network state variables. In the chaotic regime, the formed set is invariant under the system governing the dynamics of the neural network. The approach can be viewed as an extension of the unimodality technique for one-dimensional map, thereby generating chaos from higher-dimensional systems. We show that the dis...
Robotic task planning using a backchaining theorem prover for multiplicative exponential first-order linear logic
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In this paper, we propose an exponential multiplicative fragment of linear logic to encode and solve planning problems efficiently in STRIPS domain, that we call the Linear Planning Logic (LPL). Linear logic is a resource aware logic treating resources as single use assumptions, therefore enabling encoding and reasoning of domains with dynamic state. One of the most important examples of dynamic state domains is robotic task planning, since informational or physical states of a robot include non-monotonic c...
Citation Formats
E. Ege, “Feedback motion planning of unmanned underwater vehicles via random sequential composition,” Thesis (Ph.D.) -- Graduate School of Natural and Applied Sciences. Electrical and Electronics Engineering., Middle East Technical University, 2019.