Autonomous and manual driving of a multiple turret system in extreme environment

Yerlikaya, Ümit
In this thesis, firstly two methods are developed to obtain multi-dimensional configuration space for path planning problems. In typical cases, the path planning problems are solved directly in the 3-D workspace. However, this method is inefficient in handling the robots with various geometrical and mechanical restrictions. To overcome these difficulties, path planning may be formalized and solved in a new space which is called configuration space. In the first method, the point clouds of all the bodies of the system and interaction of them are used. The second method is performed via using the clearance function of simulation software where the minimum distances between surfaces of bodies are simultaneously measured. A sample 4-D configuration space of a double-turret system is obtained in these two methods. As a result of this, the difference between these two ways is about 1% which depends on the point cloud density. Then, Instead of using the tedious process of manual positioning, an off-line path planning algorithm has been developed for military turrets to improve their accuracy and efficiency. In the scope of this research, an algorithm is proposed to search a path in three different types of configuration spaces which are rectangular, circular and torus shaped by providing three converging options named as fast, medium and optimum depending on the application. With the help of the proposed algorithm, 4-dimensional (D) path planning problem was realized as 2-D + 2-D by using 6 sequences and their options. The results obtained were simulated and no collision was observed between any bodies in these three options. Finally, with the help of new collision avoidance algorithm, all types of turrets can be driven more efficiently and safely according to the specified speed, acceleration and jerk limits. Since all possible worst scenarios are examined one by one, it is guaranteed that the algorithm provides collision free motion in both simulations and real-time tests. A configuration space where worst scenarios can occur is created for the performance measurement of the algorithm, and the same space is used in all tests. As a result of these tests, it is shown that there is no collision. Finally, by adding cascade position control loop, the departure from the starting point to the desired target point is achieved without any collision. The most important feature that distinguishes this algorithm from others is both speed and position can be controlled and during transition phase, the target point can be changed instantly. In addition, no target position is required for the system to move collision-free, only axis speed commands are sufficient. Since the algorithm does not intervene in the speed and torque loops in contrast to potential field-based methods, it can be added to ready-to-use systems by manipulating only the speed references.


RG-Trees: Trajectory-Free Feedback Motion Planning Using Sparse Random Reference Governor Trees
Gölbol, Ferhat; Ankaralı, Mustafa Mert; Saranlı, Afşar (2018-10-05)
Sampling based methods resulted in feasible and effective motion planning algorithms for high dimensional configuration spaces and complex environments. A vast majority of such algorithms as well as their application rely on generating a set of open-loop trajectories first, which are then tracked by feedback control policies. However, controlling a dynamic robot to follow the planned path, while respecting the spatial constraints originating from the obstacles is still a challenging problem. There are some ...
UNERI, M; Erkmen, Aydan Müşerref (1992-08-13)
The sensorimotor learning phase is considered in the context of a collision-free dynamic path planning architecture for a robot manipulator in an uncertain task environment. The robot dynamics are effected by a vector field generated by partially known attractors and repellers, with uncertainty represented by entropy measures. The joint space is transformed into a cell space, and sensor uncertainty is taken proportional to cell size. The authors use a cell-to-cell mapping concept to develop a simple cell ma...
Intelligent design objects applied to the spatial allocation problem
Zaratiegui Fernandez, Javier Ignacio; Sorguç, Arzu; Computational Design and Fabrication Technologies in Department of Architecture (2014)
This thesis approaches the spatial allocation problem as a multi-objective optimization problem. It proposes the use of Intelligent Design Objects (IDO) model to help designers with this task. Solutions are generated and evaluated, according the user defined criteria. Iterative improvement is proposed as a help to visualize candidate solutions and conceive the desired spatial relations. By defining the criteria and rating it numerically, both designer and client are able compare the solutions obtained. The ...
Feedback motion planning of unmanned underwater vehicles via random sequential composition
Ege, Emr; Orguner, Umut; Department of Electrical and Electronics Engineering (2019)
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 ...
Single and multi agent real-time path search in dynamic and partially observable environments
Ündeğer, Çağatay; Polat, Faruk; Department of Computer Engineering (2007)
In this thesis, we address the problem of real-time path search in partially observable grid worlds, and propose two single agent and one multi-agent search algorithm. The first algorithm, Real-Time Edge Follow (RTEF), is capable of detecting the closed directions around the agent by analyzing the nearby obstacles, thus avoiding dead-ends in order to reach a static target more effectively. We compared RTEF with a well-known algorithm, Real-Time A* (RTA*) proposed by Korf, and observed significant improvemen...
Citation Formats
Ü. Yerlikaya, “Autonomous and manual driving of a multiple turret system in extreme environment,” Ph.D. - Doctoral Program, Middle East Technical University, 2021.