Vision-based Testbeds For Control System Applicaitons

In the field of control systems, testbeds are a pivotal step in the validation and improvement of new algorithms for different applications. They provide a safe, controlled environment typically having a significantly lower cost of failure than the final application. Vision systems provide nonintrusive methods of measurement that can be easily implemented for various setups and applications. This work presents methods for modeling, removing distortion, calibrating, and rectifying single and two camera systems, as well as, two very different applications of vision-based control system testbeds: deflection control of shape memory polymers and trajectory planning for mobile robots. First, a testbed for the modeling and control of shape memory polymers (SMP) is designed. Red-green-blue (RGB) thresholding is used to assist in the webcam-based, 3D reconstruction of points of interest. A PID based controller is designed and shown to work with SMP samples, while state space models were identified from step input responses. Models were used to develop a linear quadratic regulator that is shown to work in simulation. Also, a simple to use graphical interface is designed for fast and simple testing of a series of samples. Second a robot testbed is designed to test new trajectory planning algorithms. A templatebased predictive search algorithm is investigated to process the images obtained through a lowcost webcam vision system, which is used to monitor the testbed environment. Also a userfriendly graphical interface is developed such that the functionalities of the webcam, robots, and optimizations are automated. The testbeds are used to demonstrate a wavefront-enhanced, Bspline augmented virtual motion camouflage algorithm for single or multiple robots to navigate through an obstacle dense and changing environment, while considering inter-vehicle conflicts, iv obstacle avoidance, nonlinear dynamics, and different constraints. In addition, it is expected that this testbed can be used to test different vehicle motion planning and control algorithms

Indoor Target Intercept Using an Acoustic Sensor Network and Dual Wavefront Path Planning

This paper presents an approach that enables a mobile "Interceptor" robot to intercept targets in an indoor environment using information from a distributed acoustic sensor network. The approach assumes the indoor environment has been previously mapped and that the sensor nodes know their position in the map. The targets are localized in the sensor network based upon local maxima of the acoustic volume. The current target localization information is reported to an Interceptor robot, which utilizes a dual wavefront path planner to move from its current location to a location that is within visibility range of a target. Results of the complete implementation of this approach using 70 sensor net robots in the Player/Stage multirobot simulator are reported, as well as implementation results to date on a team of physical robots. To our knowledge, this is the first implementation of a multi-robot system that combines the use of an acoustic sensor net for target detection with an Interceptor robot that can efficiently reach the moving position of the detected target in indoor environments

3D Motion Planning using Kinodynamically Feasible Motion Primitives in Unknown Environments

Autonomous vehicles are a great asset to society by helping perform many dangerous or tedious tasks. They have already been successfully employed for many practical applications, such as search and rescue, automated surveillance, exploration and mapping, sample collection, and remote inspection. In order to perform most tasks autonomously, the vehicle must be able to safely and efficiently navigate through its environment. The algorithms and techniques that allow an autonomous vehicle to find traversable paths to its destination defines the set of problems in robotics known as motion planning. This thesis presents a new motion planner that is capable of finding collision-free paths through an unknown environment while satisfying the kinodynamic constraints of the vehicle. This is done using a two step process. In the first step, a collision-free path is generated using a modified Probabilistic Roadmap (PRM) based planner by assuming unexplored areas are obstacle-free. As obstacles are detected, the planner will replan the path as necessary to ensure that it remains collision-free. In complex environments, it is often necessary to increase the size of the PRM graph during the replanning step so that the graph remains connected. However, this causes the algorithm to slow down significantly over time. To mitigate these issues, the novel local sampling and PRM regeneration techniques are used to increase the computational efficiency of the replanning step. The local sampling technique biases the search towards the neighborhood of the obstacle blocking the path. This encourages the planner to generate small detours around the obstacle instead of rerouting the whole path. The PRM regeneration technique is used to remove all non-critical nodes from the PRM graph. This is used to bound the size of the PRM graph so that it does not grow increasingly large over time. In the second step, the collision-free path is transformed into a series of kinodynamically feasible motion primitives using two novel algorithms: the heuristic re-sampling algorithm and the transformation algorithm. The heuristic re-sampling algorithm is a greedy heuristic algorithm that increases the clearance around the path while removing redundant segments. This algorithm can be applied to any piece-wise linear path, and is guaranteed to produce a solution that is at least as good as the initial path. The transformation algorithm is a method to convert a path into a series of kinodynamically feasible motion primitives. It is extremely efficient computationally, and can be applied to any piece-wise linear path. To achieve good computational performance with PRM based planners, it is necessary to use sampling strategies that can efficiently form connected graphs through narrow and complex regions of the configuration space. Many proposed sampling methods attempt to bias the sample density in favor of these difficult to connect areas. However, these methods do not distinguish between samples that lie inside narrow passages and those that lie along convex borders. The orthogonal bridge test is a novel sampling technique that can identify and reject samples that lie along convex borders. This allows connected PRM graphs to be constructed with fewer nodes, which leads to less collision checking and reduced runtimes. The presented algorithms are experimentally verified using an AR.Drone quadrotor unmanned aerial vehicle (UAV) and a custom built skid-steer unmanned ground vehicle (UGV). Using a simple kinematic model and a basic position controller, the AR.Drone is able to traverse a series of motion primitives with less than 0.3 m of crosstrack error. The skid-steer UGV is able to navigate through unknown environments filled with obstacles to reach a desired destination. Furthermore, the observed runtimes of the proposed motion planner suggest that it is fully capable of computing solution paths online. This is an important result, because online computation is necessary for efficient autonomous operations and it can not be achieved with many existing kinodynamic motion planners