On-line path planning by heuristic hierarchical search

In this paper, the problem of path planning for robot manipulators with six degrees of freedom in an on-line provided three-dimensional environment is investigated. As a basic approach, the best-first algorithm is used to search in the implicit descrete configuration space. Collisions are detected in the Cartesian workspace by hierarchical distance computation based on the given CAD model. The basic approach is extended by three simple mechanisms and results in a heuristic hierarchical search. This is done by adjusting the stepsize of the search to the distance between the robot and the obstacles. As a first step, we show encouraging experimental results with two degrees of freedom for five typical benchmark problems

Educational hands-on testbed using Lego robot for learning guidance, navigation, and control

The aim of this paper is to propose an educational hands-on testbed using inexpensive systems composed of a Lego Mindstorms NXT robot and a webcam and easy-to-deal-with tools especially for learning and testing guidance, navigation, and control as well as search and obstacle mapping, however the extendibility and applicability of the proposed approach is not limited to only the educational purpose. In order to provide navigation information of the Lego robot in an indoor environment, an vision navigation system is proposed based on a colour marker detection robust to brightness change and an Extended Kalman filter. Furthermore, a spiral-like search, a command-to-line-of-sight guidance, a motor control, and two-dimensional Splinegon approximation are applied to sensing and mapping of a complex-shaped obstacle. The experimental result shows that the proposed testbed can be viewed as an efficient tool for the education of image processing and estimation as well as guidance, navigation, and control with a minimum burden of time and cost. © 2011 IFAC

Development of Modeling and Simulation Platform for Path-Planning and Control of Autonomous Underwater Vehicles in Three-Dimensional Spaces

Autonomous underwater vehicles (AUVs) operating in deep sea and littoral environments have diverse applications including marine biology exploration, ocean environment monitoring, search for plane crash sites, inspection of ship-hulls and pipelines, underwater oil rig maintenance, border patrol, etc. Achieving autonomy in underwater vehicles relies on a tight integration between modules of sensing, navigation, decision-making, path-planning, trajectory tracking, and low-level control. This system integration task benefits from testing the related algorithms and techniques in a simulated environment before implementation in a physical test bed. This thesis reports on the development of a modeling and simulation platform that supports the design and testing of path planning and control algorithms in a synthetic AUV, representing a simulated version of a physical AUV. The approach allows integration between path-planners and closed-loop controllers that enable the synthetic AUV to track dynamically feasible trajectories in three-dimensional spaces. The dynamical behavior of the AUV is modeled using the equations of motion that incorporate the effects of external forces (e.g., buoyancy, gravity, hydrodynamic drag, centripetal force, Coriolis force, etc.), thrust forces, and inertial forces acting on the AUV. The equations of motion are translated into a state space formulation and the S-function feature of the Simulink and MATLAB scripts are used to evolve the state trajectories from initial conditions. A three-dimensional visualization of the resulting AUV motion is achieved by feeding the corresponding position and orientation states into an animation code. Experimental validation is carried out by performing integrated waypoint planner (e.g., using the popular A* algorithm) and PD controller implementations that allow the traversal of the synthetic AUV in two-dimensional (XY, XZ, YZ) and three-dimensional spaces. An underwater pipe-line inspection task carried out by the AUV is demonstrated in a simulated environment. The simulation testbed holds a potential to support planner and controller design for implementation in physical AUVs, thereby allowing exploration of various research topics in the field

The topography of the environment alters the optimal search strategy for active particles

In environments with scarce resources, adopting the right search strategy can make the difference between succeeding and failing, even between life and death. At different scales, this applies to molecular encounters in the cell cytoplasm, to animals looking for food or mates in natural landscapes, to rescuers during search-and-rescue operations in disaster zones, as well as to genetic computer algorithms exploring parameter spaces. When looking for sparse targets in a homogeneous environment, a combination of ballistic and diffusive steps is considered optimal; in particular, more ballistic L\'evy flights with exponent {\alpha} <= 1 are generally believed to optimize the search process. However, most search spaces present complex topographies, with boundaries, barriers and obstacles. What is the best search strategy in these more realistic scenarios? Here we show that the topography of the environment significantly alters the optimal search strategy towards less ballistic and more Brownian strategies. We consider an active particle performing a blind search in a two-dimensional space with steps drawn from a L\'evy distribution with exponent varying from {\alpha} = 1 to {\alpha} = 2 (Brownian). We demonstrate that the optimal search strategy depends on the topography of the environment, with {\alpha} assuming intermediate values in the whole range under consideration. We interpret these findings in terms of a simple theoretical model, and discuss their robustness to the addition of Brownian diffusion to the searcher's motion. Our results are relevant for search problems at different length scales, from animal and human foraging to microswimmers' taxis, to biochemical rates of reaction