Optimal path planning for nonholonomic robotics systems via parametric optimisation

Abstract. Motivated by the path planning problem for robotic systems this paper considers nonholonomic path planning on the Euclidean group of motions SE(n) which describes a rigid bodies path in n-dimensional Euclidean space. The problem is formulated as a constrained optimal kinematic control problem where the cost function to be minimised is a quadratic function of translational and angular velocity inputs. An application of the Maximum Principle of optimal control leads to a set of Hamiltonian vector field that define the necessary conditions for optimality and consequently the optimal velocity history of the trajectory. It is illustrated that the systems are always integrable when n = 2 and in some cases when n = 3. However, if they are not integrable in the most general form of the cost function they can be rendered integrable by considering special cases. This implies that it is possible to reduce the kinematic system to a class of curves defined analytically. If the optimal motions can be expressed analytically in closed form then the path planning problem is reduced to one of parameter optimisation where the parameters are optimised to match prescribed boundary conditions.This reduction procedure is illustrated for a simple wheeled robot with a sliding constraint and a conventional slender underwater vehicle whose velocity in the lateral directions are constrained due to viscous damping

Homotopic Path Planning on Manifolds for Cabled Mobile Robots

We present two path planning algorithms for mobile robots that are connected by cable to a fixed base. Our algorithms efficiently compute the shortest path and control strategy that lead the robot to the target location considering cable length and obstacle interactions. First, we focus on cable-obstacle collisions. We introduce and formally analyze algorithms that build and search an overlapped configuration space manifold. Next, we present an extension that considers cable-robot collisions. All algorithms are experimentally validated using a real robot

Motion Planning via Manifold Samples

We present a general and modular algorithmic framework for path planning of robots. Our framework combines geometric methods for exact and complete analysis of low-dimensional configuration spaces, together with practical, considerably simpler sampling-based approaches that are appropriate for higher dimensions. In order to facilitate the transfer of advanced geometric algorithms into practical use, we suggest taking samples that are entire low-dimensional manifolds of the configuration space that capture the connectivity of the configuration space much better than isolated point samples. Geometric algorithms for analysis of low-dimensional manifolds then provide powerful primitive operations. The modular design of the framework enables independent optimization of each modular component. Indeed, we have developed, implemented and optimized a primitive operation for complete and exact combinatorial analysis of a certain set of manifolds, using arrangements of curves of rational functions and concepts of generic programming. This in turn enabled us to implement our framework for the concrete case of a polygonal robot translating and rotating amidst polygonal obstacles. We demonstrate that the integration of several carefully engineered components leads to significant speedup over the popular PRM sampling-based algorithm, which represents the more simplistic approach that is prevalent in practice. We foresee possible extensions of our framework to solving high-dimensional problems beyond motion planning.Comment: 18 page

IMG 305 - PEMBUNGKUSAN MAKANAN NOV.05.

We discuss the use of Agent-based Modelling for the development and testing of theories about emergent social phenomena in marketing and the social sciences in general. We address both theoretical aspects about the types of phenomena that are suitably addressed with this approach and practical guidelines to help plan and structure the development of a theory about the causes of such a phenomenon in conjunction with a matching ABM. We argue that research about complex social phenomena is still largely fundamental research and therefore an iterative and cyclical development process of both theory and model is to be expected. To better anticipate and manage this process, we provide theoretical and practical guidelines. These may help to identify and structure the domain of candidate explanations for a social phenomenon, and furthermore assist the process of model implementation and subsequent development. The main goal of this paper was to make research on complex social systems more accessible and help anticipate and structure the research process

Multiple Robot Path Planning for Robot Soccer

The Robot World Cup Initiative (RoboCup) is an international joint project to promote AI, robotics, and related field. It provides a standard platform for robotic soccer game which includes a vision system, a strategic play algorithm and small mobile robots. The aim of the vision system for RoboCup small robot league is to track and predict the motion states of 6 agents and a ball, and send the states information to the Artificial Intelligence module. This paper presents a design and implementation of a real-time, robust global vision system. The two main efforts are realized in this study, design and implementation of multi robot structure and the construction and tasting of the tracking and path planning algorithm. In the tracking phase, the color-based segmentation is used to locate the interesting objects in the image; the blob analysis is adopted to calculate the positions of the objects in the image and the noise is filtered out; and the linear filter is adopted to track and predict information of the states. It has been planned that the information gathered using this study can be used in multiple agent robotics applications. © Springer-Verlag Berlin Heidelberg 2006

Needle Steering and Model-Based Trajectory Planning

Abstract. Needle insertion for percutaneous therapies is formulated as a trajectory planning and control problem. A new concept of needle steer-ing is developed and a Needle Manipulation Jacobian is defined using numerical needle insertion models that include needle deflection and soft tissue deformation. This concept is used in conjunction with a potential-field-based path planning technique to demonstrate needle tip placement and obstacle avoidance. Results from open loop insertion experiments are provided.