STAM: A System of Tracking and Mapping in Real Environments

We have implemented a system of tracking mobile robots and mapping an unstructured environment, using up to 25 wireless sensor nodes in an indoor setting. These sensor nodes form an ad hoc network of beacons, self-localize with respect to three anchor nodes, and then track the locations of mobile robots in the field. The system described here was motivated by search and rescue applications, and has been demonstrated in real physical environments

Heuristic algorithms for motion planning

Motion planning is an increasingly important field of research. Factory automation is becoming more prevalent and at the same time, production runs are shortening in the name of customisation. With computer controlled equipment becoming cheaper and more modular, setting up near-fully automated production lines is becoming fast and easy. This means that the actual programming of the robots and assembly system is becoming the rate determining step. Automated motion planning is a possible solution to this—but only if it can run fast enough.Many heuristic planners have been created in an attempt to achieve the necessary speeds in off-line (or more ambitiously, on-line) processing. This thesis aims to show that different types of heuristic planners can be designed to take advantage of specialised environments or robot characteristics. To show this, three distinct classes of heuristic planners are put forward for discussion.The first of these classes, addressed in Chapter 2, is of very generic planners which will work in virtually all situations (ie. almost any combination of robot and environment). This generality is obviously useful when lacking more specific domain knowledge. However these methods do suffer performance-wise in comparison with more specialised planners when there are characteristics of the problem which can be targeted.Chapter 3 moves to planners which are designed to specifically address certain peculiarities of the environment. Particular focus is given to environments whose corresponding configuration-spaces contain narrow gaps and passages.Finally Chapter 4 addresses a third class of planners: those which are designed for specific types of robots and movements. The particular focus is on locomotion for legged vehicles.For each of these three classes, some discussion is made of existing planners which can be so characterised. In addition, a novel algorithm is introduced in each as an example for particular consideration.</p

Factorisation of Large Integers on some Vector and Parallel Computers

We compare implementations of two integer factorization algorithms, the elliptic curve method (ECM) and a variant of the Pollard &quot;rho&quot; method, on three machines with parallel and/or vector architectures. ECM is scalable and well suited for both vector and parallel architectures. The &quot;rho&quot; method is simpler than ECM but is not scalable. 1 Introduction The factorization of large integers is a significant mathematical problem with practical applications to public-key cryptography [16]. Although the theoretical complexity of factorization is unknown, it is a computationally expensive task with the best known algorithms. The development of new algorithms and faster machines has made the factorization of &quot;general&quot; integers with 100--120 digits feasible. Several authors have considered vector and parallel implementations of the MPQS and NFS algorithms [5, 8, 10, 14, 15]. These algorithms have the property that the run-time depends mainly on the size of the number N to be factored. For anothe..

Motion planning of legged vehicles in an unstructured environment

A planner for statically-stable motion of a legged robotic vehicle over an uneven terrain is presented that can plan the footplacement of individual legs for highly cluttered terrain. A method for determining the traversability over a generic discretised height map terrain is presented. Planning is broken into two levels of refinement to reduce the overall complexity and incorporates a number of heuristics. The planner has successfully planned the motion of 6 and 8 legged configurations of the XE-ROX PARC PolyBot modular reconfigurable robot as well as the CMU Ambler in simulation over arbitrarily complex terrain. A distributed implementation of the planner has also been shown on Poly-Bot’s distributed computational platform.

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Heuristic algorithms for motion planning

Motion planning is an increasingly important field of research. Factory automation is becoming more prevalent and at the same time, production runs are shortening in the name of customisation. With computer controlled equipment becoming cheaper and more modular, setting up near-fully automated production lines is becoming fast and easy. This means that the actual programming of the robots and assembly system is becoming the rate determining step. Automated motion planning is a possible solution to this—but only if it can run fast enough.Many heuristic planners have been created in an attempt to achieve the necessary speeds in off-line (or more ambitiously, on-line) processing. This thesis aims to show that different types of heuristic planners can be designed to take advantage of specialised environments or robot characteristics. To show this, three distinct classes of heuristic planners are put forward for discussion.The first of these classes, addressed in Chapter 2, is of very generic planners which will work in virtually all situations (ie. almost any combination of robot and environment). This generality is obviously useful when lacking more specific domain knowledge. However these methods do suffer performance-wise in comparison with more specialised planners when there are characteristics of the problem which can be targeted.Chapter 3 moves to planners which are designed to specifically address certain peculiarities of the environment. Particular focus is given to environments whose corresponding configuration-spaces contain narrow gaps and passages.Finally Chapter 4 addresses a third class of planners: those which are designed for specific types of robots and movements. The particular focus is on locomotion for legged vehicles.For each of these three classes, some discussion is made of existing planners which can be so characterised. In addition, a novel algorithm is introduced in each as an example for particular consideration.</p

Using Genetic Algorithms to Solve the Motion Planning Problem

Motion planning is a field of growing importance as more and more computer controlled devices are being used. Many different approaches exist to motion planning|none of them ideal in all situations. This paper considers how to convert a general motion planning problem into one of global optimisation. We regard the general problem as being the classical configuration space findpath problem, but assume that the configurations of the device can be bounded by a hierarchy of hyper-spheres rather than being explicitly computed. A program to solve this problem has been written employing Genetic Algorithms. This paper describes how this was done, and some preliminary results of using it