Statistics for Spatial Data.

C4.5: Programs for Machine Learning (書評)

Feel lonely? What about reading books? Book is one of the greatest friends to accompany while in your lonely time. When you have no friends and activities somewhere and sometimes, reading book can be a great choice. This is not only for spending the time, it will increase the knowledge. Of course the b=benefits to take will relate to what kind of book that you are reading. And now, we will concern you to try reading modelling and control of robot manipulators as one of the reading material to finish quickly.

Modelling and Control of Robot Manipulators

Harmful algal blooms and climate change: Learning from the past and present to forecast the future.

Deep Sea Research Part II: Topical Studies in Oceanography

Autonomous Underwater Vehicles (AUVs) are an increasingly important tool for oceanographic research demonstrating their capabilities to sample the water column in depths far beyond what humans are capable of visiting, and doing so routinely and cost-effectively. However, control of these platforms to date has relied on fixed sequences for execution of pre-planned actions limiting their effectiveness for measuring dynamic and episodic ocean phenomenon. In this paper we present an agent architecture developed to overcome this limitation through on-board planning using Constraint- based Reasoning. Preliminary versions of the architecture have been integrated and tested in simulation and at sea.

A deliberative architecture for AUV control

We present a formal framework of an autonomous agent as a collection of coordinated control loops, with a recurring sense, plan, act cycle. Our framework manages the information flow within the partitioned structure to ensure consistency in order to direct the flow of goals and observations in a timely manner. The resulting control structure improves scalability since many details of each controller can be encapsulated within a single control loop. This partitioned agent design promises a domain-independent, scalable and robust approach for control of real-world autonomous robots operating in dynamic environments. We validate our framework with experimental results from deployments in two different real-world domains.

A systematic agent framework for situated autonomous systems

We describe a novel integration of Planning with Probabilistic State Estimation and Execution. The resulting system is a unified representational and computational framework based on declarative models and constraint-based temporal plans. The work is motivated by the need to explore the oceans more cost-effectively through the use of Autonomous Underwater Vehicles (AUV), requiring them to be goal-directed, perceptive, adaptive and robust in the context of dynamic and uncertain conditions. The novelty of our approach is in integrating deliberation and reaction over different temporal and functional scopes within a single model, and in breaking new ground in oceanography by allowing for precise sampling within a feature of interest using an autonomous robot. The system is general-purpose and adaptable to other ocean going and terrestrial platforms.

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Adaptive control for autonomous underwater vehicles

We extend existing oceanographic sampling methodologies to sample an advecting feature of interest using autonomous robotic platforms. GPS-tracked Lagrangian drifters are used to tag and track a water patch of interest with position updates provided periodically to an autonomous underwater vehicle (AUV) for surveys around the drifter as it moves with ocean currents. Autonomous sampling methods currently rely on geographic waypoint track-line surveys that are suitable for static or slowly changing features. When studying dynamic, rapidly evolving oceanographic features, such methods at best introduce error through insufficient spatial and temporal resolution, and at worst, completely miss the spatial and temporal domain of interest. We demonstrate two approaches for tracking and sampling of advecting oceanographic features. The first relies on extending static-plan AUV surveys (the current state-of-the-art) to sample advecting features. The second approach involves planning of surveys in the drifter or patch frame of reference. We derive a quantitative envelope on patch speeds that can be tracked autonomously by AUVs and drifters and show results from a multi-day off-shore field trial. The results from the trial demonstrate the applicability of our approach to long-term tracking and sampling of advecting features. Additionally, we analyze the data from the trial to identify the sources of error that affect the quality of the surveys carried out. Our work presents the first set of experiments to autonomously observe advecting oceanographic features in the open ocean.

Coordinated sampling of dynamic oceanographic features with underwater vehicles and drifters

To achieve the ever increasing demand for science return, planetary exploration rovers require more autonomy to successfully perform their missions. Indeed, the communication delays are such that teleoperation is unrealistic. Although the current rovers (such as MER) demonstrate a limited navigation autonomy, and mostly rely on ground mission planning, the next generation (e.g., NASA Mars Science Laboratory and ESA Exomars) will have to regularly achieve long range autonomous navigation tasks. However, fully autonomous long range navigation in partially known planetary-like terrains is still an open challenge for robotics. Navigating hundreds of meters without any human intervention requires the robot to be able to build adequate representations of its environment, to plan and execute trajectories according to the kind of terrain traversed, to control its motions, and to localize itself as it moves. All these activities have to be planned, scheduled, and performed according to the rover context, and controlled so that the mission is correctly fulfilled. To achieve these objectives, we have developed a temporal planner and an execution controller, which exhibit plan repair and replanning capabilities. The planner is in charge of producing plans composed of actions for navigation, science activities (moving and operating instruments), communication with Earth and with an orbiter or a lander, while managing resources (power, memory, etc.) and respecting temporal constraints (communication visibility windows, rendezvous, etc.). High level actions also need to be refined and their execution temporally and logically controlled. Finally, in such critical applications, we believe it is important to deploy a component that protects the system against dangerous or even fatal situations resulting from unexpected interactions between subsystems (e.g., move the robot while the robot arm is unstowed) and/or software components (e.g., take and store a picture in a buffer while the previous one is still being processed). In this article we review the aforementioned capabilities, which have been developed, tested, and evaluated on board our rovers (Lama and Dala). After an overview of the architecture design principle adopted, we summarize the perception, localization, and motion generation functions required by autonomous navigation, and their integration and concurrent operation in a global architecture. We then detail the decisional components: a high level temporal planner that produces the robot activity plan on board, and temporal and procedural execution controllers. We show how some failures or execution delays are being taken care of with online local repair, or replanning. © 2007 Wiley Periodicals, Inc.

/pdf/decisional-autonomy-of-planetary-rovers-3gtobtjv5x.pdf

Frederic Py

Papers

A deliberative architecture for AUV control

A systematic agent framework for situated autonomous systems

Adaptive control for autonomous underwater vehicles

Coordinated sampling of dynamic oceanographic features with underwater vehicles and drifters

Decisional autonomy of planetary rovers