10 research outputs found
Physics-based Motion Planning with Temporal Logic Specifications
One of the main foci of robotics is nowadays centered in providing a great
degree of autonomy to robots. A fundamental step in this direction is to give
them the ability to plan in discrete and continuous spaces to find the required
motions to complete a complex task. In this line, some recent approaches
describe tasks with Linear Temporal Logic (LTL) and reason on discrete actions
to guide sampling-based motion planning, with the aim of finding
dynamically-feasible motions that satisfy the temporal-logic task
specifications. The present paper proposes an LTL planning approach enhanced
with the use of ontologies to describe and reason about the task, on the one
hand, and that includes physics-based motion planning to allow the purposeful
manipulation of objects, on the other hand. The proposal has been implemented
and is illustrated with didactic examples with a mobile robot in simple
scenarios where some of the goals are occupied with objects that must be
removed in order to fulfill the task.Comment: The 20th World Congress of the International Federation of Automatic
Control, 9-14 July 201
Task planning using physics-based heuristics on manipulation actions
© 2016 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works.In order to solve mobile manipulation problems, the efficient combination of task and motion planning is usually required. Moreover, the incorporation of physics-based information has recently been taken into account in order to plan the tasks in a more realistic way. In the present paper, a task and motion planning framework is proposed based on a modified version of the Fast-Forward task planner that is guided by physics-based knowledge.
The proposal uses manipulation knowledge for reasoning on symbolic literals (both in offline and online modes) taking into account geometric information in order to evaluate the applicability as well as feasibility of actions while evaluating the heuristic cost. It results in an efficient search of the state space and in the obtention of low-cost physically-feasible plans. The proposal has been implemented and is illustrated with a manipulation problem consisting of a mobile robot and some fixed and manipulatable objects.Peer ReviewedPostprint (author's final draft
Autonomous Underwater Robotic System for Aquaculture Applications
Aquaculture is a thriving food-producing sector producing over half of the
global fish consumption. However, these aquafarms pose significant challenges
such as biofouling, vegetation, and holes within their net pens and have a
profound effect on the efficiency and sustainability of fish production.
Currently, divers and/or remotely operated vehicles are deployed for inspecting
and maintaining aquafarms; this approach is expensive and requires highly
skilled human operators. This work aims to develop a robotic-based automatic
net defect detection system for aquaculture net pens oriented to on- ROV
processing and real-time detection of different aqua-net defects such as
biofouling, vegetation, net holes, and plastic. The proposed system integrates
both deep learning-based methods for aqua-net defect detection and feedback
control law for the vehicle movement around the aqua-net to obtain a clear
sequence of net images and inspect the status of the net via performing the
inspection tasks. This work contributes to the area of aquaculture inspection,
marine robotics, and deep learning aiming to reduce cost, improve quality, and
ease of operation.Comment: arXiv admin note: text overlap with arXiv:2308.1382
Vision-Based Autonomous Navigation for Unmanned Surface Vessel in Extreme Marine Conditions
Visual perception is an important component for autonomous navigation of
unmanned surface vessels (USV), particularly for the tasks related to
autonomous inspection and tracking. These tasks involve vision-based navigation
techniques to identify the target for navigation. Reduced visibility under
extreme weather conditions in marine environments makes it difficult for
vision-based approaches to work properly. To overcome these issues, this paper
presents an autonomous vision-based navigation framework for tracking target
objects in extreme marine conditions. The proposed framework consists of an
integrated perception pipeline that uses a generative adversarial network (GAN)
to remove noise and highlight the object features before passing them to the
object detector (i.e., YOLOv5). The detected visual features are then used by
the USV to track the target. The proposed framework has been thoroughly tested
in simulation under extremely reduced visibility due to sandstorms and fog. The
results are compared with state-of-the-art de-hazing methods across the
benchmarked MBZIRC simulation dataset, on which the proposed scheme has
outperformed the existing methods across various metrics.Comment: IEEE/RSJ International Conference on Intelligent Robots (IROS-2023
Task planning using physics-based heuristics on manipulation actions
© 2016 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works.In order to solve mobile manipulation problems, the efficient combination of task and motion planning is usually required. Moreover, the incorporation of physics-based information has recently been taken into account in order to plan the tasks in a more realistic way. In the present paper, a task and motion planning framework is proposed based on a modified version of the Fast-Forward task planner that is guided by physics-based knowledge.
The proposal uses manipulation knowledge for reasoning on symbolic literals (both in offline and online modes) taking into account geometric information in order to evaluate the applicability as well as feasibility of actions while evaluating the heuristic cost. It results in an efficient search of the state space and in the obtention of low-cost physically-feasible plans. The proposal has been implemented and is illustrated with a manipulation problem consisting of a mobile robot and some fixed and manipulatable objects.Peer Reviewe