Autonomous search of real-life environments combining dynamical system-based path planning and unsupervised learning

In recent years, advancements have been made towards the goal of using chaotic coverage path planners for autonomous search and traversal of spaces with limited environmental cues. However, the state of this field is still in its infancy as there has been little experimental work done. Current experimental work has not developed robust methods to satisfactorily address the immediate set of problems a chaotic coverage path planner needs to overcome in order to scan realistic environments within reasonable coverage times. These immediate problems are as follows: (1) an obstacle avoidance technique which generally maintains the kinematic efficiency of the robot's motion, (2) a means to spread chaotic trajectories across the environment (especially crucial for large and/or complex-shaped environments) that need to be covered, and (3) a real-time coverage calculation technique that is accurate and independent of cell size. This paper aims to progress the field by proposing algorithms that address all of these problems by providing techniques for obstacle avoidance, chaotic trajectory dispersal, and accurate coverage calculation. The algorithms produce generally smooth chaotic trajectories and provide high scanning coverage of environments. These algorithms were created within the ROS framework and make up a newly developed chaotic path planning application. The performance of this application was comparable to that of a conventional optimal path planner. The performance tests were carried out in environments of various sizes, shapes, and obstacle densities, both in real-life and Gazebo simulations

Deep Reinforcement Learning for Complete Coverage Path Planning in Unknown Environments

Mobile robots must operate autonomously, often in unknown and unstructured environments. To achieve this objective, a robot must be able to correctly perceive its environment, plan its path, and move around safely, without human supervision. Navigation from an initial position to a target lo- cation has been a challenging problem in robotics. This work examined the particular navigation task requiring complete coverage planning in outdoor environments. A motion planner based on Deep Reinforcement Learning is proposed where a Deep Q-network is trained to learn a control policy to approximate the optimal strategy, using a dynamic map of the environment. In addition to this path planning algorithm, a computer vision system is presented as a way to capture the images of a stereo camera embedded on the robot, detect obstacles and update the workspace map. Simulation results show that the algorithm generalizes well to different types of environments. After multiple sequences of training of the Reinforcement Learning agent, the virtual mobile robot is able to cover the whole space with a coverage rate of over 80% on average, starting from a varying initial position, while avoiding obstacles by using relying on local sensory information. The experiments also demonstrate that the DQN agent was able to better perform the coverage when compared to a human

Autonomous, Collaborative, Unmanned Aerial Vehicles for Search and Rescue

Search and Rescue is a vitally important subject, and one which can be improved through the use of modern technology. This work presents a number of advances aimed towards the creation of a swarm of autonomous, collaborative, unmanned aerial vehicles for land-based search and rescue. The main advances are the development of a diffusion based search strategy for route planning, research into GPS (including the Durham Tracker Project and statistical research into altitude errors), and the creation of a relative positioning system (including discussion of the errors caused by fast-moving units). Overviews are also given of the current state of research into both UAVs and Search and Rescue

2023- The Twenty-seventh Annual Symposium of Student Scholars

The full program book from the Twenty-seventh Annual Symposium of Student Scholars, held on April 18-21, 2023. Includes abstracts from the presentations and posters.https://digitalcommons.kennesaw.edu/sssprograms/1027/thumbnail.jp

Semantic Based Support for Visualisation in Complex Collaborative Planning Environments

Centre for Intelligent Systems and their ApplicationsVisualisation in intelligent planning systems [Ghallab et al., 2004] is a subject that has not been given much attention by researchers. Among the existing planning systems, some well known planners do not propose a solution for visualisation at all, while others only consider a single approach when this solution sometimes is not appropriate for every situation. Thus, users cannot make the most of planning systems because they do not have appropriate support for interaction with them. This problem is more enhanced when considering mixed-initiative planning systems, where agents that are collaborating in the process have different backgrounds, are playing different roles in the process, have different capabilities and responsibilities, or are using different devices to interact and collaborate in the process. To address this problem, we propose a general framework for visualisation in planning systems that will give support for a more appropriate visualisation mechanism. This framework is divided into two main parts: a knowledge representation aspect and a reasoning mechanism for multi-modality visualisation. The knowledge representation uses the concept of ontology to organise and model complex domain problems. The reasoning mechanism gives support to reasoning about the visualisation problem based on the knowledge bases available for a realistic collaborative planning environment, including agent preferences, device features, planning information, visualisation modalities, etc. The main result of the reasoning mechanism is an appropriate visualisation modality for each specific situation, which provides a better interaction among agents (software and human) in a collaborative planning environment. The main contributions of this approach are: (1) it is a general and extensible framework for the problem of visualisation in planning systems, which enables the modelling of the domain from an information visualisation perspective; (2) it allows a tailored approach for visualisation of information in an AI collaborative planning environment; (3) its models can be used separately in other problems and domains; (4) it is based on real standards that enable easy communication and interoperability with other systems and services; and (5) it has a broad potential for its application on the Semantic Web

NOTIFICATION !!!

All the content of this special edition is retrieved from the conference proceedings published by the European Scientific Institute, ESI. http://eujournal.org/index.php/esj/pages/view/books The European Scientific Journal, ESJ, after approval from the publisher re publishes the papers in a Special edition

NOTIFICATION !!!

All the content of this special edition is retrieved from the conference proceedings published by the European Scientific Institute, ESI. http://eujournal.org/index.php/esj/pages/view/books The European Scientific Journal, ESJ, after approval from the publisher re publishes the papers in a Special edition

NOTIFICATION !!!

All the content of this special edition is retrieved from the conference proceedings published by the European Scientific Institute, ESI. http://eujournal.org/index.php/esj/pages/view/books The European Scientific Journal, ESJ, after approval from the publisher re publishes the papers in a Special edition