A Hierarchal Planning Framework for AUV Mission Management in a Spatio-Temporal Varying Ocean

The purpose of this paper is to provide a hierarchical dynamic mission planning framework for a single autonomous underwater vehicle (AUV) to accomplish task-assign process in a limited time interval while operating in an uncertain undersea environment, where spatio-temporal variability of the operating field is taken into account. To this end, a high level reactive mission planner and a low level motion planning system are constructed. The high level system is responsible for task priority assignment and guiding the vehicle toward a target of interest considering on-time termination of the mission. The lower layer is in charge of generating optimal trajectories based on sequence of tasks and dynamicity of operating terrain. The mission planner is able to reactively re-arrange the tasks based on mission/terrain updates while the low level planner is capable of coping unexpected changes of the terrain by correcting the old path and re-generating a new trajectory. As a result, the vehicle is able to undertake the maximum number of tasks with certain degree of maneuverability having situational awareness of the operating field. The computational engine of the mentioned framework is based on the biogeography based optimization (BBO) algorithm that is capable of providing efficient solutions. To evaluate the performance of the proposed framework, firstly, a realistic model of undersea environment is provided based on realistic map data, and then several scenarios, treated as real experiments, are designed through the simulation study. Additionally, to show the robustness and reliability of the framework, Monte-Carlo simulation is carried out and statistical analysis is performed. The results of simulations indicate the significant potential of the two-level hierarchical mission planning system in mission success and its applicability for real-time implementation

Transversality and Transition: Branching to New Regional Path Dependence

Since Paul David published his economic histories of path dependent innovation the subject has exerted fascination upon scholars of innovation, technological change and, latterly, regional scientists and economic geographers. This paper speaks to the third and fourth of these communities in the main, though it may have theoretical and empirical elements of interest to the first two as well. The paper begins with an overview of recent perspectives and critiques concerning the relevance of the path dependence concept to the understanding of regional economic development and its associated governance. It then goes on to discuss the contribution of evolutionary economic geography to thinking about ÔbranchingÕ from path dependence and the creation of new paths. Evidence for key generic spatial processes of path transition is provided before the main content of the paper concludes with new insights into the contributions of regional innovation policy to path evolution. Conclusions are then drawn.regional path dependence, branching, transition, transversality

Modeling the mobility of living organisms in heterogeneous landscapes: Does memory improve foraging success?

Thanks to recent technological advances, it is now possible to track with an unprecedented precision and for long periods of time the movement patterns of many living organisms in their habitat. The increasing amount of data available on single trajectories offers the possibility of understanding how animals move and of testing basic movement models. Random walks have long represented the main description for micro-organisms and have also been useful to understand the foraging behaviour of large animals. Nevertheless, most vertebrates, in particular humans and other primates, rely on sophisticated cognitive tools such as spatial maps, episodic memory and travel cost discounting. These properties call for other modeling approaches of mobility patterns. We propose a foraging framework where a learning mobile agent uses a combination of memory-based and random steps. We investigate how advantageous it is to use memory for exploiting resources in heterogeneous and changing environments. An adequate balance of determinism and random exploration is found to maximize the foraging efficiency and to generate trajectories with an intricate spatio-temporal order. Based on this approach, we propose some tools for analysing the non-random nature of mobility patterns in general.Comment: 14 pages, 4 figures, improved discussio

A multiobjective optimization issue: genetic control planning or trajectories.

Part of a research project on cooperative marine robotics is the scenario of a submarine rendez-vous. This paper considers this case, where a high-manoeuvrability AUV (autonomous underwater vehicle) should meet a submarine platform for energy, samples and data service. Since the AUV is equipped with a set of thrusters, the problem of an adequate command of the thrusters appears. Given initial and final points for the AUV underwater trajectory, the question is to determine the set of forces and times to be exerted by the thrusters to get an adequate trajectory. Several constraints and simultaneous objectives to be optimized must be considered. Given the complexity of the multi-objective optimisation problem, it seems opportune to use Genetic Algorithms. The paper describes the problem to be solved, then explains how the GA were applied, and presents results for a set of cases considered, including obstacle avoidance

Evolution: Complexity, uncertainty and innovation

Complexity science provides a general mathematical basis for evolutionary thinking. It makes us face the inherent, irreducible nature of uncertainty and the limits to knowledge and prediction. Complex, evolutionary systems work on the basis of on-going, continuous internal processes of exploration, experimentation and innovation at their underlying levels. This is acted upon by the level above, leading to a selection process on the lower levels and a probing of the stability of the level above. This could either be an organizational level above, or the potential market place. Models aimed at predicting system behaviour therefore consist of assumptions of constraints on the micro-level – and because of inertia or conformity may be approximately true for some unspecified time. However, systems without strong mechanisms of repression and conformity will evolve, innovate and change, creating new emergent structures, capabilities and characteristics. Systems with no individual freedom at their lower levels will have predictable behaviour in the short term – but will not survive in the long term. Creative, innovative, evolving systems, on the other hand, will more probably survive over longer times, but will not have predictable characteristics or behaviour. These minimal mechanisms are all that are required to explain (though not predict) the co-evolutionary processes occurring in markets, organizations, and indeed in emergent, evolutionary communities of practice. Some examples will be presented briefly

Autonomous Drone Landings on an Unmanned Marine Vehicle using Deep Reinforcement Learning

This thesis describes with the integration of an Unmanned Surface Vehicle (USV) and an Unmanned Aerial Vehicle (UAV, also commonly known as drone) in a single Multi-Agent System (MAS). In marine robotics, the advantage offered by a MAS consists of exploiting the key features of a single robot to compensate for the shortcomings in the other. In this way, a USV can serve as the landing platform to alleviate the need for a UAV to be airborne for long periods time, whilst the latter can increase the overall environmental awareness thanks to the possibility to cover large portions of the prevailing environment with a camera (or more than one) mounted on it. There are numerous potential applications in which this system can be used, such as deployment in search and rescue missions, water and coastal monitoring, and reconnaissance and force protection, to name but a few. The theory developed is of a general nature. The landing manoeuvre has been accomplished mainly identifying, through artificial vision techniques, a fiducial marker placed on a flat surface serving as a landing platform. The raison d'etre for the thesis was to propose a new solution for autonomous landing that relies solely on onboard sensors and with minimum or no communications between the vehicles. To this end, initial work solved the problem while using only data from the cameras mounted on the in-flight drone. In the situation in which the tracking of the marker is interrupted, the current position of the USV is estimated and integrated into the control commands. The limitations of classic control theory used in this approached suggested the need for a new solution that empowered the flexibility of intelligent methods, such as fuzzy logic or artificial neural networks. The recent achievements obtained by deep reinforcement learning (DRL) techniques in end-to-end control in playing the Atari video-games suite represented a fascinating while challenging new way to see and address the landing problem. Therefore, novel architectures were designed for approximating the action-value function of a Q-learning algorithm and used to map raw input observation to high-level navigation actions. In this way, the UAV learnt how to land from high latitude without any human supervision, using only low-resolution grey-scale images and with a level of accuracy and robustness. Both the approaches have been implemented on a simulated test-bed based on Gazebo simulator and the model of the Parrot AR-Drone. The solution based on DRL was further verified experimentally using the Parrot Bebop 2 in a series of trials. The outcomes demonstrate that both these innovative methods are both feasible and practicable, not only in an outdoor marine scenario but also in indoor ones as well

Distributed Task-priority Based Control in Area Coverage & Adaptive Sampling

Abstract\u2014The paper presents the first simulative results and algorithmic developments of the task-priority based control applied to a distributed sampling network in an area coverage or adaptive sampling mission scenario. The proposed approach allowing the fulfilment of a chain of tasks with decreasing priority each of which directly related to both operability and safety aspects of the entire mission. The task-priority control is presented both in the centralized and decentralized implementations showing a comparison of performance. Finally simulations of the area coverage mission scenario are provided showing the effectiveness of the proposed approac