The NOPTILUS project: Autonomous Multi-AUV Navigation for Exploration of Unknown Environments

  title={The NOPTILUS project: Autonomous Multi-AUV Navigation for Exploration of Unknown Environments},
  author={Savvas A. Chatzichristofis and Athanasios Ch. Kapoutsis and Elias B. Kosmatopoulos and Lefteris Doitsidis and D. V. Rovas and Jo{\~a}ao Borges de Sousa},
  journal={IFAC Proceedings Volumes},
Abstract Current multi-AUV systems are far from being capable of fully autonomously taking over real-life complex situation-awareness operations. As such operations require advanced reasoning and decision-making abilities, current designs have to heavily rely on human operators. The involvement of humans, however, is by no means a guarantee of performance; humans can easily be over-whelmed by the information overload, fatigue can act detrimentally to their performance, properly coordinating… 

Figures from this paper

A distributed architecture for supervision of autonomous multi-robot missions
HiDDeN is presented, a distributed deliberative architecture that manages the execution of a hierarchical plan that ensures operational constraints while reducing the need of communication between robots, as communication may be intermittent or even nonexistent when the robots operate in completely separate environments.
A Multi-Objective Exploration Strategy for Mobile Robots Under Operational Constraints
This work model the environment constraints in cost functions and utilize the cognitive-based adaptive optimization algorithm to meet time-critical objectives and produces an exploration path that is optimal in the sense of globally minimizing the required time as well as maximizing the explored area of a partially unknown workspace.
Real-Time Active SLAM and Obstacle Avoidance for an Autonomous Robot Based on Stereo Vision
A modified version of the so-called cognitive-based adaptive optimization algorithm is introduced for the robot to successfully complete its tasks in real time and avoid any local minima entrapment.
An improved multi-AUV patrol path planning method
A multi-AUV cooperative path planning optimization algorithm is proposed, which makes the detection of the updated particles and avoids the generation of infeasible particles and has better patrol performance in line with the actual application environment.
Managing communication challenges in vehicle networks for remote maritime operations
This paper presents operational vignettes where the viability of autonomous teams' use in larger scale scenarios is increased and new concepts of operation must be explored, alongside planning and control software, to ascertain operational limitations.
The transport of underwater vehicles is proposed to be carried out with an unmanned surface vessel, equipped with actuators for the automatic release of a group of vehicles under water and receiving on board after the end of the underwater mission.
Rendezvous Path Planning for Multiple Autonomous Marine Vehicles
In this paper, a distributed shell-space decomposition (DSSD) scheme is proposed for rendezvous trajectory planning of multiple autonomous marine vehicles (AMVs); this category of vehicle includes
A Predictive Guidance Obstacle Avoidance Algorithm for AUV in Unknown Environments
The simulation results show that the PGOA algorithm can better predict the trajectory point of the obstacle avoidance path of AUV, and the secondary optimization function can successfully achieve collision avoidance for different complex obstacle environments.
Collision Tolerant Packet Scheduling for Underwater Acoustic Localization
The joint problem of packet scheduling and self-localization in an underwater acoustic sensor network where sensor nodes are distributed randomly in an operating area is considered, and an iterative Gauss-Newton algorithm is employed by each sensor node for self- localization.


Cognitive-based adaptive control for cooperative multi-robot coverage
The proposed method, based on a new cognitive-based, adaptive optimization algorithm (CAO), allows getting coordinated and scalable controls to accomplish the task, even when the obstacles are unknown and the team is heterogeneous, i.e. each robot is equipped with a different type of visual sensor.
Multi-Robot 3D Coverage of Unknown Areas
A new approach based on the Cognitive-ba sed Adaptive Optimization (CAO) algorithm is proposed and evaluated, establishing that the proposed approach provides a scalable and efficient methodology that inco rporates any particular physical constraints and limitations able to navigate the robots to an arrangement that (locally) optimizes surveillance coverage.
Multi-robot three-dimensional coverage of unknown areas
Rigorous mathematical arguments and extensive simulations establish that the proposed approach provides a scalable and efficient methodology that incorporates any particular physical constraints and limitations used to navigate the robots into an arrangement that (locally) optimizes surveillance coverage.
Global A-Optimal Robot Exploration in SLAM
  • Robert Sim, N. Roy
  • Computer Science
    Proceedings of the 2005 IEEE International Conference on Robotics and Automation
  • 2005
It is shown that optimizing the a-optimal information measure results in a more accurate map than existing approaches, using a greedy, closed-loop strategy, and that by restricting the planning to an appropriate policy class, one can tractably find non-greedy, global planning trajectories that produce more accurate maps.
3D surveillance coverage using maps extracted by a monocular SLAM algorithm
A two-step centralized procedure to align optimally a swarm of flying vehicles for the aforementioned task, using the cognitive adaptive methodology initially introduced in [1], [2].
Optimal Motion Strategies for Range-Only Constrained Multisensor Target Tracking
Two algorithms are proposed, modified Gauss-Seidel relaxation and linear programming (LP) relaxation, for determining the set of feasible locations that each sensor should move to in order to collect the most informative measurements; i.e., distance measurements that minimize the uncertainty about the position of the target.
A solution to the simultaneous localization and map building (SLAM) problem
The paper proves that a solution to the SLAM problem is indeed possible and discusses a number of key issues raised by the solution including suboptimal map-building algorithms and map management.
Power-SLAM: a linear-complexity, anytime algorithm for SLAM
Simulation and experimental results are presented that demonstrate the accuracy of the proposed algorithm (Power-SLAM) when compared to the standard EKF-based SLAM with quadratic computational cost and two linear-complexity competing alternatives.
Analysis of Positioning Uncertainty in Cooperative Localization and Target Tracking ( CLATT )
In this report, we study the positioning accuracy of Cooperative Localization and Target Tracking (CLATT) in a network of mobile robots, and derive analytical upper bounds for the positioning
Large Scale Nonlinear Control System Fine-Tuning Through Learning
A new learning/adaptive algorithm that can provide with convergent, an efficient and safe fine-tuning of general LNCS is introduced and it significantly outperforms the algorithms proposed by Kosmatopoulos as well as other existing adaptive optimization algorithms.