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In order to autonomously transfer from one point of the environment to the other, Autonomous Underwater Vehicles (AUV) need a navigational system. While navigating underwater the vehicles usually use a dead reckoning method which calculates vehicle movement on the basis of the information about velocity (sometimes also acceleration) and course (heading) provided by on-board devicesl ike Doppler Velocity Logs and Fibre Optical Gyroscopes. Due to inaccuracies of the devices and the influence of environmental forces, the position generated by the dead reckoning navigational system (DRNS) is not free from errors, moreover the errors grow exponentially in time. The problem becomes even more serious when we deal with small AUVs which do not have any speedometer on board and whose course measurement device is inaccurate. To improve indications of the DRNS the vehicle can emerge onto the surface from time to time, record its GPS position, and measure position error which can be further used to estimate environmental influence and inaccuracies caused by mechanisms of the vehicle. This paper reports simulation tests which were performed to determine the most effective method for correction of DRNS designed for a real Biomimetic AUV.

Usually, a rough software architecture designed for a robot can be can be shortly presented in the form of layers. The lowest layer is responsible for direct control of the hardware, i.e. engines, energy system, sensors, navigation devices, etc. A next layer is a low–level control which knows how to use the hardware in order to achieve a desired state of the robot, e.g. to stay on a desired course. And the last layer, the layer which is the nearest to the human–operator, is a high–level control which decides how to use the low–level control and sometimes also individual pieces of the hardware to achieve predefined objectives. The paper describes architecture, tasks and operation of the high–level control system (HLCS) designed for Biomimetic Autonomous Underwater Vehicle (BAUV).

A swarm of autonomous underwater vehicles can be a valuable alternative for fully equipped and very expensive super-vehicles. A distributed system of tightly cooperating vehicles can be cheaper, simpler in maintenance, more reliable, more flexible and universal than traditional single-vehicle systems. However, keeping a tight formation of underwater vehicles in the condition of the sea current, unclear environment, and rare inter-vehicle communication is a very challenging problem, which requires an effective vehicle control system. The paper proposes a solution to the above-mentioned problem, which is based on neuroevolution. Moreover, the paper also presents the first results of the proposed system. [ABSTRACT FROM AUTHOR]

Autonomous underwater vehicles are vehicles that are entirely or partly independent of human decisions. In order to obtain operational independence, the vehicles have to be equipped with specialized software. The task of the software is to move the vehicle along a trajectory while avoiding collisions. In its role of avoiding obstacles, the vehicle may sometimes encounter situations in which it is very difficult to determine what the next movement should be from an ad hoc perspective. When such a situation occurs, a planning component of the vehicle software should be run with the task of charting a safe trajectory between nearby obstacles. This paper presents a new path planning algorithm for a Biomimetic Autonomous Underwater Vehicle. The main distinguishing feature of the algorithm is its high speed compared with such classic planning algorithms as A*. In addition to presenting the algorithm, this paper also summarizes preliminary experiments intended to assess the effectiveness of the proposed algorithm. [ABSTRACT FROM AUTHOR]