Effect of Communication Delays on the Successful Coordination of a Group of Biomimetic AUVs

In this paper, the influence of delays on the ability of a formation control algorithm to coordinate a group of twelve Biomimetic Autonomous Underwater Vehicles (BAUVs) is investigated. In this study the formation control algorithm is a decentralized methodology based on the behavioural mechanisms of fish within school structures. Incorporated within this algorithm is a representation of the well-known and frequently used communication protocol, Time-Division-Multiple-Access (TDMA). TDMA operates by assigning each vehicle a specific timeslot during which it can broadcast to the remaining members of the group. The size of this timeslot varies depending on a number of operational parameters such as the size of the message being transmitted, the hardware used and the distance between neighbouring vehicles. Therefore, in this work, numerous timeslot sizes are tested that range from theoretical possible values through to values used in practice. The formation control algorithm and the TDMA protocol have been implemented within a validated mathematical of the RoboSalmon BAUV designed and manufactured at the University of Glasgow. The results demonstrate a significant deterioration in the ability of the formation control algorithms as the timeslot size is increased. This deterioration is due to the fact that as the timeslot size is increased, the interim period between successive communication updates increases and as a result, the error between where the formation control algorithm estimates each vehicle to be and where they actually are, increases. As a result, since the algorithm no longer has an accurate representation of the positioning of neighbouring vehicles, it is no longer capable of selecting the correct behavioural equation and subsequently, is unable to coordinate the vehicles to form a stable group structure

Adaptive cancelation of self-generated sensory signals in a whisking robot

Sensory signals are often caused by one's own active movements. This raises a problem of discriminating between self-generated sensory signals and signals generated by the external world. Such discrimination is of general importance for robotic systems, where operational robustness is dependent on the correct interpretation of sensory signals. Here, we investigate this problem in the context of a whiskered robot. The whisker sensory signal comprises two components: one due to contact with an object (externally generated) and another due to active movement of the whisker (self-generated). We propose a solution to this discrimination problem based on adaptive noise cancelation, where the robot learns to predict the sensory consequences of its own movements using an adaptive filter. The filter inputs (copy of motor commands) are transformed by Laguerre functions instead of the often-used tapped-delay line, which reduces model order and, therefore, computational complexity. Results from a contact-detection task demonstrate that false positives are significantly reduced using the proposed scheme

Wireless aquatic navigator for detection and analysis (WANDA)

The cost of monitoring and detecting pollutants in natural waters is of major concern. Current and forthcoming bodies of legislation will continue to drive demand for spatial and selective monitoring of our environment, as the focus increasingly moves towards effective enforcement of legislation through detection of events, and unambiguous identification of perpetrators. However, these monitoring demands are not being met due to the infrastructure and maintenance costs of conventional sensing models. Advanced autonomous platforms capable of performing complex analytical measurements at remote locations still require individual power, wireless communication, processor and electronic transducer units, along with regular maintenance visits. Hence the cost base for these systems is prohibitively high, and the spatial density and frequency of measurements are insufficient to meet requirements. In this paper we present a more cost effective approach for water quality monitoring using a low cost mobile sensing/communications platform together with very low cost stand-alone ‘satellite’ indicator stations that have an integrated colorimetric sensing material. The mobile platform is equipped with a wireless video camera that is used to interrogate each station to harvest information about the water quality. In simulation experiments, the first cycle of measurements is carried out to identify a ‘normal’ condition followed by a second cycle during which the platform successfully detected and communicated the presence of a chemical contaminant that had been localised at one of the satellite stations

Autonomous Optimization of Swimming Gait in a Fish Robot With Multiple Onboard Sensors

Autonomous gait optimization is an essential survival ability for mobile robots. However, it remains a challenging task for underwater robots. This paper addresses this problem for the locomotion of a bio-inspired robotic fish and aims at identifying fast swimming gait autonomously by the robot. Our approach for learning locomotion controllers mainly uses three components: 1) a biological concept of central pattern generator to obtain specific gaits; 2) an onboard sensory processing center to discover the environment and to evaluate the swimming gait; and 3) an evolutionary algorithm referred to as particle swarm optimization. A key aspect of our approach is the swimming gait of the robot is optimized autonomously, equivalent to that the robot is able to navigate and evaluate its swimming gait in the environment by the onboard sensors, and simultaneously run a built-in evolutionary algorithm to optimize its locomotion all by itself. Forward speed optimization experiments conducted on the robotic fish demonstrate the effectiveness of the developed autonomous optimization system. The latest results show that our robotic fish attained a maximum swimming speed of 1.011 BL/s (40.42 cm/s) through autonomous gait optimization, faster than any of the robot's previously recorded speeds

Design and development of autonomous robotic fish for object detection and tracking

In this article, an autonomous robotic fish is designed for underwater operations like object detection and tracking along with collision avoidance. The computer-aided design model for prototype robotic fish is designed using the Solid Works® software to export an stereolithography (STL) file to MakerBot, a 3D printer, to manufacture the parts of robotic fish using polylactic acid thermoplastic polymer. The precise maneuverability of the robotic fish is achieved by the propulsion of a caudal fin. The oscillation of the caudal fin is controlled by a servomotor. A combination of visual and ultrasonic sensors is used to track the position and distance of the desired object with respect to the fish and also to avoid the obstacles. The robotic fish has the ability to detect an object up to a distance of 90 cm at normal exposure conditions. A computational fluid dynamics analysis is conducted to analyze the fluid hydrodynamics (flow rate of water and pressure) around the hull of a robotic fish and the drag force acting on it. A series of experimental results have shown the effectiveness of the designed underwater robotic fish. </jats:p