Andrew Lammas

Optimal route planning with prioritized task scheduling for AUV missions

This paper presents a solution to Autonomous Underwater Vehicles (AUVs) large scale route planning and task assignment joint problem. Given a set of constraints (e.g., time) and a set of task priority values, the goal is to find the optimal route for underwater mission that maximizes the sum of the priorities and minimizes the total risk percentage while meeting the given constraints. Making use of the heuristic nature of genetic and swarm intelligence algorithms in solving NP-hard graph problems, Particle Swarm Optimization (PSO) and Genetic Algorithm (GA) are employed to find the optimum solution, where each individual in the population is a candidate solution (route). To evaluate the robustness of the proposed methods, the performance of the all PS and GA algorithms are examined and compared for a number of Monte Carlo runs. Simulation results suggest that the routes generated by both algorithms are feasible and reliable enough, and applicable for underwater motion planning. However, the GA-based route planner produces superior results comparing to the results obtained from the PSO based route planner.

Efficient Path Re-planning for AUVs Operating in Spatiotemporal Currents

This paper presents an on-line dynamic path re-planning system for an autonomous underwater vehicle (AUV) to enable it to operate efficiently in a spatiotemporal, cluttered, and uncertain environment. The proposed strategy combines path re-planning with an evolutionary algorithm to adapt and regenerate the trajectory during the course of the mission using continuously updated current profiles from on-board sensors, such as a Horizontal Acoustic Doppler Velocity Logger. A quantum-behaved particle swarm optimization (QPSO) algorithm is used with a cost function which is based on the total time required to travel along the path segments accounting for the effect of space-time variable currents. The proposed path planner is designed to generate an optimal trajectory for an AUV navigating through a spatiotemporal ocean environment in the presence of irregularly shaped terrains as well as obstacles whose position coordinates are uncertain. Simulation results show that using the same on-board computation resources, the proposed path re-planning methodology with reuse of information gained from the previous planning history is able to obtain a more optimized trajectory than one relying on reactive path planning. Subsets of representative Monte Carlo simulations were run to analyse the performance of these dynamic planning systems. The results demonstrate the inherent robustness and superiority of the proposed planner based on path re-planning scheme when compared with the reactive path planning scheme.

Real-time quasi-optimal trajectory planning for autonomous underwater docking

In this paper, a real-time quasi-optimal trajectory planning scheme is employed to guide an autonomous underwater vehicle (AUV) safely into a funnel-shape stationary docking station. By taking advantage of the direct method of calculus of variation and inverse dynamics optimization, the proposed trajectory planner provides a computationally efficient framework for autonomous underwater docking in a 3D cluttered undersea environment. Vehicular constraints, such as constraints on AUV states and actuators; boundary conditions, including initial and final vehicle poses; and environmental constraints, for instance no-fly zones and current disturbances, are all modelled and considered in the problem formulation. The performance of the proposed planner algorithm is analyzed through simulation studies. To show the reliability and robustness of the method in dealing with uncertainty, Monte Carlo runs and statistical analysis are carried out. The results of the simulations indicate that the proposed planner is well suited for real-time implementation in dynamic and uncertain environment.

Path Planning for Rendezvous of Multiple AUVs Operating in a Variable Ocean

This paper presents a path planner for rendezvous of multiple autonomous underwater vehicles (AUVs) in turbulent, cluttered, and uncertain environments. The proposed strategy combines an Optimized Mass-center rendezvous point selection scheme with an evolutionary path planner to find trajectories for multiple AUVs with minimal time usage over all participating vehicles and simultaneous arrival of the vehicles at their selected rendezvous destination. A quantum-behaved particle swarm optimization (QPSO) algorithm is used with a cost function which is determined by the sum of time usage over all participating vehicles accounting for the effect of space-time variable currents and the sum of the waiting time of every vehicle. The proposed path planner is tested to generate optimal trajectories for rendezvous of multiple AUVs navigating through a variable ocean environment in the presence of irregularly shaped terrains as well as obstacles whose position coordinates are uncertain. Simulation results show that with integration of the Optimized Mass-center rendezvous point selection scheme, the proposed methodology is able to obtain more optimized trajectories for multiple AUVs than the ones relying on centroid, mass-center or optimized full-scale rendezvous point selection schemes.

Efficient path evaluation for AUVs using adaptive B-spline approximation

This paper presents an optimal and efficient path planner using adaptive B-spline approximation mechanism for Autonomous Underwater Vehicles (AUVs) operating in turbulent, cluttered and uncertain environments. The proposed method recursively inserts midpoint knots until an approximated B-Spline curve that satisfies the accuracy criteria is achieved. The method is able to adapt the quantity of internal knots inserted based on the specific needs of each path to conform to its respective desired smooth path and satisfy the accuracy criteria. Consequently, this method effectively minimizes the number of internal knots for any given trajectory, thus effectively improving the computation efficiency of the path fitness evaluation and hence path planning. The proposed method is integrated with a Genetic Algorithm (GA) based path planner and tested to generate an optimal trajectory for an AUV travelling through a turbulent ocean field in scenarios with uncertainty in position estimates. Simulation results show that the resulting approach is able to quickly and effectively guide the AUV to its destination with significant savings in computation time compared with B-Spline based planners with fixed numbers of internal nodes.

IDVD-based trajectory generator for autonomous underwater docking operations

Abstract This paper investigates capability and efficiency of utilizing the inverse dynamics in the virtual domain (IDVD) method to provide the real-time updates of feasible trajectory for an autonomous underwater vehicle (AUV) during underwater docking operations. The applicability of the IDVD method is examined for two scenarios. For the first scenario, referred to as an offline scenario, a nominal trajectory may be generated ahead of time based on a priori knowledge about the docking station (DS) pose (position and orientation). The second scenario, referred to as an online scenario, assumes some uncertainty in the DS pose; hence, the reference trajectory needs to be constantly recomputed in real time based on the updates about the DS pose. The offline scenario solution serves as a benchmark solution to check feasibility and optimality of generated trajectory subject to constraints on the states and controls. In particular, the offline solution can assist in making informed trade-off decisions between optimality of solution and computational efficiency. For the relatively simple offline scenario, the IDVD-method solution is compared with the Legendre–Gauss–Lobatto pseudo-spectral (LGLPS) method solution. The software-in-the-loop simulations and Monte Carlo trials are run for robustness assessment. Finally, the potential for the IDVD method to work online, in a closed-loop guidance system, is explored using a realistic cluttered operational simulation environment. Simulation results show that the IDVD-method based guidance system guarantees a reliable and efficient docking process by generating computationally efficient, feasible and ready to be tracked trajectories.

A comparison of optimization techniques for AUV path planning in environments with ocean currents

To date, a large number of optimization algorithms have been presented for Autonomous Underwater Vehicle (AUV) path planning. However, little effort has been devoted to compare these techniques. In this paper, an quantum-behaved particle swarm optimization (QPSO) algorithm is introduced for solving the optimal path planning problem of an AUV operating in environments with ocean currents. An extensive study of the most important optimization techniques applied to optimize the trajectory for an AUV in several test scenarios is presented. Extensive Monte Carlo trials were also run to analyse the performance of these optimization techniques based on solution quality and stability. The weaknesses and strengths of each technique have been stated and the most appropriate algorithm for AUV path planning has been determined. A QPSO algorithm is introduced for AUV path planners.Important optimization techniques applied to AUV path planning are compared in several test scenarios.Monte Carlo trials were also run to analyse the performance of these optimization techniques.The weaknesses and strengths of each optimization technique have been stated.

Time and energy efficient trajectory generator for autonomous underwater vehicle docking operations

This paper presents a novel real-time quasi-optimal trajectory generator, based on the inverse dynamics in the virtual domain (IDVD) method, to produce a reliable and efficient guidance system for autonomous underwater vehicle (AUV) docking operations. To this end, a challenging docking scenario is defined in a cluttered operating field, encompassing ocean currents and no-fly zones. Using the IDVD method, a trajectory that takes into consideration the hydrodynamic model of the vehicle is generated and the optimality of this trajectory, in regards to mission time and energy expenditure of the vehicle, is considered. Computer simulations demonstrate that the IDVD-based strategy enables the guiding of a vehicle into the dock by satisfying the final boundary conditions of the docks position and orientation. Generated trajectories are feasible, smooth and realizable using for the vehicles low-level auto-pilot module. In terms of computation, the solution is suitable for real-time implementation that incorporates uncertainty handling of the operating environment. For further analysis, the generated trajectory is evaluated on a high fidelity AUV simulator. The result of this latter test also demonstrates applicability of the utilized IDVD method for optimization of docking trajectories.

Improving navigational accuracy for AUVs using the MAPR Particle Filter

The objective of this paper is to compare the performance of the proposed measurement assisted partial resampling (MAPR) particle filter against the performance of the extended Kalman filter (EKF) within the context of a dynamic 6 DoF hydrodynamic system. In order to compare the respective performances of the above two filters in resolving a navigation solution, the filters are given a trajectory that closely resembles a raster scan mission, a typical mission for AUVs. This paper will show that the MAPR filter is capable of computing an estimate that, like the EKF, takes into account the dynamics of the system but like all particle filters also has the desired capability of estimating non Gaussian distributions and tracking nonlinear motion.

A 6 DoF Navigation Algorithm for Autonomous Underwater Vehicles

The objective of this paper is to compare the performance of a new proposed measurement assisted partial re-sampling (MAPR) filter against the performance of the extended Kalman filter and the mixture Monte Carlo localizer within the context of a navigation algorithm for a dynamic 6 DoF system. In this paper, an autonomous underwater vehicle (AUV) is used as the dynamic system. The performances of the above three filters in resolving a navigation solution are assessed by giving the AUV a sequence of trajectories that highlight the sensitivities of the navigation algorithm to noise. This paper demonstrates that the MAPR filter is capable of computing an estimate that, like the EKF, takes into account the dynamics of the system, but like all particle filters is also capable of estimating non-Gaussian distributions.