Task Assignment and Path Planning for Multiple Autonomous Underwater Vehicles using 3D Dubins Curves

This paper investigates the task assignment and path planning problem for multiple AUVs in three dimensional (3D) underwater wireless sensor networks where nonholonomic motion constraints of underwater AUVs in 3D space are considered. The multi-target task assignment and path planning problem is modeled by the Multiple Traveling Sales Person (MTSP) problem and the Genetic Algorithm (GA) is used to solve the MTSP problem with Euclidean distance as the cost function and the Tour Hop Balance (THB) or Tour Length Balance (TLB) constraints as the stop criterion. The resulting tour sequences are mapped to 2D Dubins curves in the X − Y plane, and then interpolated linearly to obtain the Z coordinates. We demonstrate that the linear interpolation fails to achieve G1 continuity in the 3D Dubins path for multiple targets. Therefore, the interpolated 3D Dubins curves are checked against the AUV dynamics constraint and the ones satisfying the constraint are accepted to finalize the 3D Dubins curve selection. Simulation results demonstrate that the integration of the 3D Dubins curve with the MTSP model is successful and effective for solving the 3D target assignment and path planning problem

Control of free-ranging automated guided vehicles in container terminals

Container terminal automation has come to the fore during the last 20 years to improve their efficiency. Whereas a high level of automation has already been achieved in vertical handling operations (stacking cranes), horizontal container transport still has disincentives to the adoption of automated guided vehicles (AGVs) due to a high degree of operational complexity of vehicles. This feature has led to the employment of simple AGV control techniques while hindering the vehicles to utilise their maximum operational capability. In AGV dispatching, vehicles cannot amend ongoing delivery assignments although they have yet to receive the corresponding containers. Therefore, better AGV allocation plans would be discarded that can only be achieved by task reassignment. Also, because of the adoption of predetermined guide paths, AGVs are forced to deploy a highly limited range of their movement abilities while increasing required travel distances for handling container delivery jobs. To handle the two main issues, an AGV dispatching model and a fleet trajectory planning algorithm are proposed. The dispatcher achieves job assignment flexibility by allowing AGVs towards to container origins to abandon their current duty and receive new tasks. The trajectory planner advances Dubins curves to suggest diverse optional paths per origin-destination pair. It also amends vehicular acceleration rates for resolving conflicts between AGVs. In both of the models, the framework of simulated annealing was applied to resolve inherent time complexity. To test and evaluate the sophisticated AGV control models for vehicle dispatching and fleet trajectory planning, a bespoke simulation model is also proposed. A series of simulation tests were performed based on a real container terminal with several performance indicators, and it is identified that the presented dispatcher outperforms conventional vehicle dispatching heuristics in AGV arrival delay time and setup travel time, and the fleet trajectory planner can suggest shorter paths than the corresponding Manhattan distances, especially with fewer AGVs.Open Acces

Optimal Control of Two-Wheeled Mobile Robots for Patrolling Operations

Optimal Control of Two-Wheeled Mobile Robots for Patrolling Operations Walaaeldin Ahmed Ghadiry, Concordia University, 2015 This work studies the use of the two-wheeled mobile robots in patrolling operations, and provides the most distance-e�cient as well as time-e�cient trajectories to patrol a given area. Novel formulations in the context of constrained optimization are introduced which can be solved using existing software. The main concept of the problem is directly related to the well-known Traveling Salesman Problem (TSP) and its variants, where a salesman starts from a base city and visits a number of other cities with minimum travel distance while satisfying the constraint that each city has to be visited only once. Finally, the salesman returns back to the starting base city after completing the mission. Two di�erent patrolling con�gurations that are related to the TSP and its variants, namely the Single Depot multiple Traveling Salesman Problem (mTSP) and the Multidepot multiple Traveling Salesman Problem (MmTSP) are investigated. Novel algorithms are introduced for the trajectory planning of multiple two-wheeled mobile robots, either with two di�erential motors (which can turn on the spot) or with Dubins-like vehicles. The output trajectories for both types of wheeled robots are investigated by using a model predictive control scheme to ensure their kinematic feasibility for the best monitoring performance. The proposed formulations and algorithms are veri�ed by a series of simulations using e�cient programming and optimization software as well as experimental tests in the lab environment

Coverage Path Planning for a Moving Vehicle

A simple coverage plan called a Conformal Lawn Mower plan is demonstrated. This plan enables a UAV to fully cover the route ahead of a moving ground vehicle. The plan requires only limited knowledge of the ground vehicle's future path. For a class of curvature-constrained ground vehicle paths, the proposed plan requires a UAV velocity that is no more than twice the velocity required to cover the optimal plan. Necessary and sufficient UAV velocities, relative to the ground vehicle velocity, required to successfully cover any path in the curvature restricted set are established. In simulation, the proposed plan is validated, showing that the required velocity to provide coverage is strongly related to the curvature of the ground vehicle's path. The results also illustrate the relationship between mapping requirements and the relative velocities of the UAV and ground vehicle. Next, I investigate the challenges involved in providing timely mapping information to a moving ground vehicle where the path of that vehicle is not known in advance. I establish necessary and sufficient UAV velocities, relative to the ground vehicle velocity, required to successfully cover any path the ground vehicle may follow. Finally, I consider a reduced problem for sensor coverage ahead of a moving ground vehicle. Given the ground vehicle route, the UAV planner calculates the regions that must be covered and the time by which each must be covered. The UAV planning problem takes the form of an Orienteering Problem with Time Windows (OPTW). The problem is cast the problem as a Mixed Integer Linear Program (MILP) to find a UAV path that maximizes the area covered within the time constraints dictated by the moving ground vehicle. To improve scalability of the proposed solution, I prove that the optimization can be partitioned into a set of smaller problems, each of which may be solved independently without loss of overall solution optimality. This divide and conquer strategy allows faster solution times, and also provides higher-quality solutions when given a fixed time budget for solving the MILP. We also demonstrate a method of limited loss partitioning, which can perform a trade-off between improved solution time and a bounded objective loss