Employing Multiple Unmanned Aerial Vehicles for Co-Operative Path Planning

Abstract

In this paper, we work to develop a path planning solution for a group of Unmanned Aerial Vehicles (UAVs) using a Mixed Integer Linear Programming (MILP) approach. Co-operation among team members not only helps reduce mission time, it makes the execution more robust in dynamic environments. However, the problem becomes more challenging as it requires optimal resource allocation and is NP-hard. Since UAVs may be lost or may suffer significant damage during the course of the mission, plans may need to be modified in real-time as the mission proceeds. Therefore, multiple UAVs have a better chance of completing a mission in the face of failures. Such military operations can be treated as a variant of the Multiple Depot Vehicle Routing Problem (MDVRP). The proposed solution must be such that m UAVs start from multiple source locations to visit n targets and return to a set of destination locations such that (1) each target is visited exactly by one of the chosen UAVs (2) the total distance travelled by the group is minimized and (3) the number of targets that each UAV visits may not be less than K or greater than L.

UAV Mission Planning Resistant to Weather Uncertainty

“…Air density is a function of humidity, air pressure, and temperature [3,15]. The current state of research has yet to consider weather factors and assumes the weather has a negligible impact on performance [2,16,31,32]. Rarely has research included consideration of wind conditions' impact on energy consumption while concurrently using that information in planning the missions of UAVs [2,33,34], with only a few contributions identifiable in current state.…”

“…A number of contributions have proposed to subdivide the mission area taking into account UAVs' relative capabilities and to cluster the subsequent smaller areas to reduce the problem size [3,4,31,35,36].…”

“…Certain studies in the current state have used strategies to cluster the network to reduce the problem complexity [31,[35][36][37]. Utilizing this as the foundation, this study proposes to cluster customer nodes and for each customer cluster a set of feasible weather-resistant UAV fleet schedules with routes are created.…”

Ensemble Coordination Approach in Multi-AGV Systems Applied to Industrial Warehouses

“…A solution for the complexity of large-scale multirobot coordination problem is provided by decentralized approaches (see, for instance, [10] and references therein). In these methods, each robot autonomously determines its routes, dissolving the conflicts and collecting information from other robots [11], [12]. Decentralized techniques are generally faster than centralized ones, but they present several drawbacks, such as failing in finding valid paths for all the robots due to deadlocks [13].…”

Robot Path Optimization Based on a Reference Model and Sigmoid Functions

“…Path optimization techniques encompass pseudospectral optimization [4], elastic bands [5], or bioinspired algorithms such as ant colony optimization [6] among many others. Applications include but are not limited to aerial vehicle navigation [7], underwater vehicle command [8], and motion of robot platforms [9]. Almost every application requiring motion can benefit from optimization, which offers potential savings in energy and more natural and intuitive paths for devices.…”