An Intelligent, Heuristic Path Planner for Multiple Agent Unmanned Air Systems

Abstract

In situ observations of atmospheric variables are currently obtained with radiosondes, collecting data along uncontrolled trajectories. As an alternative, we propose an unmanned air system comprising a swarm of unmanned aerial vehicles, released from high altitude weather balloons. Their trajectories are optimised for efficient sampling, with an objective function measuring the space-filling properties of the entire swarm. The dynamics of the aircraft swarm are captured in a number of primitive manoeuvres simulated with JSBSim. A combination of these paths forms a set of moves available to a greedy heuristic algorithm, which determines which of the flyable paths is optimal according to the objective function. At each heuristic optimisation step a decision is made upon the next single move and, once the move is complete, the heuristic repeated, resulting in a stitching together of optimal moves from the flyable set. The proposed path planner comprises of a centralised algorithm, which executes offline, thus, each aircraft executes the heuristic synchronously and the result is a cloud of waypoints to be flown by the swarm. A case study based on orographic flows over South Georgia is used to test the performance of the algorithm in windy environments. The results indicate good performance of the algorithm, even in high, unsteady wind fields. I. MotivationMost commonly, balloon and aircraft launched radiosondes are utilised to collect physical, chemical and biological measurements in the atmosphere. These are predominantly used to assist the modelling of atmospheric processes from large global systems to small localised phenomena. Although relatively easy to deploy, balloon and aircraft launched radiosondes have disadvantages. The sonde is usually lost, which incurs an expense and only data transmitted during the flight can be recovered. The most notable disadvantage is the sparsity of the data collected. The sondes collect their information on an uncontrolled trajectory, determined by the flight of the (on the way up) balloon or the parachute (on the way down), which provides for limited control of the sampling locations.

Collaborative online planning for automated victim search in disaster response

“…Also several biologically inspired methods exist [27,28,29], and some use potential field based approaches in unstructured environments [30] that is beneficial when unexpected obstacles appear, but requires the tuning of several parameters to adopt to different settings. When the motion capabilities are limited as for fixed-wing UAVs, performing a tree search within the feasible paths can be used to control the vehicles [17,31,32] (detailed in Section 2.4). Unfortunately, to use such techniques for long-term online planning, the action space has to be significantly reduced by either discretising the problem [16] or limiting the action space to a set of predefined search patterns [15].…”

Comparison of A* and RRT in real–time 3D path planning of UAVs

“…14 Moreover, UAVs can be utilised in situations where the mission is too difficult or too dangerous for human pilots such as monitor critical structures in natural disasters, search and rescue and monitor weather inside a storm. 15 The path planning algorithms utilise sensory, processing and actuator systems to generate paths in view of different kinematic, dynamic 16,17 and environmental 18,19 time-varying constraints. Once a path is generated through the path planning algorithm, a path following algorithm will generate the control parameters for the UAV to follow the generated path.…”

Advancements for A* and RRT in 3D path planning of UAVs

“…Path planning is the process of automatically generating feasible and optimal 2D 2, 3 or 3D 4,5 paths to a predefined destination in view of weather, 7,8 sensory and model constraints 9, 10 and uncertainties. 11,12 Different path planning algorithm were proposed each with their strongholds and drawbacks.…”