Abstract
Two important aspects in dealing with autonomous navigation of a swarm of drones are collision avoidance mechanism and formation control strategy; a possible competition between these two modes of operation may have negative implications for success and efficiency of the mission. This issue is exacerbated in the case of distributed formation control in leader-follower based swarms of drones since nodes concurrently decide and act through individual observation of neighbouring nodes' states and actions. To dynamically handle this duality of control, a plan of action for multi-priority control is required. In this paper, we propose a method for formation-collision co-awareness by adapting the thin-plate splines algorithm to minimize deformation of the swarm's formation while avoiding obstacles. Furthermore, we use a non-rigid mapping function to reduce the lag caused by such maneuvers. Simulation results show that the proposed methodology maintains the desired formation very closely in the presence of obstacles, while the response time and overall energy efficiency of the swarm is significantly improved in comparison with the existing methods where collision avoidance and formation control are only loosely coupled. Another important result of using non-rigid mapping is that the slowing down effect of obstacles on the overall speed of the swarm is significantly reduced, making our approach especially suitable for time critical missions.
Highlights
Resource utilization and decision making optimization in autonomous navigation for a swarm of robots is gaining traction in the research community [1]
Our approach focuses on integrating all these features together along with reducing the total energy of the system. Once these factors have been taken into account, and the pattern has been developed for switching between the formation maintenance and collision avoidance modes autonomously, the thin-plate splines technique is integrated into the algorithm in order to optimize it further by reducing the overall energy of the system
RELATED WORK Formation control algorithms can be categorized into three general approaches [10], [11], namely: 1) the virtual structure based approach, in which all the drones in the swarm formation are navigated as if there was a single big drone and the same trajectory is taken [12], [13]; 2) the leader-follower based approach, where every drone functions individually and autonomously by calibrating or altering its position according to the leader and maintaining its position in the formation as close as possible to the desired coordinates [14], [15]; and 3) the behaviour based approach, in which based on a pre-defined strategy the drone selects one of the multiple behaviours [16], [17]
Summary
Resource utilization and decision making optimization in autonomous navigation for a swarm of robots is gaining traction in the research community [1]. The proposed algorithm considers these factors by taking into account the strategy of maintaining the swarm formation dynamically with variable speeds of UAVs along with an efficient collision avoidance methodology To make it safe, each drone should obey a maximum possible distance from the obstacle and other drones. Once these factors have been taken into account, and the pattern has been developed for switching between the formation maintenance and collision avoidance modes autonomously, the thin-plate splines technique is integrated into the algorithm in order to optimize it further by reducing the overall energy of the system This technique helps by optimally reducing the disturbances caused by obstacle(s) by bringing the node(s) or UAVs back to their stable coordinates in a timely yet aggressive manner.
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