Energy-Efficient Formation Morphing for Collision Avoidance in a Swarm of Drones

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.

Swarm formation morphing for congestion-aware collision avoidance

“…To evaluate the effectiveness of the proposed technique, we implemented the well-known techniques of shortest path based formation morphing and collision avoidance (EFMCA) and leader-follower formation (IOAA) presented in [10] and [14] and set the results side by side with the obtained results from the proposed technique in the similar experimental setup. Here we evaluate the performance in two different setups: normal and complex.…”

“…Our previous works have focused on: (1) energy-efficient formation morphing by systematic integration of formation control and collision avoidance for formation-collision co-awareness and the use of non-rigid mapping by utilizing a thin-plate splines (TPS) based algorithm to minimize deformation in the swarm [10] ; (2) reducing the energy consumption owing to sensor(s) usage in the swarm by introducing the concepts of translational coordinates based navigation and adaptive consciousness in the agents [23] , [36] ; and (3) dynamic formation reshaping for collision avoidance while passing through the available gaps between the obstacles without slowing down [37] .…”

“…We take inspiration from the technique presented in [10] and base the reformation function by utilizing point set registration [41] , [42] that is based on a well known technique used to data interpolation and smoothing issues, i.e., thin-plate splines (TPS) [43] . The amount by which the formation is distorted is assessed by the energy function as shown in Eq.…”

“…Navigation of a swarm of agents introduces several research challenges. Among these, the two most significant ones are formation maintenance and collision avoidance [10] . Collision avoidance systems are responsible for guiding an autonomous agent in order to safely and reliably avoid potential collisions with other agents in the swarm as well as with other objects in the environment [11] .…”

“…Moreover, formation maintenance algorithms are responsible for guiding the agents to maintain a certain/desired shape, i.e., location of every agent is defined with respect to other agents in the swarm [13] . Formation control approaches can be divided into the following three categories, i.e., leader-follower based approach [10] , [14] , behavior-based approach [15] , [16] , and virtual structure based approach [17] , [18] . Among the aforementioned methodologies, the leader-follower based approach is more common pertaining to its ease of analysis, robustness, scalability, and implementation [13] , [19] .…”