Pursuit and Evasion of Drone Swarms and Turrets



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The proliferation of inexpensive remotely-controlled drones makes large drone swarms a present reality. Swarms of drones can be useful for overwhelming defensive positions and then loitering in hostile areas near potential adversaries. Even small commercially-available quadcopters can be armed with explosives and can become a significant threat to military forces, as seen in current conflicts. While solutions for countering drones are in development, the problem of countering a swarm of drones falls on existing fixed defensive systems and includes several challenges for the defender. The pursuit-evasion problem that arises from the need to engage each of the drones in the swarm in an efficient way and subject to constraints is an example of a Moving Target Traveling Salesman Problem with Time Windows (MTTSPTW).

A defending turret must visit each attacking drone in the swarm once, as quickly as possible to counter the threat they pose. This constitutes a Shortest Hamiltonian Path (SHP) through the swarm subject to the time-window constraints of visiting each drone before it can reach the defending turret. Finding this path is made more difficult when the swarm can alter its configuration, changing the relative distances between drones. The rapid pace of engagement, computational complexity, and changing constraints combine to make optimal solvers infeasible for all but a small number of drones. We determined the optimal theoretical solution for one and two drones and investigated the problem of larger swarms in two and three dimensions. We present an analysis of a critical limitation of existing pan-tilt turrets and the swarm strategy to exploit it. This work presents a comparison of two greedy and two hybrid targeting strategies to counter a swarm, validated by the simulation of random swarms. Using a two-pass approach, with targets grouped by proximity and the path selected by shortest travel distance, provided the best results.

Once the drones have reached the proximity around a defensive position, they can disable it and loiter in the area. This dissertation investigates the bulk movement patterns of the swarm to avoid intra-swarm collisions, while moving the swarm in a hostile area. Using Virtual Reality simulations, we compared the effectiveness of a selection of swarm motion-plans in evading fire from a hostile human user. Parametric knot-like patterns with small random motions provided the best survivability against human adversaries.



Pursuit-Evasion, Drones


Portions of this document appear in: Biediger, D. & Becker, A. T. (2022). Threat-Aware Selection for Target Engagement, In 18th IEEE International Conference on Automation Science and Engineering, CASE 2022, Mexico City, Mexico; and in: Biediger, D., Popov, L. & Becker, A. T. (2021). The Pursuit and Evasion of Drones Attacking an Automated Turret. CoRR, abs/2107.12660arXiv 2107.12660. https://arxiv.org/abs/2107.12660; and in: Biediger, D., Popov, L. & Becker, A. T. (2021). The Pursuit and Evasion of Drones Attacking an Automated Turret, In 2021 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS). https://doi.org/10.1109/IROS51168.2021.9636731; and in: Sultan, M. M., Biediger, D., Li, B. & Becker, A. T. (2021). The Reachable Set of a Drone: Exploring the Position Isochrones for a Quadcopter, In IEEE International Conference on Robotics and Automation, ICRA 2021, Xi’an, China, May 30 - June 5, 2021, IEEE. https://doi.org/10.1109/ICRA48506.2021.9561715; and in: Biediger, D., Mahadev, A. & Becker, A. T. (2019). Investigating the survivability of drone swarms with flocking and swarming flight patterns using virtual reality, In 15th IEEE International Conference on Automation Science and Engineering, CASE 2019, Vancouver, BC, Canada, august 22-26, 2019, IEEE. https://doi.org/10.1109/COASE.2019.8843173