Despite advancements, the existing collision avoidance systems that are currently regulated within the civilian airspace were unable to integrate both manned and unmanned aircraft. The research discussed in this post therefore presents the design, implementation, and verification of two types of cooperative collision avoidance algorithms for UAVs in multi-aircraft conflict scenarios.
UAVs are aircraft and rotorcraft without pilots or passengers that are operated remotely and have a high degree of autonomy. UAVs are used in numerous applications such as aerial photography, product delivery, search and rescue, wildlife surveys, weather monitoring, agriculture and aerial crop surveys, construction, journalism, law enforcement, military reconnaissance, and visual inspection of infrastructures like bridges, roads, and power lines. These vehicles can range in size from the size of an insect to a heavy-lift drone that can carry hundreds of pounds.
To approach the research, two types of collision avoidance algorithms were developed and verified in simulation: a rules-based algorithm and a cooperative path planning-based algorithm. The rules-based collision avoidance algorithm mimics the tactical Traffic Collision Avoidance System (TCAS) that is used on commercial passenger airliners. To enable multi-aircraft collision avoidance, two methods for combining the pairwise rules-based collision avoidance actions were proposed, namely Resolution Action Superposition (RAS) and pairwise Closest-Intruder-First (CIF).
A simulation environment was created to test both the rules-based and path planning based collision avoidance algorithms. In this regard, set-piece conflict avoidance scenarios were performed to produce illustrative results. The simulations illustrated that both rules-based and path planning based collision avoidance can resolve both pairwise and multi-aircraft conflicts. Furthermore, Monte Carlo simulations were performed to produce statistical results and evaluate the performance of both algorithms in random conflict scenarios. The Monte Carlo simulation is a computerised mathematical technique that allows people to account for risk in quantitative analysis and decision making.
The simulation results show that both the rules-based and path planning based solutions can successfully resolve collision scenarios involving multiple unmanned aerial vehicles. The rules-bases solution requires less computational effort but does not optimise the collision avoidance plans. The path planning based solution requires much more computational effort but provides optimal solutions that minimise the deviation from the original flights and the control effort of the avoidance actions.
The following recommendation and future work references were made on the cooperative path planning for multiple UAVs over a wireless reliable communication network.
Based on the following research dissertation: https://scholar.sun.ac.za/handle/10019.1/108293