Collision avoidance between UAV clusters using swarm intelligence techniques

In this article, several basic swarming laws for Unmanned Aerial Vehicles (UAVs) are developed for both two-dimensional (2D) plane and three-dimensional (3D) space. Effects of these basic laws on the group behaviour of swarms of UAVs are studied. It is shown that when cohesion rule is applied an equilibrium condition is reached in which all the UAVs settle at the same altitude on a circle of constant radius. It is also proved analytically that this equilibrium condition is stable for all values of velocity and acceleration. A decentralised autonomous decision-making approach that achieves collision avoidance without any central authority is also proposed in this article. Algorithms are developed with the help of these swarming laws for two types of collision avoidance, Group-wise and Individual, in 2D plane and 3D space. Effect of various parameters are studied on both types of collision avoidance schemes through extensive simulations.

Search Strategies for Multiple UAV Search and Destroy Missions

Multiple UAVs are deployed to carry out a search and destroy mission in a bounded region. The UAVs have limited sensor range and can carry limited resources which reduce with use. The UAVs perform a search task to detect targets. When a target is detected which requires different type and quantities of resources to completely destroy, then a team of UAVs called as a coalition is formed to attack the target. The coalition members have to modify their route to attack the target, in the process, the search task is affected, as search and destroy tasks are coupled. The performance of the mission is a function of the search and the task allocation strategies. Therefore, for a given task allocation strategy, we need to devise search strategies that are efficient. In this paper, we propose three different search strategies namely; random search strategy, lanes based search strategy and grid based search strategy and analyze their performance through Monte-Carlo simulations. The results show that the grid based search strategy performs the best but with high information overhead.

Multiple UAV Coalitions for a Search and Prosecute Mission

Unmanned aerial vehicles (UAVs) have the potential to carry resources in support of search and prosecute operations. Often to completely prosecute a target, UAVs may have to simultaneously attack the target with various resources with different capacities. However, the UAVs are capable of carrying only limited resources in small quantities, hence, a group of UAVs (coalition) needs to be assigned that satisfies the target resource requirement. The assigned coalition must be such that it minimizes the target prosecution delay and the size of the coalition. The problem of forming coalitions is computationally intensive due to the combinatorial nature of the problem, but for real-time applications computationally cheap solutions are required. In this paper, we propose decentralized sub-optimal (polynomial time) and decentralized optimal coalition formation algorithms that generate coalitions for a single target with low computational complexity. We compare the performance of the proposed algorithms to that of a global optimal solution for which we need to solve a centralized combinatorial optimization problem. This problem is computationally intensive because the solution has to (a) provide a coalition for each target, (b) design a sequence in which targets need to be prosecuted, and (c) take into account reduction of UAV resources with usage. To solve this problem we use the Particle Swarm Optimization (PSO) technique. Through simulations, we study the performance of the proposed algorithms in terms of mission performance, complexity of the algorithms and the time taken to form the coalition. The simulation results show that the solution provided by the proposed algorithms is close to the global optimal solution and requires far less computational resources.

Optimal location of centre of gravity for swashplateless helicopter UAV and MAV

To investigate the use of centre of gravity location on reducing cyclic pitch control for helicopter UAV's (unmanned air vehicles) and MAV's (micro air vehicles). Low cyclic pitch is a necessity to implement the swashplateless rotor concept using trailing edge flaps or active twist using current generation low authority piezoceramic actuators. Design/methodology/approach – An aeroelastic analysis of the helicopter rotor with elastic blades is used to perform parametric and sensitivity studies of the effects of longitudinal and lateral center of gravity (cg) movements on the main rotor cyclic pitch. An optimization approach is then used to find cg locations which reduce the cyclic pitch at a given forward speed. Findings – It is found that the longitudinal cyclic pitch and lateral cyclic pitch can be driven to zero at a given forward speed by shifting the cg forward and to the port side, respectively. There also exist pairs of numbers for the longitudinal and lateral cg locations which drive both the cyclic pitch components to zero at a given forward speed. Based on these results, a compromise optimal cg location is obtained such that the cyclic pitch is bounded within ±5° for a BO105 helicopter rotor. Originality/value – The reduction in the cyclic pitch due to helicopter cg location is found to significantly reduce the maximum magnitudes of the control angles in flight, facilitating the swashplateless rotor concept. In addition, the existence of cg locations which drive the cyclic pitches to zero allows for the use of active cg movement as a way to replace the cyclic pitch control for helicopter MAV's.

Multi-UAV Task Allocation using Team Theory

A multiple UAV search and attack mission in a battlefield involves allocating UAVs to different target tasks efficiently. This task allocation becomes difficult when there is no communication among the UAVs and the UAVs sensors have limited range to detect the targets and neighbouring UAVs, and assess target status. In this paper, we propose a team theoretic approach to efficiently allocate UAVs to the targets with the constraint that UAVs do not communicate among themselves and have limited sensor range. We study the performance of team theoretic approach for task allocation on a battle field scenario. The performance obtained through team theory is compared with two other methods, namely, limited sensor range but with communication among all the UAVs, and greedy strategy with limited sensor range and no communication. It is found that the team theoretic strategy performs the best even though it assumes limited sensor range and no communication.

UAV Grouping and Coordination Tactics for Ground Attack Missions

Grouping and coordination tactics for ground attack missions by a heterogeneous mix of reconnaissance, enemy suppression, and attack unmanned aerial vehicles (UAVs) is presented. Dubins' paths are used to determine the optimal number of attack UAVs and their positional and heading freedoms, as functions of weapon seeker range and field of view. A generic battlefield scenario with layered defense is created and the tactics are evaluated on a Group Flyer simulation platform for both nominal and off-nominal conditions.

Cooperative Communication of UAV to Perform Multi-task Using Nature Inspired Techniques

In this paper, the approach for assigning cooperative communication of Uninhabited Aerial Vehicles (UAV) to perform multiple tasks on multiple targets is posed as a combinatorial optimization problem. The multiple task such as classification, attack and verification of target using UAV is employed using nature inspired techniques such as Artificial Immune System (AIS), Particle Swarm Optimization (PSO) and Virtual Bee Algorithm (VBA). The nature inspired techniques have an advantage over classical combinatorial optimization methods like prohibitive computational complexity to solve this NP-hard problem. Using the algorithms we find the best sequence in which to attack and destroy the targets while minimizing the total distance traveled or the maximum distance traveled by an UAV. The performance analysis of the UAV to classify, attack and verify the target is evaluated using AIS, PSO and VBA.