Mehdi Alighanbari

Cooperative path planning for multiple UAVs in dynamic and uncertain environments

This paper addresses the problem of cooperative path planning for a fleet of unmanned aerial vehicles (UAVs). The paths are optimized to account for uncertainty/adversaries in the environment by modeling the probability of UAV loss. The approach extends prior work by coupling the failure probabilities for each UAV to the selected missions for all other UAVs. In order to maximize the expected mission score, this stochastic formulation designs coordination plans that optimally exploit the coupling effects of cooperation between UAVs to improve survival probabilities. This allocation is shown to recover real-world air operations planning strategies, and to provide significant improvements over approaches that do not correctly account for UAV attrition. The algorithm is implemented in an approximate decomposition approach that uses straight-line paths to estimate the time-of-flight and risk for each mission. The task allocation for the UAVs is then posed as a mixed-integer linear program that can be solved using CPLEX.

Coordination and control of multiple UAVs with timing constraints and loitering

This paper describes methods for optimizing the task allocation problem for a fleet of unmanned aerial vehicles (UAVs) with tightly coupled tasks and rigid relative timing constraints. The overall objective is to minimize the mission completion time for the fleet, and the task assignment must account for differing UAV capabilities and no-fly zones. Loitering times are included as extra degrees of freedom in the problem to help meet the timing constraints. The overall problem is formulated using mixed-integer linear programming (MILP), which gives the globally optimal solution. An approximate decomposition solution method is also used to overcome the computational issues that arise when using MILP for larger problems. The problem is also posed in a way that can be solved using Tabu search. This approach is demonstrated to provide good solutions in reasonable computation times for large problems that are very difficult to solve using the exact or approximate decomposition methods.

Decentralized Task Assignment for Unmanned Aerial Vehicles

This paper investigates the problem of decentralized task assignment for a fleet of UAVs. Centralized task assignment for a fleet of UAVs is often not practical due to communication limits, robustness issues, and scalability, and using a distributed approach can mitigate many of these problems. One recently proposed decentralized approach is to replicate the central assignment algorithm on each UAV. The success of this implicit coordination strongly depends on the assumption that all UAVs have the same situational awareness. Examples are presented in this paper to show that this consensus in the information is both necessary and potentially time consuming. This paper extends the basic implicit coordination approach to achieve better performance with imperfect data synchronization. The resulting robust decentralized task assignment method assumes some degree of data synchronization, but adds a second planning step based on sharing the planning data. The approach is analogous to closing a synchronization loop on the planning process to reduce the sensitivity to exogenous disturbances. Further simulations are presented to show the advantages of this method in reducing the conflicts in the assignments, resulting in improved performance compared to implicit coordination. These results also clearly demonstrate the effect of communication at the different stages of the planning algorithm on the overall mission performance.

An unbiased Kalman consensus algorithm

This paper investigates the consensus problem for a team of agents with inconsistent information, which is a core component for many proposed distributed planning schemes. Kalman filtering approaches to the consensus problem have been proposed, and they were shown to converge for strongly connected networks. However, it is demonstrated in this paper that these previous techniques can result in biased estimates that deviate from the centralized solution, if it had been computed. A modification to the basic algorithm is presented to ensure the Kalman filter converges to an unbiased estimate. The proof of convergence for this modified distributed Kalman consensus algorithm to the unbiased estimate is then provided for both static and dynamic communication networks. These results are demonstrated in simulation using several simple examples.

Robust Decentralized Task Assignment for Cooperative UAVs

This paper investigates the problem of decentralized task assignment for a fleet of cooperative UAVs. It extends the analysis of a previously proposed algorithm to consider the performance with dierent communication network topologies. The results show that the second communication step introduced during the planning phase of the new algorithm is crucial for sparse networks because the convergence rate of the information consensus algorithms can be quite slow. Further analysis shows that the selection of the candidate plans communicated during this planning phase has a significant impact on the performance of the overall algorithm. A comparison of the performance and computation of four selection approaches clearly shows the importance of correctly accounting for the potential actions of the other UAVs, even though that tends to be more computationally expensive. A modification of the original candidate plan selection algorithm is also presented, which further improves the overall performance by increasing the robustness to inconsistencies in the information across the team.

Robust planning for coupled cooperative UAV missions

This paper presents a new formulation for the UAV task assignment problem with uncertainty in the environment. The problem is posed as a task assignment with uncertainty in the cost information, and we apply a modified robust technique that allows the operator to tune the level of robustness in the optimization. This formulation is then used to solve the assignment problem for a heterogeneous fleet of vehicles operating in an uncertain environment. The key aspect of this formulation is that it directly addresses the inherent coupling in deciding how to assign vehicles to perform reconnaissance tasks that provide the most benefit to the strike part of the missions. We demonstrate that the robust solution to this coupled problem can be solved as single mixed-integer linear problem. The paper presents and discusses simulations for the proposed formulation, demonstrating significant improvements over previous ones.

Filter-Embedded UAV Task Assignment Algorithms for Dynamic Environments

This paper presents a modified formulation of the classical task assignment problem that has recently been used to coordinate teams of UAVs. The main contribution is a version of the task assignment problem that can be used to tailor the control system to mitigate the eect of noise in the situational awareness on the solution. The net eect will be to limit the rate of change in the reassignment in a well-defined manner. The approach here is to perform reassignment at the rate that information is updated, which enables immediate reaction to any significant changes in the environment. We demonstrate that the modified formulation can be interpreted as a noise rejection algorithm that can be tuned to reduce the eect of variation in the uncertain parameters in the problem. Simulations are presented to demonstrate the eectiveness of this algorithm.

Cooperative task assignment of unmanned aerial vehicles in adversarial environments

This paper addresses the problem of risk in the environment and presents a new stochastic formulation of the UAV task assignment problem. This formulation explicitly accounts for the interaction between the UAVs /sub i/splaying cooperation between the vehicles rather than just coordination. As defined in the paper, cooperation entails coordinated task assignment with the additional knowledge of the future implications of a UAVs actions on improving the expected performance of the other UAVs. The key point is that the actions of each UAV can reduce the risk in the environment for all other UAVs; and the new formulation takes advantage of this fact to generate cooperative assignments that achieve better performance. This change in the formulation is accomplished by coupling the failure probabilities for each UAV to the selected missions for all other UAVs. This results in coordinated plans that optimally exploit the coupling effects of cooperation to improve the survival probabilities and expected performance. This allocation is shown to recover real-world air operations planning strategies that provide significant improvements over approaches that do not correctly account for UAV attrition. The problem is formulated as a dynamic programming (DP) problem, which is shown to be more computationally tractable than previous MILP solution approaches. Two DP approximation methods (the one-step and two-step look-ahead) are also developed for larger problems. Simulation results show that the one-step look-ahead can generate cooperative solutions very quickly, but the performance degrades considerably. The two-step look-ahead policy generates plans that are very close to (and in many cases, identical to) the optimal solution and the computation time is still significantly lower than the exact DP approach.

A Robust Approach to the UAV Task Assignment Problem

This paper presents a new formulation for the UAV task assignment problem for uncertain dynamic environments. Uncertainty in this time-varying information directly implies that the optimization data, such as target cost and target-UAV distances, will be uncertain. To mitigate the impact of this uncertainty, the new algorithm combines two key approaches that have been developed to handle the changes in the data of the assignment problem. One approach is to design task assignment plans that are robust to the uncertainty in the data, which reduces the sensitivity to errors in the situational awareness (SA), but can be overly conservative for long duration plans. An alternative strategy is to replan as the SA is updated, which results in the best plan given the current information, but can lead to churning if the updates are too rapid. This paper presents an alternative strategy that combines robust planning with the techniques developed to eliminate churning. The resulting robust filter embedded task assignment (RFETA) uses both proactive and reactive techniques to handle the uncertainty in the information and is shown to improve worst-case behavior of the plans, while at the same time ensuring that limited churning behavior is exhibited by the vehicle responding to noisy measurements. Numerous simulations are shown that demonstrate the performance benefits of this new algorithm

Unbiased Kalman Consensus Algorithm

This paper investigates the consensus problem for a team of agents with inconsistent information, which is a core component for many proposed distributed planning schemes. Kalman filtering approaches to the consensus problem have been proposed, and they were shown to converge for strongly connected networks. However, it is demonstrated in this paper that these previous techniques can result in biased estimates that deviate from the centralized solution, if it had been computed. A modification to the basic algorithm is presented to ensure the Kalman filter converges to an unbiased estimate. The proof of convergence for this modified distributed Kalman consensus algorithm to the unbiased estimate is then provided for both static and dynamic communication networks. These results are demonstrated in simulation using several simple examples