Sameera S. Ponda

Trajectory optimization for target localization using small unmanned aerial vehicles

Small unmanned aerial vehicles (UAVs) equipped with navigation and video capabilities can be used to perform target localization. Combining UAV state estimates with image data leads to bearing measurements of the target that can be processed to determine its position. This 3-D bearings-only estimation problem is nonlinear and traditional ltering methods are prone to biases, noisy estimates, and lter instabilities. The performance of the target localization is highly dependent on the vehicle trajectory, motivating the development of optimal UAV trajectories. This work presents methods for designing trajectories that increase the amount of information provided by the measurements and shows that these trajectories lead to enhanced estimation performance. The Fisher Information Matrix (FIM) is used to quantify the information provided by the measurements. Several objective functions based on the FIM are considered and the A-optimality criterion is shown to be the best suited for trajectory optimization in the 3-D bearings-only target localization problem. The resulting trajectories produce spirals, which increase the angular separation between measurements and reduce the range to the target, supporting geometric intuition. The problem of simultaneous target estimation and vehicle trajectory optimization is explored and the resulting algorithms produce vehicle trajectories that increase the information provided by the measurements, enhancing the target estimation performance by increasing accuracy, reducing uncertainty and improving lter convergence.

Decentralized planning for complex missions with dynamic communication constraints

This paper extends the consensus-based bundle algorithm (CBBA), a distributed task allocation framework previously developed by the authors, to address complex missions for a team of heterogeneous agents in a dynamic environment. The extended algorithm proposes appropriate handling of time windows of validity for tasks, fuel costs of the vehicles, and heterogeneity in the agent capabilities, while preserving the robust convergence properties of the original algorithm. An architecture to facilitate real-time task replanning in a dynamic environment is also presented, along with methods to handle varying communication constraints and dynamic network topologies. Simulation results and experimental flight tests in an indoor test environment verify the proposed task planning methodology for complex missions.

Distributed Planning Strategies to Ensure Network Connectivity for Dynamic Heterogeneous Teams

This paper presents a cooperative distributed planning algorithm that ensures network connectivity for a team of heterogeneous agents operating in dynamic and communication-limited environments. The algorithm, named CBBA with Relays, builds on the Consensus-Based Bundle Algorithm (CBBA), a distributed task allocation framework developed previously by the authors and their colleagues. Information available through existing consensus phases of CBBA is leveraged to predict the network topology and to propose relay tasks to repair connectivity violations. The algorithm ensures network connectivity during task execution while preserving the distributed and polynomial-time guarantees of CBBA. By employing under-utilized agents as communication relays, CBBA with Relays improves the range of the team without limiting the scope of the active agents, thus improving mission performance. The algorithm is validated through simulation trials and through experimental indoor and outdoor field tests, demonstrating the real-time applicability of the approach.

Dynamic Mission Planning for Communication Control in Multiple Unmanned Aircraft Teams

A multi-UAV system relies on communications to operate. Failure to communicate remotely sensed mission data to the base may render the system ineffective, and the inability to exchange command and control messages can lead to system failures. This paper describes a unique method to control network communications through distributed task allocation to engage under-utilized UAVs to serve as communication relays and to ensure that the network supports mission tasks. This work builds upon a distributed algorithm previously developed by the authors, CBBA with Relays, which uses task assignment information, including task location and proposed execution time, to predict the network topology and plan support using relays. By explicitly coupling task assignment and relay creation processes, the team is able to optimize the use of agents to address the needs of dynamic complex missions. In this work, the algorithm is extended to explicitly consider realistic network communication dynamics, including path loss, stochastic fading, and information routing. Simulation and flight test results validate the proposed approach, demonstrating that the algorithm ensures both data-rate and interconnectivity bit-error-rate requirements during task execution.

Phase and amplitude imaging from noisy images by Kalman filtering.

We propose and demonstrate a computational method for complex-field imaging from many noisy intensity images with varying defocus, using an extended complex Kalman filter. The technique offers dynamic smoothing of noisy measurements and is recursive rather than iterative, so is suitable for adaptive measurements. The Kalman filter provides near-optimal results in very low-light situations and may be adapted to propagation through turbulent, scattering, or nonlinear media.

Improving the Eciency of a Decentralized Tasking Algorithm for UAV Teams with Asynchronous Communications

This work presents a decentralized task allocation algorithm for networked agents communicating through an asynchronous channel. The algorithm extends the Consensus-Based Bundle Algorithm (CBBA) to account for more realistic asynchronous communication protocols. Direct implementation of CBBA into such an asynchronous setting requires agents to frequently broadcast their information states, which would cause signicant communication overow. In contrast, the extension proposed in this paper, named Asynchronous CBBA (ACBBA), minimizes communication load while preserving the convergence properties. ACBBA applies a new set of local deconiction rules that do not require access to the global in

Asynchronous Decentralized Task Allocation for Dynamic Environments

This work builds on a decentralized task allocation algorithm for networked agents communicating through an asynchronous channel, by extending the Asynchronous ConsensusBased Bundle Algorithm (ACBBA) to account for more real time implementation issues resulting from a decentralized planner. This paper specically talks to the comparisons between global and local convergence in asynchronous consensus algorithms. Also a feature called asynchronous replan is introduced to ACBBA’s functionality that enables ecient updates to large changes in local situational awareness. A real-time software implementation using multiple agents communicating through the user datagram protocol (UDP) validates the proposed algorithm.

Distributed chance-constrained task allocation for autonomous multi-agent teams

This research presents a distributed chance-constrained task allocation framework that can be used to plan for multi-agent networked teams operating in stochastic and dynamic environments. The algorithm employs an approximation strategy to convert centralized problem formulations into distributable sub-problems that can be solved by individual agents. A key component of the distributed approximation is a risk adjustment method that allocates individual agent risks based on a global risk threshold. The results show large improvements in distributed stochastic environments by explicitly accounting for uncertainty propagation during the task allocation process.

Allowing non-submodular score functions in distributed task allocation

Submodularity is a powerful property that can be exploited for provable performance and convergence guarantees in distributed task allocation algorithms. However, some mission scenarios cannot easily be approximated as submodular a priori. This paper introduces an algorithmic extension for distributed multi-agent multi-task assignment algorithms which provides guaranteed convergence using non-submodular score functions. This algorithm utilizes non-submodular ranking of tasks within each agents internal decision making process, while externally enforcing that shared bids appear as if they were created using submodular score functions. Provided proofs demonstrate that all convergence and performance guarantees hold with respect to this apparent submodular score function. The algorithm allows significant improvements over heuristic approaches that approximate truly non-submodular score functions.

Predictive Planning for Heterogeneous Human-Robot Teams

This research was supported in part by AFOSR (FA9550-08-1-0086) and MURI (FA9550-08-1-0356).