Han-Lim Choi

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.

Continuous trajectory planning of mobile sensors for informative forecasting

This paper addresses the planning of continuous paths for mobile sensors to reduce the uncertainty in some quantities of interest in the future. The mutual information between the measurement along the continuous path and the future verification variables defines the information reward. Two expressions for computing this mutual information are presented: the filter form extended from the state of the art and the smoother form inspired by the conditional independence structure. The key properties of the approach using the filter and smoother strategies are presented and compared. The smoother form is shown to be preferable because it provides better computational efficiency, facilitates easy integration with existing path synthesis tools, and, most importantly, enables correct quantification of the rate of information accumulation. A spatial interpolation technique is used to relate the motion of the sensor to the evolution of the measurement matrix, which leads to the formulation of the optimal path planning problem. A gradient-ascent steering law based on the concept of information potential field is also presented as a computationally efficient suboptimal strategy. A simplified weather forecasting example is used to compare several planning methodologies and to illustrate the potential performance benefits of using the proposed planning approach.

Consensus-Based Auction Approaches for Decentralized Task Assignment

This paper addresses task assignment in the coordination of a fleet of unmanned vehicles by presenting two decentralized algorithms: consensus-based auction algorithm (CBAA) and its generalization to the multi-assignment problem, consensus-based bundle algorithm (CBBA). These algorithms utilize a market-based decision strategy as the mechanism for decentralized task selection, and use a consensus routine based on local communication as the conflict resolution mechanism by achieving agreement on the winning bid values. The conflict resolution process of CBBA is further enhanced to address the dependency of the score value on the previously selected tasks in the multi-assignment setting. This work shows that the proposed algorithms, under reasonable assumptions on the scoring scheme and network connectivity, guarantee convergence to a conflict-free assignment. Also, the converged solutions are shown to guarantee 50% optimality in the worst-case and to exhibit provably good performance on average. Moreover, the proposed algorithms produce a feasible assignment even in the presence of inconsistency in situational awareness across the fleet, and even when the score functions varies with time in some standard manner. Numerical experiments verify quick convergence and good performance of the presented methods for both static and dynamic assignment problems.

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.

Decentralized task allocation with coupled constraints in complex missions

This paper presents a decentralized algorithm that creates feasible assignments for a network of autonomous agents in the presence of coupled constraints. The coupled constraints address complex mission characteristics that include assignment relationships, where the value of a task is conditioned on whether or not another task has been assigned, and temporal relationships, where the value of a task is conditioned on when it is performed relative to other tasks. The new algorithm is developed as an extension to the Consensus-Based Bundle Algorithm (CBBA), introducing the notion of pessimistic or optimistic bidding strategies and the relative timing constraints between tasks. This extension, called Coupled-Constraint CBBA (CCBBA), is compared to the baseline in a complex mission simulation and is found to outperform the baseline, particularly for task-rich scenarios.

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

An intelligent Cooperative Control Architecture

This paper presents an extension of existing cooperative control algorithms that have been developed for multi-UAV applications to utilize real-time observations and/or performance metric(s) in conjunction with learning methods to generate a more intelligent planner response. We approach this issue from a cooperative control perspective and embed elements of feedback control and active learning, resulting in an new intelligent Cooperative Control Architecture (iCCA). We describe this architecture, discuss some of the issues that must be addressed, and present illustrative examples of cooperative control problems where iCCA can be applied effectively.

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.

Efficient Targeting of Sensor Networks for Large-Scale Systems

This paper proposes an efficient approach to an observation targeting problem that is complicated by a combinatorial number of targeting choices and the large dimension of the system state, when the goal is to minimize the uncertainty in some quantities of interest. The primary improvements in the efficiency are obtained by computing the impact of each possible measurement choice on the uncertainty reduction backwards. This backward method provides an equivalent solution to a traditional forward approach under some standard assumptions, while removing the requirement of calculating a combinatorial number of covariance updates. A key contribution of this paper is to prove that the backward approach operates never slower than the forward approach, and that it works significantly faster than the forward one for ensemble-based representations. The primary benefits are shown on a simplified weather problem using the Lorenz-95 model.

Ensemble-Based Adaptive Targeting of Mobile Sensor Networks

This work presents an efficient algorithm for an observation targeting problem that is complicated by the combinatorial number of targeting choices. The approach explicitly incorporates an ensemble forecast to ensure that the measurements are chosen based on their expected improvement in the forecast at a separate verification time and location. The primary improvements in the efficiency are obtained by computing the impact of each possible measurement on the uncertainty reduction over this verification site backwards. In particular, the approach determines the impact of a series of fictitious observations taken at the verification site back on the search space (and time), which provides all of the information needed to optimize the set of measurements to take and significantly reduces the number of times that the computationally expensive ensemble updates must be performed. A computation time analysis and numerical performance simulations using the two-dimensional Lorenz-95 chaos model are presented to validate the computational advantage of the proposed algorithm over conventional search strategies.