Zachary N. Sunberg

Information Space Receding Horizon Control for Multisensor Tasking Problems

In this paper, we present a receding horizon solution to the problem of optimal scheduling for multiple sensors monitoring a group of dynamical targets. The term target is used here in the classic sense of being the object that is being sensed or observed by the sensors. This problem is motivated by the space situational awareness (SSA) problem. The multisensor optimal scheduling problem can be posed as a multiagent Markov decision process on the information space which has a dynamic programming (DP) solution. We present a simulation-based stochastic optimization technique that exploits the structure inherent in the problem to obtain variance reduction along with a distributed solution. This stochastic optimization technique is combined with a receding horizon approach which uses online solution of the control problems to obviate the need to solve the computationally intractable multiagent information space DP problem and hence, makes the technique computationally tractable. The technique is tested on a moderate scale SSA example which is nonetheless computationally intractable for existing solution techniques.

Optimized and trusted collision avoidance for unmanned aerial vehicles using approximate dynamic programming

Safely integrating unmanned aerial vehicles into civil airspace is contingent upon development of a trustworthy collision avoidance system. This paper proposes an approach whereby a parameterized resolution logic that is considered trusted for a given range of its parameters is adaptively tuned online. Specifically, to address the potential conservatism of the resolution logic with static parameters, we present a dynamic programming approach for adapting the parameters dynamically based on the encounter state. We compute the adaptation policy offline using a simulation-based approximate dynamic programming method that accommodates the high dimensionality of the problem. Numerical experiments show that this approach improves safety and operational performance compared to the baseline resolution logic, while retaining trustworthiness.

The value of inferring the internal state of traffic participants for autonomous freeway driving

Safe interaction with human drivers is one of the primary challenges for autonomous vehicles. In order to plan driving maneuvers effectively, the vehicles control system must infer and predict how humans will behave based on their latent internal state (e.g., intentions and aggressiveness). This research uses a simple model for human behavior with unknown parameters that make up the internal states of the traffic participants and presents a method for quantifying the value of estimating these states and planning with their uncertainty explicitly modeled. An upper performance bound is established by an omniscient Monte Carlo Tree Search (MCTS) planner that has perfect knowledge of the internal states. A baseline lower bound is established by planning with MCTS assuming that all drivers have the same internal state. MCTS variants are then used to solve a partially observable Markov decision process (POMDP) that models the internal state uncertainty to determine whether inferring the internal state offers an advantage over the baseline. Applying this method to a freeway lane changing scenario reveals that there is a significant performance gap between the upper bound and baseline. POMDP planning techniques come close to closing this gap, especially when important hidden model parameters are correlated with measurable parameters.

Information space sensor tasking for Space Situational Awareness

In this paper, we apply a receding horizon control approach to the sensor tasking aspect of a simplified version of the Space Situational Awareness (SSA) problem: “Given a small number of sensors and a large number of satellites, how should the sensors be used to maximize the information gained about the states of the satellites” Finding the globally optimal solution to this partially observed Markov decision process is computationally intractable. However, by using a stochastic gradient ascent algorithm proposed in previous work to improve an open-loop control policy over a shortened horizon, large performance improvements can be made over a baseline myopic tasking policy in a computationally tractable manner. The structure of this approach also allows for a distributed implementation in which each sensor acts as an agent that is semi-independent from the others.