First-Order Short-Range Mover Prediction Model (SRMPM)

Abstract

An important problem for intelligent autonomous mobile systems/agents is the ability to predict the motions of other objects/agents. This has natural extensions to cooperative behavior control, where mobile agents avoid each other by predicting the other's motion. In this paper, we have formulated a spatial probability distribution for moving objects with respect to First-Order predictions, which take into account mobility characteristics and how they relate to probable motion. This is a novel method since the most common approach uses Kalman Filters to estimate future states based upon observed previous states only, assuming a geospatial 2-D Gaussian distribution with monolithic variances in both the normal and tangential directions of motion. Unlike prior approaches, our methodology takes into consideration specific dynamic constraints (e.g., Ackermann Steering), and probable decision making capabilities of the mover. By adding higher levels of fidelity to prediction models, more accurate and precise object tracking, avoidance, or engagement can be accomplished with already developed techniques.

Belief convergence to facilitate cooperative behaviors

“…We use our Autonomous Cooperative Command and Control Environment (AC 3 E) architecture for cooperative planning [1], [2]. Figure 2 is an example of a basic AC 3 E construct for collectives of autonomous and semiautonomous systems to work together.…”

“…The study in this paper is a continuation of previous studies performed to facilitate these sought cooperative behaviors [1], [2]. State observers, such as Kalman filters, can be used by agents to estimate the belief states of their neighbors, which are a set of values that define how an agent perceives the effort required by an agent (itself or a neighbor) to accomplish a task.…”