Analysis of massive track datasets is a challenging problem, especially when examining n-way relations inherent in social networks. In this paper, we use the Mitsubishi track database to examine the usefulness of three types of interaction features observable in tracklet networks. We explore ways in which social network information can be extracted and visualized using a statistical sampling of these features from a very large track dataset, with very little ground truth or outside knowledge. Special attention is given to methods that are likely to scale well beyond the size of the Mitsubishi dataset.

Control based on artificial potentials results in, bounded-torque controllers. The authors extend this notion by devising energy-reference controllers that use harmonic potentials to produce collision-free kinodynamic behavior. In addition, the phase space structure of a manipulator controlled by an artificial potential "copies" the critical point structure of that potential. This result is used in conjunction with harmonic potentials to generate and control convergent and repetitive motions for a manipulator.

This paper describes a relationship between harmonic functions (used for path planning) and work on randomization and learning in robotics. In short, the hitting probabilities for random walks can be cast as a Dirichlet problem for harmonic functions, in much the same way as in path planning. This equivalence has implications both for uncertainty in motion planning and in some robot learning problems.>

A method for planning smooth robot paths is presented. The method relies on the use of Laplace's equation to constrain the generation of a potential function over regions of the configuration space of an effector. Once the function is computed, paths may be found very quickly. These functions do not exhibit the local minima which plague the potential field method. Unlike decompositional and algebraic techniques. Laplace's equation is very well suited to computation on massively parallel architectures.>