Computational load reduction of the agent guidance problem using Mixed Integer Programming

Abstract

This paper employs a solution to the agent-guidance problem in an environment with obstacles, whose avoidance techniques have been extensively used in the last years. There is still a gap between the solution times required to obtain a trajectory and those demanded by real world applications. These usually face a tradeoff between the limited on-board processing performance and the high volume of computing operations demanded by those real-time applications. In this paper we propose a deferred decision-based technique that produces clusters used for obstacle avoidance as the agent moves in the environment, like a driver that, at night, enlightens the road ahead as her/his car moves along a highway. By considering the spatial and temporal relevance of each obstacle throughout the planning process and pruning areas that belong to the constrained domain, one may relieve the inherent computational burden of avoidance. This strategy reduces the number of operations required and increases it on demand, since a computationally heavier problem is tackled only if the simpler ones are not feasible. It consists in an improvement based solely on problem modeling, which, by example, may offer processing times in the same order of magnitude than the lower-bound given by the relaxed form of the problem.

Trajectory planning with a dynamic obstacle clustering strategy using Mixed-Integer Linear Programming

“…As the number of clusters available is limited, the clustering algorithm adjusts the cluster configuration according to the position of the agent to allow a minimum-cost trajectory to the target, which causes the decrease in the value of the cost function regarding what was predicted in the preceding step. This illustrates the advantage of our proposal regarding methods that rely on pre-processing the environment, such as the alternatives found in the literature [4], [5], [6]. The embedding of the clustering strategy within the optimization problem enables to decide the clusters considering the minimization of the cost.…”

“…More recently, several techniques using both preprocessing of the environment as well as taking advantage of the current available solution of the MPC problem were proposed in [6]. Regarding pre-processing, the proposal involved clustering the obstacles according to their distances to the agent and among themselves, replacing several obstacles and the binary variables associated to them with fewer clusters and their corresponding binary variables.…”