Balancing composite motion optimization Abstract   Metaheuristic algorithms play an important role in the optimization field thanks to their robustness and programming simplicity. Many metaheuristic methods have been devised in recent years. Inspired by nature, they usually simulate natural or humanspecific phenomena in a better way. A large amount of them are based on complicated behaviors requiring several implementation steps and algorithmspecific control parameters, which impedes users and limits solutions to different types of optimization problems. Hence, designing effective simple and parameterfree optimization methods attracts much attention. In this paper, we propose a novel populationbased optimization algorithm based on balancing composite motions (BCMO). The core idea is balancing composite motion properties of individuals in solution space. Equalizing global and local searches via a probabilistic selection model creates a movement mechanism of each individual. Four test suites selected in the literature, which vary from numerical benchmarks to practical problems, to demonstrate the performance of BCMO include: (1) 23 classical benchmark functions, (2) CEC 2005 benchmark functions, (3) CEC 2014 benchmark functions, and (4) 3 real engineering design problems. The statistical results reveal the promising performance and application of BCMO in a variety of optimization and practical problems with constrained and unknown search spaces.

Improved Optimization of Motion Primitives for Motion Planning in State Lattices In this paper, we propose a framework for generating motion primitives for latticebased motion planners automatically. Given a family of systems, the user only needs to specify which principle types of motions, which are here denoted maneuvers, that are relevant for the considered system family. Based on the selected maneuver types and a selected system instance, the algorithm not only automatically optimizes the motions connecting predefined boundary conditions, but also simultaneously optimizes the endpoint boundary conditions as well. This significantly reduces the time consuming part of manually specifying all boundary value problems that should be solved, and no exhaustive search to generate feasible motions is required. In addition to handling static a priori known system parameters, the framework also allows for fast automatic reoptimization of motion primitives if the system parameters change while the system is in use, e.g, if the load significantly changes or a trailer with a new geometry is picked up by an autonomous truck. We also show in several numerical examples that the framework can enhance the performance of the motion planner in terms of total cost for the produced solution.