This paper introduces the Manipulation Task Model (MTM), an algebraic approach to describe manipulation tasks for robotic systems. The model is based on dual quaternion algebra to describe poses, linear and angular velocities, and forces and moments, all of which are joined to form the algebraic group of actions. We then use this group together with process algebra to describe temporal and logical relations between actions composing manipulation tasks. A simulation example of a cleaner robot shows that the proposed model is comprehensive and expressive enough, and that it provides robustness in relation to perturbations at the task level. Future developments will include merging the model with underlying controllers in order to provide a unified means for description, planning, and execution of manipulation tasks for robotic systems.

This paper addresses the problem of coordinating the motion of multiple fixed-wing Unmanned Aerial Vehicles (UAVs) following closed intersecting curves. We require that each UAV avoid collisions with its teammates without changing its predefined, periodic path. Also, each robot must keep a minimum speed to avoid stall and a maximum speed determined by its physical constraints. The centralized solution presented in this paper is modeled as a Mixed Integer Linear Programming (MILP) problem. The solution to this problem, which maximizes safeness (in the sense of collision avoidance), determines, for each UAV, the start time and the velocity profile over the curve.

Different robotic tasks can be solved by controlling a robot to circulate along curves. These include, for example, border inspection and surveillance, multirobot manipulation, and pattern generation. In a previous, work we have proposed a vector field approach for robot convergence and circulation along time-varying curves embedded in N-dimensional spaces. In the present work we instantiate this approach for three-dimensional spaces and, for the first time, show the efficacy of this method to control actual robots. Besides new theoretical analysis when constant speed control is applied, we present experimental results with aerial (quadrotors) and ground (differential-driven) robot.

This paper presents a methodology for computation of artificial vector fields that allows a robot to converge to and circulate around generic curves specified in$n$-dimensional spaces. These vector fields may be directly applied to solve several robot-navigation problems such as border monitoring, surveillance, target tracking, and multirobot pattern generation, with special application to fixed-wing aerial robots, which must keep a positive forward velocity and cannot converge to a single point. Unlike previous solutions found in the literature, the approach is based on fully continuous vector fields and is generalized to time-varying curves defined in$n$-dimensional spaces. We provide mathematical proofs and present simulation and experimental results that illustrate the applicability of the proposed approach. We also present a methodology for construction of the target curve based on a given set of its samples.

Uncertainties are commonly present in optimization systems, and when they are considered in the design stage, the problem usually is called a robust optimization problem. Robust optimization problems can be treated as noisy optimization problems, as worst case minimization problems, or by considering the mean and standard deviation values of the objective and constraint functions. The worst case scenario is preferred when the effects of the uncertainties on the nominal solution are critical to the application under consideration. Based on this worst case scenario, we developed the [I]RMOEA (Interval Robust Multi-Objective Evolutionary Algorithm), a hybrid method that combines interval analysis techniques to deal with the uncertainties in a deterministic way and a multi-objective evolutionary algorithm. We introduce [I]RMOEA and illustrate it on three robust test functions based on the ZDT problems. The results show that [I]RMOEA is an adequate way of tackling robust optimization problems with evolutionary techniques taking advantage of the interval analysis framework.