Efficient model-free Q-factor approximation in value space via log-sum-exp neural networks

We propose an efficient technique for performing data-driven optimal control of discrete-time systems. In particular, we show that log-sum-exp ($lse$) neural networks, which are smooth and convex universal approximators of convex functions, can be efficiently used to approximate Q-factors arising from finite-horizon optimal control problems with continuous state space. The key advantage of these networks over classical approximation techniques is that they are convex and hence readily amenable to efficient optimization

A Variation on a Random Coordinate Minimization Method for Constrained Polynomial Optimization

In this paper, an algorithm is proposed for solving constrained and unconstrained polynomial minimization problems. The algorithm is a variation on random coordinate descent, in which transverse steps are seldom taken. Differently from other methods available in the literature, the proposed technique is guaranteed to converge in probability to the global solution of the minimization problem, even when the objective polynomial is nonconvex. The theoretical results are corroborated by a complexity analysis and by numerical tests that validate its efficiency

Collision-avoiding decentralized control for vehicle platoons: a mechanical perspective

A new bidirectional decentralized control algorithm for vehicle platoons is proposed, which guarantees absence of collisions between the vehicles. The algorithm exploits an elegant parallel between vehicles platoon and chains of interconnected mass-spring-damper systems and the idea of barrier certificates. Stability and robustness properties of the algorithm are examined. The results are illustrated by numerical examples, simulating different driving scenarios

Online supervised global path planning for AMRs with human-obstacle avoidance

In smart factories, the performance of the production lines is improved thanks to the wide application of mobile robots. In workspaces where human operators and mobile robots coexist, safety is a fundamental factor to be considered. In this context, the motion planning of Autonomous Mobile Robots is a challenging task, since it must take into account the human factor. In this paper, an implementation of a three-level online path planning is proposed, in which a set of waypoints belonging to a safe path is computed by a supervisory planner. Depending on the nature of the detected obstacles during the robot motion, the re-computation of the safe path may be enabled, after the collision avoidance action provided by the local planner is initiated. Particular attention is devoted to the detection and avoidance of human operators. The supervisory planner is triggered as the detected human gets sufficiently close to the mobile robot, allowing it to follow a new safe virtual path while conservatively circumnavigating the operator. The proposed algorithm has been experimentally validated in a laboratory environment emulating industrial scenarios

L2-Gain for hybrid linear systems with periodic jumps: A game theoretic approach for analysis and design

In this paper, the disturbance attenuation problem is formulated and solved for a class of linear hybrid systems in the presence of periodic jumps. The results are achieved, both in the finite and infinite horizon cases, by borrowing ideas from the theory of dynamic games. In the considered formulation, independent disturbances affecting the continuous-time and the discrete-time components of the hybrid system are allowed. Moreover, the analysis is carried out by introducing easily verifiable conditions, involving the definition of a Monodromy Riccati Equation , i.e., a classical Riccati equation defined for the one-period discrete-time equivalent model. Interestingly, as a by-product, the main statements essentially characterize the solution of zero-sum noncooperative dynamic games for periodic linear hybrid systems, which is of interest per se

On f-invariant and attractive affine varieties for continuous-time polynomial systems: The case of robot motion planning

The main objective of this paper is to describe a class of polynomial vector fields f, whose associated dynamic system has one or more affine varieties as f-invariant and attractive sets. This result can be used for robot motion planning, thus computing robot paths, avoiding collisions with obstacles and reaching a target point

On polynomial vector fields having a given affine variety as attractive and invariant set: Application to robotics

The main goal of this paper is to compute a class of polynomial vector fields, whose associated dynamical system has a given affine variety as attractive and invariant set, a given point in such an affine variety as invariant and attractive and another given affine variety as invariant set, solving the application of this technique in the robotic area. This objective is reached by using some tools taken from algebraic geometry. Practical examples of how these vector fields can be computed are reported. Moreover, by using these techniques, two feedback control laws, respectively, for a unicycle-like mobile robot and for a car-like mobile robot, which make them move, within the workspace, approaching to a selected algebraic curve, are given