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A common requirement in optimal control problems arising in autonomous navigation is that the decision variables are constrained to be outside certain sets. Such set exclusion constraints represent obstacles that must be avoided by the motion system. This paper presents a simple and efficient method for solving optimization problems with general set exclusion and implicit constraints. The method embeds the set exclusion constraints in a quadratic penalty framework and solves the inner optimization problems using a proximal algorithm that deals directly with the implicit constraints. We derive convergence results for this method by transforming the generated iterates to points of a reformulated problem with complementarity constraints. Furthermore, the practical application of the solution method is validated in numerical simulations of a model predictive control approach to path planning for a mobile robot. Finally, a runtime comparison with state-of-the-art solvers applied to the problem with complementarity constraints illustrates the efficiency of the proposed method. [ABSTRACT FROM AUTHOR]

Right Ventricular Pacing (RVP) may have detrimental effects in ventricular function. Left Bundle Branch Area Pacing (LBBAP) is a new pacing strategy that appears to have better results. The aim of this systematic review and meta-analysis is to compare the safety and efficacy of LBBAP vs RVP in patients with bradyarrhythmia and conduction system disorders. MEDLINE, EMBASE and Pubmed databases were searched for studies comparing LBBAP with RVP. Outcomes were all-cause mortality, atrial fibrillation (AF) occurrence, heart failure hospitalizations (HFH) and complications. QRS duration, mechanical synchrony and LVEF changes were also assessed. Pairwise meta-analysis was conducted using random and fixed effects models. Twenty-five trials with 4250 patients (2127 LBBAP) were included in the analysis. LBBAP was associated with lower risk for HFH (RR:0.33, CI 95%:0.21 to 0.50; p < 0.001), all-cause mortality (RR:0.52 CI 95%:0.34 to 0.80; p = 0.003), and AF occurrence (RR:0.43 CI 95%:0.27 to 0.68; p < 0.001) than RVP. Lead related complications were not different between the two groups (p = 0.780). QRSd was shorter in the LBBAP group at follow-up (WMD: −32.20 msec, CI 95%: −40.70 to −23.71; p < 0.001) and LBBAP achieved better intraventricular mechanical synchrony than RVP (SMD: -1.77, CI 95%: −2.45 to −1.09; p < 0.001). LBBAP had similar pacing thresholds (p = 0.860) and higher R wave amplitudes (p = 0.009) than RVP. LBBAP has better clinical outcomes, preserves ventricular electrical and mechanical synchrony and has excellent pacing parameters, with no difference in complications compared to RVP. • Systematic review and meta-analysis of twenty-five trials with 4250 patients. • LBBAP has lower risk of Heart Failure Hospitalizations compared to RVP. • LBBAP shows better results in all-cause mortality than RVP. • AF occurrence rates are lower with LBBAP. [ABSTRACT FROM AUTHOR]

We present a case study applying learning-based distributionally robust model predictive control to highway motion planning under stochastic uncertainty of the lane change behavior of surrounding road users. The dynamics of road users are modeled using Markov jump systems, in which the switching variable describes the desired lane of the vehicle under consideration and the continuous state describes the pose and velocity of the vehicles. We assume the switching probabilities of the underlying Markov chain to be unknown. As the vehicle is observed and thus, samples from the Markov chain are drawn, the transition probabilities are estimated along with an ambiguity set which accounts for misestimations of these probabilities. Correspondingly, a distributionally robust optimal control problem is formulated over a scenario tree, and solved in receding horizon. As a result, a motion planning procedure is obtained which through observation of the target vehicle gradually becomes less conservative while avoiding overconfidence in estimates obtained from small sample sizes. We present an extensive numerical case study, comparing the effects of several different design aspects on the controller performance and safety. [ABSTRACT FROM AUTHOR]