A standardized robot-testing algorithm should satisfy accuracy, efficiency, and very importantly, repeatability-consistently yielding similar outcomes across multiple executions by different stakeholders. Achieving repeatability grows challenging with increasing complexity, intelligence, diversity, and inherent stochasticity in testing methods, robotic platforms, and environments. While existing efforts address repeatability through ethical, hardware, or procedural means, this study specifically targets algorithm-level repeatability, focusing on statistical query (SQ) algorithms commonly used in standardized evaluations. We propose a lightweight, adaptive modification for any SQ-based routine, including Monte Carlo, importance sampling, and adaptive sampling, guaranteeing provable repeatability with bounded accuracy and efficiency. Effectiveness is demonstrated across three cases: standardized manipulator testing, intelligent risk assessment for automated vehicles, and performance evaluation of humanoid robot locomotion.

Risk assessment of a robot in controlled environments, such as laboratories and proving grounds, is a common means to assess, certify, validate, verify, and characterize the robots' safety performance before, during, and even after their commercialization in the real-world. A standard testing program that acquires the risk estimate is expected to be (i) repeatable, such that it obtains similar risk assessments of the same testing subject among multiple trials or attempts with the similar testing effort by different stakeholders, and (ii) reliable against a variety of testing subjects produced by different vendors and manufacturers. Both repeatability and reliability are fundamental and crucial for a testing algorithm's validity, fairness, and practical feasibility, especially for standardization. However, these properties are rarely satisfied or ensured, especially as the subject robots become more complex, uncertain, and varied. This issue was present in traditional risk assessments through Monte-Carlo sampling, and remains a bottleneck for the recent accelerated risk assessment methods, primarily those using importance sampling. This study aims to enhance existing accelerated testing frameworks by proposing a new algorithm that provably integrates repeatability and reliability with the already established formality and efficiency. It also features demonstrations assessing the risk of instability from frontal impacts, initiated by push-over disturbances on a controlled inverted pendulum and a 7-DoF planar bipedal robot Rabbit managed by various control algorithms.

A connected and automated vehicle safety metric determines the performance of a subject vehicle (SV) by analyzing the data involving the interactions among the SV and other dynamic road users and environmental features. When the data set contains only a finite set of samples collected from the naturalistic mixed multi-modal traffic driving environment, a metric is expected to generalize the safety assessment outcome from the observed finite samples to the unobserved cases by specifying in what domain the SV is expected to be safe and how safe the SV is, statistically, in that domain. However, to the best of our knowledge, none of the existing safety metrics is able to justify the above properties with an operational domain specific, guaranteed complete, and provably unbiased safety evaluation outcome. In this paper, we propose a novel safety metric that involves the$\alpha $-shape and the$\epsilon $-almost robustly forward invariant set to characterize the SV’s almost safe operable domain and the probability for the SV to remain inside the safe domain indefinitely, respectively. The empirical performance of the proposed method is demonstrated in several different operational design domains through a series of cases covering a variety of fidelity levels (real-world and simulators), driving environments (highway, urban, and intersections), road users (car, truck, and pedestrian), and SV driving behaviors (human driver and self driving algorithms).

Monocular vision based navigation for automated driving is a challenging task due to the lack of enough information to compute temporal relationships among objects on the road. Optical flow is an option to obtain temporal information from monocular camera images, and has been used widely with the purpose of identifying objects and their relative motion. This work proposes to generate an artificial potential field, i.e. visual potential field, from a sequence of images using sparse optical flow, which is used together with a gradient tracking sliding mode controller to navigate the vehicle to destination without collision with obstacles. The angular reference for the vehicle is computed online. This work considers that the vehicle does not require to have a priori information from the map or obstacles to navigate successfully. The proposed technique is tested both in synthetic and real images.

Automated driving in urban settings is challenging. Human participant behavior is difficult to model, and conventional, rule-based Automated Driving Systems (ADSs) tend to fail when they face unmodeled dynamics. On the other hand, the more recent, end-to-end Deep Reinforcement Learning (DRL) based model-free ADSs have shown promising results. However, pure learning-based approaches lack the hard-coded safety measures of model-based controllers. Here we propose a hybrid approach for integrating a path planning pipe into a vision based DRL framework to alleviate the shortcomings of both worlds. In summary, the DRL agent is trained to follow the path planner's waypoints as close as possible. The agent learns this policy by interacting with the environment. The reward function contains two major terms: the penalty of straying away from the path planner and the penalty of having a collision. The latter has precedence in the form of having a significantly greater numerical value. Experimental results show that the proposed method can plan its path and navigate between randomly chosen origin-destination points in CARLA, a dynamic urban simulation environment. Our code is open-source and available online.

The purpose of this work is to combine the concepts of sliding mode control and internal model control to design a Dynamical Sliding Mode Control (DMSC). The Internal Model Control (IMC) structure is used to obtain a simple and easy way to design the controller. This new approach eliminates the major sliding mode control problem: chattering. The process is reduced to a first-order-plus-deadtime (FOPDT) model, and it is divided in two components an invertible one and other non-invertible. A lead term is added to the invertible term and a lead-lag model is obtained and used to get the desired controller. The approach is tested in a high order system with elevated deadtime. The performance of the controller is tested under tracking and regulation conditions: change in the set point, persistent disturbance applied at a determined time, and addition of noise. Finally, a robustness test is conducted to prove how well the controller works when the plant ages. In every of these cases, the performance of the controller is satisfactory, also neither chattering nor instabilities are presented.