In this letter we present a grammar and control synthesis framework for online modification of Event-based Signal Temporal Logic (STL) specifications, during execution. These modifications allow a user to change the r...
详细信息
In this letter we present a grammar and control synthesis framework for online modification of Event-based Signal Temporal Logic (STL) specifications, during execution. These modifications allow a user to change the robots' task in response to potential future violations, changes to the environment, or user-defined task changes. In cases where a modification is not possible, we provide feedback to the user and suggest alternative modifications. We demonstrate our task modification process using a Hello Robot Stretch.
For the safe operation of robotic systems, it is important to accurately understand its failure modes using prior testing. Hardware testing of robotic infrastructure is known to be slow and costly. Instead, failure pr...
详细信息
For the safe operation of robotic systems, it is important to accurately understand its failure modes using prior testing. Hardware testing of robotic infrastructure is known to be slow and costly. Instead, failure prediction in simulation can help to analyze the system before deployment. Conventionally, large-scale na & iuml;ve Monte Carlo simulations are used for testing;however, this method is only suitable for testing average system performance. For safety-critical systems, worst-case performance is more crucial as failures are often rare events, and the size of test batches increases substantially as failures become rarer. Rare-event sampling methods can be helpful;however, they exhibit slow convergence and cannot handle constraints. This research introduces a novel sampling-based testing framework for autonomous systems which bridges these gaps by utilizing a discretized gradient-based second-order Langevin algorithm combined with learning-based techniques for constrained sampling of failure modes. Our method can predict more diverse failures by exploring the search space efficiently and ensures feasibility with respect to temporal and implicit constraints. We demonstrate the use of our testing methodology on two categories of testing problems, via simulations and hardware experiments. Our method discovers up to 2X failures compared to na & iuml;ve Random Walk sampling, with only half of the sample size.
Understanding the geometry of collision-free configuration space (C-free) in the presence of Cartesian-space obstacles is an essential ingredient for collision-free motion planning. While it is possible to check for c...
详细信息
Understanding the geometry of collision-free configuration space (C-free) in the presence of Cartesian-space obstacles is an essential ingredient for collision-free motion planning. While it is possible to check for collisions at a point using standard algorithms, to date no practical method exists for computing C-free regions with rigorous certificates due to the complexity of mapping Cartesian-space obstacles through the kinematics. In this work, we present the first to our knowledge rigorous method for approximately decomposing a rational parametrization of C-free into certified polyhedral regions. Our method, called C-Iris (C-space Iterative Regional Inflation by Semidefinite programming), generates large, convex polytopes in a rational parameterization of the configuration space which are rigorously certified to be collision-free. Such regions have been shown to be useful for both optimization-based and randomized motion planning. Based on convex optimization, our method works in arbitrary dimensions, only makes assumptions about the convexity of the obstacles in the 3D Cartesian space, and is fast enough to scale to realistic problems in manipulation. We demonstrate our algorithm's ability to fill a non-trivial amount of collision-free C-space in several 2-DOF examples where the C-space can be visualized, as well as the scalability of our algorithm on a 7-DOF KUKA iiwa, a 6-DOF UR3e, and 12-DOF bimanual manipulators. An implementation of our algorithm is open-sourced in Drake. We furthermore provide examples of our algorithm in interactive Python notebooks.
Anticipating the maintenance needs of lightweight robotic manipulators at precise future instances represents a significant challenge within the automation domain. This letter introduces an innovative and comprehensiv...
详细信息
Anticipating the maintenance needs of lightweight robotic manipulators at precise future instances represents a significant challenge within the automation domain. This letter introduces an innovative and comprehensive method to estimate the severity of stress imposed on a robot joint at any given time. Additionally, we present a knowledge-based predictive model aimed at approximating the End of Life (EoL) for a robotic joint, enabling the prediction of its Remaining Useful Life (RUL) with respect to the designated load case. This predictive model is rooted in a baseline derived from empirical data covering the entire Universal Robots (UR) e-series and is trained using synthetic data. Subsequently, it undergoes evaluation with a real-world dataset and is further validated in a case study. The model demonstrates a high level of accuracy, with worst-case performance reaching 90,3 % as the lower limit.
This letter introduces a sampling-based strategy synthesis algorithm for nondeterministic hybrid systems with complex continuous dynamics under temporal and reachability constraints. We model the evolution of the hybr...
详细信息
This letter introduces a sampling-based strategy synthesis algorithm for nondeterministic hybrid systems with complex continuous dynamics under temporal and reachability constraints. We model the evolution of the hybrid system as a two-player game, where the nondeterminism is an adversarial player whose objective is to prevent achieving temporal and reachability goals. The aim is to synthesize a winning strategy - a reactive (robust) strategy that guarantees the satisfaction of the goals under all possible moves of the adversarial player. Our proposed approach involves growing a (search) game-tree in the hybrid space by combining sampling-based motion planning with a novel bandit-based technique to select and improve on partial strategies. We show that the algorithm is probabilistically complete, i.e., the algorithm will asymptotically almost surely find a winning strategy, if one exists. The case studies and benchmark results show that our algorithm is general and effective, and consistently outperforms state of the art algorithms.
Deep Reinforcement Learning (DRL) has achieved impressive performance in robotics and autonomous systems (RAS). A key challenge to its deployment in real-life operations is the presence of spuriously unsafe DRL polici...
详细信息
Deep Reinforcement Learning (DRL) has achieved impressive performance in robotics and autonomous systems (RAS). A key challenge to its deployment in real-life operations is the presence of spuriously unsafe DRL policies. Unexplored states may lead the agent to make wrong decisions that could result in hazards, especially in applications where DRL-trained end-to-end controllers govern the behaviour of RAS. This letter proposes a novel quantitative reliability assessment framework for DRL-controlled RAS, leveraging verification evidence generated from formal reliability analysis of neural networks. A two-level verification framework is introduced to check the safety property with respect to inaccurate observations that are due to, e.g., environmental noise and state changes. Reachability verification tools are leveraged locally to generate safety evidence of trajectories. In contrast, at the global level, we quantify the overall reliability as an aggregated metric of local safety evidence, corresponding to a set of distinct tasks and their occurrence probabilities. The effectiveness of the proposed verification framework is demonstrated and validated via experiments on real RAS.
Impedance control is essential in robotic manipulation tasks involving unpredictable interactions with the environment, as it ensures safety and stability during contact. While the concept of translational impedance i...
详细信息
Impedance control is essential in robotic manipulation tasks involving unpredictable interactions with the environment, as it ensures safety and stability during contact. While the concept of translational impedance is consistently understood, rotational impedance exhibits varied forms in scientific literature. This letter seeks to elucidate the fundamental nature of rotational impedance and presents a comprehensive, unified framework for formulating rotational impedance using Lie algebra and Noether's theorem. This approach facilitates the derivation of the various expressions of rotational impedance observed in the field. We utilized quaternions and rotation matrices to represent rotational motion within our proposed framework to ensure theoretical validity. This approach yielded a rotational impedance expression that aligns with existing literature. For empirical verification, we have conducted both simulated experiments using robots and actual trials on a UR5 robot. These assessments demonstrated the expected dynamic responses along all axes. These results showcase the efficacy and broad applicability of our approach.
We address the problem of resilient motion planning for robots operating under Signal Temporal Logic (STL) specifications in dynamic environments. In such settings, unforeseen events-such as the emergence of dynamic o...
详细信息
We address the problem of resilient motion planning for robots operating under Signal Temporal Logic (STL) specifications in dynamic environments. In such settings, unforeseen events-such as the emergence of dynamic obstacles-can render the original STL specification infeasible. To address this, we propose a reactive framework that enables local corrections or global replanning of the robot's trajectory in response to these unexpected occurrences. When the original STL specification becomes unsatisfiable, our framework involves minimally relaxing it by extending/shrinking time windows or removing some tasks within the user's allowance. This strategy is designed to prevent arbitrarily long delays in mission completion and to facilitate the satisfaction of the mission with minimal temporal relaxation (TR). We present theoretical results supporting our framework and demonstrate its effectiveness through high-fidelity simulations.
In this letter, a new kind of adaptive controller for the problem of output feedback tracking is proposed on the basis of the Active Disturbance Rejection Control (ADRC) paradigm. The controller is synthesized for the...
详细信息
In this letter, a new kind of adaptive controller for the problem of output feedback tracking is proposed on the basis of the Active Disturbance Rejection Control (ADRC) paradigm. The controller is synthesized for the systems linear in parameters by combining the classic ADRC algorithm with a recent Parameter Identifying Extended State Observer (PIESO) which employs a gradient adaptation law to actively identify the parameters of the plant. By means of the Lyapunov analysis, the asymptotic convergence of tracking, estimation, and identification errors is proved in the nominal case and the stability conditions of the closed-loop system are formulated. The theoretical analysis is complemented by simulation and experimental results of the proposed controller.
This study proposes a hierarchically integrated framework for safe task and motion planning (TAMP) of bipedal locomotion in a partially observable environment with dynamic obstacles and uneven terrain. The high-level ...
详细信息
This study proposes a hierarchically integrated framework for safe task and motion planning (TAMP) of bipedal locomotion in a partially observable environment with dynamic obstacles and uneven terrain. The high-level task planner employs linear temporal logic (LTL) for a reactive game synthesis between the robot and its environment and provides a formal guarantee on navigation safety and task completion. To address environmental partial observability, a belief abstraction is employed at the high-level navigation planner to estimate the dynamic obstacles' location. Accordingly, a synthesized action planner sends a set of locomotion actions to the middle-level motion planner, while incorporating safe locomotion specifications extracted from safety theorems based on a reduced-order model (ROM) of the locomotion process. The motion planner employs the ROM to design safety criteria and a sampling algorithm to generate non-periodic motion plans that accurately track high-level actions. To address external perturbations, this study also investigates safe sequential composition of the keyframe locomotion state and achieves robust transitions against external perturbations through reachability analysis. A set of ROM-based hyperparameters are finally interpolated to design whole-body locomotion gaits generated by trajectory optimization and validate the viable deployment of the ROM-based TAMP on a 20-degrees-of-freedom Cassie robot designed by Agility robotics.
暂无评论