When robotic manipulators perform high-level tasks in the presence of another agent, e.g., a human, they must have a strategy that considers possible interferences in order to guarantee task completion and efficient r...
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When robotic manipulators perform high-level tasks in the presence of another agent, e.g., a human, they must have a strategy that considers possible interferences in order to guarantee task completion and efficient resource usage. One approach to generate such strategies is called reactive synthesis. Reactive synthesis requires an abstraction, which is a discrete structure that captures the domain in which the robot and other agents operate. Existing works discuss the construction of abstractions for mobile robots through space decomposition;however, they cannot be applied to manipulation domains due to the curse of dimensionality caused by the manipulator and the objects. In this work, we present the first algorithm for automatic abstraction construction for reactive synthesis of manipulation tasks. We focus on tasks that involve picking and placing objects with possible extensions to other types of actions. The abstraction also provides an upper bound on path-based costs for robot actions. We combine this abstraction algorithm with our reactive synthesis planner to construct correct-by-construction plans. We demonstrate the power of the framework on aUR5robot, completing complex tasks in face of interferences by a human.
In this letter, we propose methods to perform swarm behavior analysis with a novel swarm signal temporal logic (SwarmSTL). We define generalized moments to describe swarm features and propose a logical proposition to ...
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In this letter, we propose methods to perform swarm behavior analysis with a novel swarm signal temporal logic (SwarmSTL). We define generalized moments to describe swarm features and propose a logical proposition to represent an event, where the Boolean value of the logical proposition at a certain time is known a priori. We develop methods for SwarmSTI, monitoring and inference. As the swarm size can be large, we also propose methods to perform the above tasks by sampling. The methods are applied to three case studies that aim to monitor swarm "maneuver" behavior, infer a SwarmSTE, formula to describe the cause of split, and explore a SwarmSTL formula to describe "mixed-species foraging flock" of birds.
Smooth position and orientation interpolation has a great effect on the performance of robot manipulators. Interpolation between several via positions can be done in a straightforward manner, which is well covered in ...
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Smooth position and orientation interpolation has a great effect on the performance of robot manipulators. Interpolation between several via positions can be done in a straightforward manner, which is well covered in the literature. However, generating a suitable trajectory between several orientations is still an open problem. In this letter, we introduce a novel trajectory generator capable of respecting kinematic limits. We address the problem of generating a singularity-free trajectory for multiple via poses in SE(3), while complying with the requirement of C-4 continuity. To achieve this, a smooth trapezoidal-like velocity profile and unit quaternions are used. A simulation platform in V-REP based on a 7-DOF lightweight robot, including inverse kinematics and dynamics is used to demonstrate the effectiveness of our trajectory generator.
Driving styles play a major role in the acceptance and use of autonomous vehicles. Yet, existing motion planning techniques can often only incorporate simple driving styles that are modeled by the developers of the pl...
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ISBN:
(纸本)9781728190778
Driving styles play a major role in the acceptance and use of autonomous vehicles. Yet, existing motion planning techniques can often only incorporate simple driving styles that are modeled by the developers of the planner and not tailored to the passenger. We present a new approach to encode human driving styles through the use of signal temporal logic and its robustness metrics. Specifically, we use a penalty structure that can be used in many motion planning frameworks, and calibrate its parameters to model different automated driving styles. We combine this penalty structure with a set of signal temporal logic formula, based on the Responsibility-Sensitive Safety model, to generate trajectories that we expected to correlate with three different driving styles: aggressive, neutral, and defensive. An online study showed that people perceived different parameterizations of the motion planner as unique driving styles, and that most people tend to prefer a more defensive automated driving style, which correlated to their self-reported driving style.
Industrial robots are widely used in industrial production as mechanical devices. It is essential to guarantee that their control software operates safely and properly, as any functional or security-related defects ma...
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ISBN:
(纸本)9798400704208
Industrial robots are widely used in industrial production as mechanical devices. It is essential to guarantee that their control software operates safely and properly, as any functional or security-related defects may lead to serious incidents. However, industrial robots are programmed mostly in proprietary languages varying from vendor to vendor, making it challenging to formally analyze their correctness in a unified way. One of the most representative robot programming languages is the RAPID language proposed by ABB robotics. In this paper, we present K-RAPID, a formal executable semantics of RAPID in the K-Framework (K). K-RAPID is developed according to the official ABB documentation and defined in a generic extensible manner. It can be used either for validating the correctness of compiler implementation or analyzing the control programs written in RAPID. We evaluate the correctness of K-RAPID by executing 563 test programs collected from multiple sources and comparing the results against the official robot simulation environment RobotStudio. The results suggest that K-RAPID covers the core features of RAPID correctly. Moreover, we show how we could apply K-RAPID to verify RAPID programs using LTL model checking and to provide a formal specification of RAPID to uncover inappropriate behaviors in the programs.
As robotic applications continue to expand and task complexity increases, the adoption of more advanced and sophisticated control algorithms and models becomes critical. Traditional methods, relying on manual abstract...
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As robotic applications continue to expand and task complexity increases, the adoption of more advanced and sophisticated control algorithms and models becomes critical. Traditional methods, relying on manual abstraction and modeling to verify these algorithms and models, may not fully encompass all potential design paths, leading to incomplete models, design defects, and increased vulnerability to security risks. The verification of control systems using formalmethods is crucial for ensuring the safety of robots. This paper introduces a formal verification framework for robot kinematics implemented in Coq. It constructs a formal proof for the theory of robot motion and control algorithms, specifically focusing on the theory of robot kinematics, which includes the homogeneous representation of robot coordinates and the transformation relations between different coordinate systems. Subsequently, we provide formal definitions and verification for several commonly used structural robots, along with their coordinate transformation algorithms. Finally, we extract the Coq code, convert the functional algorithms into OCaml code, and perform data validation using various examples. It is worth emphasizing that the framework we have built possesses a high level of reusability, providing a solid technological foundation for the development of kinematics theorem libraries.
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