Amidst the swift progress in deep learning, the YOLO series has redefined the standards for real-time object detection. The incorporation of the Transformer architecture into YOLO models has significantly broadened th...
详细信息
Autonomous exploration of unknown environments using unmanned robots is a widely researched problem of our days. The focus of this paper is to provide a novel goal-selection method in response to the problems of the s...
详细信息
作者：
Merei, MohammadTar, Jozsef K.Óbuda University
Doctoral School of Applied Informatics and Applied Mathematics Budapest Hungary Óbuda University
Doctoral School of Applied Informatics and Applied Mathematics Antal Bejczy Center for Intelligent Robotics John von Neumann Faculty of Informatics Budapest Hungary
In this paper, adaptive backstepping control is investigated for coupled nonlinear springs. A dynamic model of coupled special nonlinear springs is established with uncertain parameters. The conventional adaptive appr...
详细信息
Artificial intelligence (AI) is not a fancy term anymore, or not limited to only researchers and academia. AI is currently becoming a part and parcel of our daily life, we are using AI/ intelligent systems by knowing ...
详细信息
Real-time object detection is one of the most fundamental computer vision processes, it is of great importance for many applications, such as video surveillance and autonomous driving. Object detection with multiple m...
详细信息
The advancement of modern robotic systems is inseparable from the development of artificial sensory modules and functions that provide robots with human-like perception. Among these, haptic sensation is of vital impor...
详细信息
The increasing deployment of robots in co-working scenarios with humans has revealed complex safety and efficiency challenges in the computation of the robot behavior. Movement among humans is one of the most fundamen...
详细信息
ISBN:
(纸本)9781728196817
The increasing deployment of robots in co-working scenarios with humans has revealed complex safety and efficiency challenges in the computation of the robot behavior. Movement among humans is one of the most fundamental -and yet critical-problems in this frontier. While several approaches have addressed this problem from a purely navigational point of view, the absence of a unified paradigm for communicating with humans limits their ability to prevent deadlocks and compute feasible solutions. This paper presents a joint communication and motion planning framework that selects from an arbitrary input set of robot's communication signals while computing robot motion plans. It models a human co-worker's imperfect perception of these communications using a noisy sensor model and facilitates the specification of a variety of social/workplace compliance priorities with a flexible cost function. Theoretical results and simulator-based empirical evaluations show that our approach efficiently computes motion plans and communication strategies that reduce conflicts between agents and resolve potential deadlocks.
We describe a task and motion planning architecture for highly dynamic systems that combines a domainindependent sampling-based deliberative planning algorithm with a global reactive planner. We leverage the recent de...
详细信息
ISBN:
(纸本)9781728196817
We describe a task and motion planning architecture for highly dynamic systems that combines a domainindependent sampling-based deliberative planning algorithm with a global reactive planner. We leverage the recent development of a reactive, vector field planner that provides guarantees of reachability to large regions of the environment even in the face of unknown or unforeseen obstacles. The reachability guarantees can be formalized using contracts that allow a deliberative planner to reason purely in terms of those contracts and synthesize a plan by choosing a sequence of reactive behaviors and their target configurations, without evaluating specific motion plans between targets. This reduces both the search depth at which plans will be found, and the number of samples required to ensure a plan exists, while crucially preserving correctness guarantees. The result is reduced computational cost of synthesizing plans, and increased robustness of generated plans to actuator noise, model misspecification, or unknown obstacles. Simulation studies show that our hierarchical planning and execution architecture can solve complex navigation and rearrangement tasks, even when faced with narrow passageways or incomplete world information.
Cable-driven flexible manipulators are highly flexible and adapt well to complex, unstructured environments. Existing cable-driven flexible manipulators typically employ motor-driven actuators, which result in reduced...
详细信息
In large-scale model training, distributed training frameworks offer an effective solution to the limitations of a single GPU or node in handling massive model parameters and datasets. By distributing training tasks a...
详细信息
暂无评论