Cucumber farming plays a crucial role in Bangladesh's agricultural economy, significantly contributing to vegetable production. However, diseases like Downy Mildew, Bacterial Wilt, Anthracnose, and Belly Rot threa...
详细信息
Understanding environment dynamics is necessary for robots to act safely and optimally in the world. In realistic scenarios, dynamics are non-stationary and the causal variables such as environment parameters cannot n...
详细信息
ISBN:
(纸本)9781728196817
Understanding environment dynamics is necessary for robots to act safely and optimally in the world. In realistic scenarios, dynamics are non-stationary and the causal variables such as environment parameters cannot necessarily be precisely measured or inferred, even during training. We propose Implicit Identification for Dynamics Adaptation (IIDA), a simple method to allow predictive models to adapt to changing environment dynamics. IIDA assumes no access to the true variations in the world and instead implicitly infers properties of the environment from a small amount of contextual data. We demonstrate IIDA's ability to perform well in unseen environments through a suite of simulated experiments on MuJoCo environments and a real robot dynamic sliding task. In general, IIDA significantly reduces model error and results in higher task performance over commonly used methods. Our code, video of the method, and latest paper is available here https://***/icra-iida/
This paper investigates the efficiency of autonomous indoor exploration utilizing simulation testing environments in Gazebo. Two exploration methods, Floodfill algorithm and Frontier-based algorithm, using the 2D LiDA...
详细信息
ISBN:
(纸本)9798331517939;9788993215380
This paper investigates the efficiency of autonomous indoor exploration utilizing simulation testing environments in Gazebo. Two exploration methods, Floodfill algorithm and Frontier-based algorithm, using the 2D LiDAR sensor are compared. The Floodfill algorithm employs a systematic traversal approach, while the Frontier-based method dynamically detects and navigates towards frontiers. Results indicate that the Frontier-based approach outperforms Floodfill Algorithm in terms of efficiency and map completeness, particularly in complex environments. The study underscores the importance of the Frontier-based strategy for autonomous indoor exploration and paves the way for enhanced robotic applications in diverse domains.
Autonomous mobile robotics are becoming a common sight in smart homes and industries, where they are used for tasks such as cleaning and material handling. In some cases, it is necessary to restrict the movement of th...
详细信息
ISBN:
(纸本)9781665489218
Autonomous mobile robotics are becoming a common sight in smart homes and industries, where they are used for tasks such as cleaning and material handling. In some cases, it is necessary to restrict the movement of these robots to certain areas or rooms during specific times or for specific tasks, in order to ensure safety and prevent interference. Traditional methods for creating virtual walls and borders to accomplish this often have limitations in terms of ease of use, universality, and remote accessibility. In order to overcome these issues, this work presents a method for creating virtual walls, doors, and borders using the Robotic Operating System (ROS) and point cloud data. This method can be used to restrict the movement of autonomous mobile robots in human-centered environments, including smart homes and warehouses, and can be activated and deactivated remotely. It is a flexible and cost-effective solution that does not require specialized sensors and can be used with single or multi-robot systems. The effectiveness of our method has been demonstrated through testing in simulation based on the reconstruction of real environments.
Numerical weather prediction (NWP) and machine learning (ML) methods are popular for weather forecasting. However, NWP models have multiple possible physical parameterizations, which requires site-specific NWP optimiz...
详细信息
ISBN:
(纸本)9781665476874
Numerical weather prediction (NWP) and machine learning (ML) methods are popular for weather forecasting. However, NWP models have multiple possible physical parameterizations, which requires site-specific NWP optimization. This is further complicated when regional NWP models are used with global climate models, each with multiple possible parameterizations. In this study, a hybrid numerical-statistical approach is proposed and evaluated for four radiation models. Weather Research and Forecasting (WRF) model is run in both global and regional mode to provide an estimate for solar irradiance. This estimate is then post-processed using ML to provide a final prediction. Normalized root-mean-square error from WRF is reduced by up to 40-50% with this ML error correction model. Results obtained using CAM, GFDL, New Goddard and RRTMG radiation models were comparable after this correction, negating the need for WRF parameterization tuning. Other models incorporating nearby locations and an ensemble set-up are also evaluated, although they produced much smaller improvements.
Place recognition using SOund Navigation and Ranging (SONAR) images is an important task for simultaneous localization and mapping (SLAM) in underwater environments. This paper proposes a robust and efficient imaging ...
详细信息
ISBN:
(纸本)9798350323658
Place recognition using SOund Navigation and Ranging (SONAR) images is an important task for simultaneous localization and mapping (SLAM) in underwater environments. This paper proposes a robust and efficient imaging SONAR-based place recognition, SONAR context, and loop closure method. Unlike previous methods, our approach encodes geometric information based on the characteristics of raw SONAR measurements without prior knowledge or training. We also design a hierarchical searching procedure for fast retrieval of candidate SONAR frames and apply adaptive shifting and padding to achieve robust matching on rotation and translation changes. In addition, we can derive the initial pose through adaptive shifting and apply it to the iterative closest point (ICP)based loop closure factor. We evaluate the SONAR context's performance in the various underwater sequences such as simulated open water, real water tank, and real underwater environments. The proposed approach shows the robustness and improvements of place recognition on various datasets and evaluation metrics. Supplementary materials are available at https://***/sparolab/sonar_***.
We consider the problem of task assignment and scheduling for human-robot teams to enable the efficient completion of complex problems, such as satellite assembly. In high-mix, low volume settings, we must enable the ...
详细信息
ISBN:
(纸本)9798350323658
We consider the problem of task assignment and scheduling for human-robot teams to enable the efficient completion of complex problems, such as satellite assembly. In high-mix, low volume settings, we must enable the human-robot team to handle uncertainty due to changing task requirements, potential failures, and delays to maintain task completion efficiency. We make two contributions: (1) we account for the complex interaction of uncertainty that stems from the tasks and the agents using a multi-agent concurrent MDP framework, and (2) we use Mixed Integer Linear Programs and contingency sampling to approximate action values for task assignment. Our results show that our online algorithm is computationally efficient while making optimal task assignments compared to a value iteration baseline. We evaluate our method on a 24-task representative assembly and a real-world 60-task satellite assembly, and we show that we can find an assignment that results in a near-optimal makespan.
Between 2% to 5% of children are affected by Developmental Coordination Disorders in Canada and have been diagnosed with upper limb impairments, which affect their daily lives and reduces their autonomy. Motor impairm...
详细信息
ISBN:
(纸本)9798350323658
Between 2% to 5% of children are affected by Developmental Coordination Disorders in Canada and have been diagnosed with upper limb impairments, which affect their daily lives and reduces their autonomy. Motor impairments can be part of progressive disorders, so despite regular therapy, progress remains fleeting. Affected individuals therefore consistently face many barriers, including entertainment opportunities, as availability of off-the-shelf inclusive technology is very limited. Our long-term goal is to develop a play-mediator robot, which would facilitate play between children with motor impairments and their peers or family members. Here, games that the robot can play are remotely controlled by the participants, using appropriate interfaces (e.g. joysticks). In this paper, we take the first step towards that goal and develop an adaptive joystick controller that can compensate for individual deficits. We monitor movement statistics to determine if re-calibration of the controller is necessary. Moreover, we propose a computational model of data 'distortion', as a tool for developers to test their technology in the very early stages of prototype development, without requiring access to participants. This work is validated with data from healthy adults and children with upper limb impairments.
Performing precise, repetitive motions is essential in many robotic and automation systems. Iterative learning control (ILC) allows determining the necessary control command by using a very rough system model to speed...
详细信息
ISBN:
(纸本)9798350323658
Performing precise, repetitive motions is essential in many robotic and automation systems. Iterative learning control (ILC) allows determining the necessary control command by using a very rough system model to speed up the process. Functional iterative learning control is a novel technique that promises to solve several limitations of classic ILC. It operates by merging the input space into a large functional space, resulting in an over-determined control task in the iteration domain. In this way, it can deal with systems having more outputs than inputs and accelerate the learning process without resorting to model discretizations. However, the framework lacks so far a validation in experiments. This paper aims to provide such experimental validation in the context of robotics. To this end, we designed and built a one-link flexible arm that is actuated by a stepper motor, which makes the development of an accurate model more challenging and the validation closer to the industrial practice. We provide multiple experimental results across several conditions, proving the feasibility of the method in practice.
The Multi-Object Navigation (MultiON) task requires a robot to localize an instance (each) of multiple object classes. It is a fundamental task for an assistive robot in a home or a factory. Existing methods for Multi...
详细信息
ISBN:
(纸本)9798350323658
The Multi-Object Navigation (MultiON) task requires a robot to localize an instance (each) of multiple object classes. It is a fundamental task for an assistive robot in a home or a factory. Existing methods for MultiON have viewed this as a direct extension of Object Navigation (ON), the task of localising an instance of one object class, and are pre-sequenced, i.e., the sequence in which the object classes are to be explored is provided in advance. This is a strong limitation in practical applications characterized by dynamic changes. This paper describes a deep reinforcement learning framework for sequence-agnostic MultiON based on an actor-critic architecture and a suitable reward specification. Our framework leverages past experiences and seeks to reward progress toward individual as well as multiple target object classes. We use photo-realistic scenes from the Gibson benchmark dataset in the AI Habitat 3D simulation environment to experimentally show that our method performs better than a pre-sequenced approach and a state of the art ON method extended to MultiON.
暂无评论