This paper presents the development of autonomous mobile robotics system for road marks painting in smart cities. The current road marks painting is manually applied on the road worldwide, and the quality of construct...
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In the logistics field, due to the declining birthrate, aging population, and shrinking workforce, there is growing demand for automation of manual handling tasks. Focusing on robotic picking operations, we developed ...
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ISBN:
(纸本)9798350355376;9798350355369
In the logistics field, due to the declining birthrate, aging population, and shrinking workforce, there is growing demand for automation of manual handling tasks. Focusing on robotic picking operations, we developed two grasping methods for various items: rule-based grasp planning that considers the physical characteristics of the items and environment, and DNN-based grasp planning that can learn the grasping points obtained by the same method. Rule-based grasp planning is computationally time-consuming, and DNN-based grasp planning has a lower success rate. Therefore, this paper proposes hybrid-AI grasp planning that integrates these grasp planning methods. We effectively demonstrated that selecting an appropriate grasp planning method by the developed selector can improve throughput because it can combine a high success rate with fast calculation time.
Object manipulation has been extensively studied in the context of fixed base and mobile manipulators. However, the overactuated locomotion modality employed by snake robots allows for a unique blend of object manipul...
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ISBN:
(纸本)9798350377712;9798350377705
Object manipulation has been extensively studied in the context of fixed base and mobile manipulators. However, the overactuated locomotion modality employed by snake robots allows for a unique blend of object manipulation through locomotion, referred to as loco-manipulation. The following work presents an optimization approach to solving the locomanipulation problem based on non-impulsive implicit contact path planning for our snake robot COBRA. We present the mathematical framework and show high-fidelity simulation results and experiments to demonstrate the effectiveness of our approach.
Autonomous Underwater Vehicles (AUVs) need to operate for days without human intervention and thus must be able to do efficient and reliable task planning. Unfortunately, efficient task planning requires deliberately ...
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ISBN:
(纸本)9798350377712;9798350377705
Autonomous Underwater Vehicles (AUVs) need to operate for days without human intervention and thus must be able to do efficient and reliable task planning. Unfortunately, efficient task planning requires deliberately abstract domain models (for scalability reasons), which in practice leads to plans that might be unreliable or under performing in practice. An optimal abstract plan may turn out suboptimal or unreliable during physical execution. To overcome this, we introduce a method that first generates a selection of diverse high-level plans and then assesses them in a low-level simulation to select the optimal and most reliable candidate. We evaluate the method using a realistic underwater robot simulation, estimating the risk metrics for different scenarios, demonstrating feasibility and effectiveness of the approach.
Last-mile delivery robot has been attracted increasing attention from industry and comes into our daily life recently. However, how to safely and effectively navigate among crowded pedestrians is still an open problem...
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ISBN:
(纸本)9798350364200;9798350364194
Last-mile delivery robot has been attracted increasing attention from industry and comes into our daily life recently. However, how to safely and effectively navigate among crowded pedestrians is still an open problem. It requires the robot capable of analysing where it can traverse, understanding the intentions of surrounding pedestrians, planning the trajectory with social awareness, etc. In this paper, we have successfully completed a systematic implementation for navigation of delivery robot in pedestrian crowded environments. First, we introduced the Nanyang Sidewalk dataset, designed explicitly for class segmentation tasks on sidewalks. Second, a multi-modal 3D detection and motion prediction integrated with the social force model has been introduced to perceive the intention of pedestrians. Then, a socially aware motion planner for the delivery robot is demonstrated by following pedestrian etiquette. Extensive experiments have been conducted to verify and evaluate the performance of the proposed algorithm.
Reactive planning enables the robots to deal with dynamic events in uncertain environments. However, existing methods heavily rely on the predefined hard-coded robot behaviors, e.g, a pre-coded temporal logic formula ...
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ISBN:
(纸本)9798350384581;9798350384574
Reactive planning enables the robots to deal with dynamic events in uncertain environments. However, existing methods heavily rely on the predefined hard-coded robot behaviors, e.g, a pre-coded temporal logic formula that specifies how robot should react. Little attention has been paid for autonomous generation of reactive tasks specifications during the runtime. As a first attempt towards this goal, this work develops a real-time decision-making and motion planning framework. It allows the robot to follow a global task planned offline while taking proactive decisions and generating temporal logic specifications for local reactive tasks when encountering dynamic events. Specifically, inspired by the causal knowledge graph, a proposition graph is developed, based on which the decision module encode the environment and the task as the Boolean logic and linear temporal logic (LTL), respectively. Based on the established proposition graph and perceived environment, the agent can autonomously generate an LTL formula to realize the local temporary task. A joint sampling algorithm is then developed, in which the automaton states of local and global task are jointly considered to generate a feasible planning that satisfies both global and local tasks. Experiments demonstrate the effectiveness of the proposed decision-making and motion planning.
In this work, we introduce SMART-LLM, an innovative framework designed for embodied multi-robot task planning. SMART-LLM: Smart Multi-Agent robot Task Planning using Large Language Models (LLMs), harnesses the power o...
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ISBN:
(纸本)9798350377712;9798350377705
In this work, we introduce SMART-LLM, an innovative framework designed for embodied multi-robot task planning. SMART-LLM: Smart Multi-Agent robot Task Planning using Large Language Models (LLMs), harnesses the power of LLMs to convert high-level task instructions provided as input into a multi-robot task plan. It accomplishes this by executing a series of stages, including task decomposition, coalition formation, and task allocation, all guided by programmatic LLM prompts within the few-shot prompting paradigm. We create a benchmark dataset designed for validating the multi-robot task planning problem, encompassing four distinct categories of high-level instructions that vary in task complexity. Our evaluation experiments span both simulation and real-world scenarios, demonstrating that the proposed model can achieve promising results for generating multi-robot task plans. The experimental videos, code, and datasets from the work can be found at https://***/view/smart-llm/.
In this paper, we introduce a ROS based framework designed for the planning and control of robotic systems within the context of precision agriculture, with an emphasis on human-in-the-loop capabilities. Utilizing Lin...
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ISBN:
(纸本)9798350358513;9798350358520
In this paper, we introduce a ROS based framework designed for the planning and control of robotic systems within the context of precision agriculture, with an emphasis on human-in-the-loop capabilities. Utilizing Linear Temporal Logic to articulate complex task specifications, our algorithm creates high-level robotic plans that are not only correct by design but also adaptable in real time by human operators. This dual-focus approach ensures that while humans have the flexibility to modify the high-level plan on-the-fly or even take over low-level control of the robots, the system inherently safeguards against any human actions that could potentially breach the predefined task specifications. We demonstrate our algorithm within the dynamic and challenging environment of a real vineyard, where the collaboration between human workers and robots is critical for tasks such as harvesting and pruning, and show the practical applicability and robustness of our software. This work marks a pioneering application of formal methods to complex, real-world agricultural environments.
Learning from Demonstration (LfD) enables robots to acquire versatile skills by learning motion policies from human demonstrations. It endows users with an intuitive interface to transfer new skills to robots without ...
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Learning from Demonstration (LfD) enables robots to acquire versatile skills by learning motion policies from human demonstrations. It endows users with an intuitive interface to transfer new skills to robots without the need for time-consuming robot programming and inefficient solution exploration. During task executions, the robot motion is usually influenced by constraints imposed by environments. In light of this, task-parameterized LfD (TP-LfD) encodes relevant contextual information into reference frames, enabling better skill generalization to new situations. However, most TP-LfD algorithms typically require multiple demonstrations across various environmental conditions to ensure sufficient statistics for a meaningful model. It is not a trivial task for robot users to create different situations and perform demonstrations under all of them. Therefore, this paper presents a novel algorithm to learn skills from few demonstrations. By leveraging the reference frame weights that capture the frame importance or relevance during task executions, our method demonstrates excellent skill acquisition performance, which is validated in real robotic environments (The video of the experiments is available at https://***/JpGjk4eKC3o.).
High-level robot skills represent an increasingly popular paradigm in robot programming. However, configuring the skills' parameters for a specific task remains a manual and time-consuming endeavor. Existing appro...
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ISBN:
(纸本)9798350377712;9798350377705
High-level robot skills represent an increasingly popular paradigm in robot programming. However, configuring the skills' parameters for a specific task remains a manual and time-consuming endeavor. Existing approaches for learning or optimizing these parameters often require numerous real-world executions or do not work in dynamic environments. To address these challenges, we propose Multimodal Trajectory Transformer (MuTT), a novel encoder-decoder transformer architecture designed to predict environment-aware executions of robot skills by integrating vision, trajectory, and robot skill parameters. Notably, we pioneer the fusion of vision and trajectory, introducing a novel trajectory projection. Furthermore, we illustrate MuTT's efficacy as a predictor when combined with a model-based robot skill optimizer. This approach facilitates the optimization of robot skill parameters for the current environment, without the need for real-world executions during optimization. Designed for compatibility with any representation of robot skills, MuTT demonstrates its versatility across three comprehensive experiments, showcasing superior performance across two different skill representations.
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