We present progress on the problem of reconfiguring a 2D arrangement of building material by a cooperative group of robots. These robots must avoid collisions, deadlocks, and are subjected to the constraint of maintai...
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ISBN:
(纸本)9798350384581;9798350384574
We present progress on the problem of reconfiguring a 2D arrangement of building material by a cooperative group of robots. These robots must avoid collisions, deadlocks, and are subjected to the constraint of maintaining connectivity of the structure. We develop two reconfiguration methods, one based on spatio-temporal planning, and one based on target swapping, to increase building efficiency. The first method can significantly reduce planning times compared to other multi-robot planners. The second method helps to reduce the amount of time robots spend waiting for paths to be cleared, and the overall distance traveled by the robots.
In this paper, we focus on improving planning efficiency for ground vehicles in navigation and exploration tasks where the environment is unknown or partially known, leading to frequent updates of the navigational goa...
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ISBN:
(纸本)9798350377712;9798350377705
In this paper, we focus on improving planning efficiency for ground vehicles in navigation and exploration tasks where the environment is unknown or partially known, leading to frequent updates of the navigational goal as new sensory information is acquired. Asymptotically optimal motion planners like RRT* or FMT* can be used to plan the sequence of actions the robot can follow to achieve its current goal. Frequent replanning of the whole action sequence becomes computationally demanding when actions are not executed precisely because of limited information about the foreground terrain. The decoupled approach can decrease the computational burden with separated path planning and path following;however, it might lead to suboptimal solutions. Therefore, we propose a novel approach based on generating a reusable reward function that guides a fast sampling-based motion planner. The proposed method provides improved results in navigation scenarios compared to the former approaches, and it led to about 7% faster autonomous exploration than the decoupled approach. The present results support the suitability of the proposed method in navigation tasks with continuously updated navigation goals.
In the field of robotics, researches have sought to control robots capable of dealing with a variety of environments and tasks generically, through the use of foundation models. Among these, the systems for robot beha...
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ISBN:
(纸本)9798350377712;9798350377705
In the field of robotics, researches have sought to control robots capable of dealing with a variety of environments and tasks generically, through the use of foundation models. Among these, the systems for robot behavior planning utilizing video have also been proposed. The system enables the generation of robot behaviors that are not dependent on specific environments or tasks. This is achieved by generating videos based on text input, which utilizes the vast knowledge inherent in the foundation models. Also, by using a visual interface such as video, it is possible to confirm the behavioral indicators on which the robot is operating. Although, there are few examples of research on robot behavior planning utilizing video. Previous studies have emphasized the verification of behavior generation utilizing video, with simplified object manipulation for testing on simulations. This is not enough to demonstrate the usefulness of robot behavior planning utilizing video in real-world environments. In addition, the systems from previous studies are not open, and such systems have not been sufficiently discussed. This paper attempts to construct robot behavior planning utilizing video as an open system, and to verify the validity of the behavior planning using actual machines. In this paper, we first focus on using robotis's TURTLEBOT3 Waffle Pi and Mobile Manipulator(referred to as "Waffle") to construct robot behavior planning system utilizing video. Second, we create planning videos targeting the pick-and-place motion using the proposed system, and control the arm part of Waffle in the actual machine verification. Finally, by comparing the target coordinates from the planning video with the coordinates observed from the actual machine, we can confirm whether it is possible to control Waffle as planned. Errors are calculated from the coordinate comparison, and the control is performed again. Based on the results, we verify whether the proposed system is useful for contro
When exploring new areas, robotic systems generally exclusively plan and execute controls over geometry that has been directly measured. This planning paradigm can lead to unintuitive exploration or replanning latency...
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ISBN:
(纸本)9798350377712;9798350377705
When exploring new areas, robotic systems generally exclusively plan and execute controls over geometry that has been directly measured. This planning paradigm can lead to unintuitive exploration or replanning latency when entering areas that were previous obstructed from view. To address this we present SceneSense, a real-time 3D diffusion model for synthesizing 3D occupancy information from partial observations that effectively predicts these occluded or out of view geometries for use in future planning and control frameworks. SceneSense uses a running occupancy map and a single RGB-D camera to generate predicted geometry around the platform at runtime, even when the geometry is occluded or out of view. Our architecture ensures that SceneSense never overwrites observed free or occupied space. By preserving the integrity of the observed map, SceneSense mitigates the risk of corrupting the observed space with generative predictions. While SceneSense is shown to operate well using a single RGB-D camera, the framework is flexible enough to extend to additional modalities. Unlike existing models that necessitate multiple views and offline scene synthesis, or are focused on filling gaps in observed data, our findings demonstrate that SceneSense is an effective approach to estimating unobserved local occupancy information at runtime. Local occupancy predictions from SceneSense are shown to better represent the ground truth occupancy distribution during the test exploration trajectories than the running occupancy map. The source code can be found on our website: https://***/scenesense/
Human memory of a robot's competence, and resulting subjective perceptions of that robot, are influenced by numerous cognitive biases. One class of cognitive bias deals with the ordering of items or interactions: ...
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ISBN:
(纸本)9798350384581;9798350384574
Human memory of a robot's competence, and resulting subjective perceptions of that robot, are influenced by numerous cognitive biases. One class of cognitive bias deals with the ordering of items or interactions: information presented last among a grouping is most salient in memory formation (recency bias), followed by information presented first (primacy bias), followed by information in the middle, collectively known as the serial-position effect. For example, if a human's last observation of a robot involves a task failure, this will disproportionately negatively alter their perception of the robot's competence, as well as their trust in the robot moving forward. It is valuable to characterize the effect of these biases within human-robot interactions to inform strategies for risk-aware planning that cultivate appropriate levels of human trust. We conducted a human-subjects study (n=53) testing the influence of the serial-position effect on recalled competence (see overview at https://***/BgH2zhh1s48). Participants viewed videos of a robot performing the same tasks at the same level of competence, with task order differing by experimental condition (rising competence, falling competence, or failures at the midpoint), asking participants to rate robot competence in between every video as well at the very end of the experiment. We found that while the average between-video rating of robot competence remained stable across conditions, the recalled, post-experiment ratings of competence and trust were significantly lower in the condition with decreasing competence than in either of the other two conditions, suggesting a notable recency bias. We conclude with implications for human-subjects experiment design (i.e., how subjective measures are influenced by ordering effects) and provide design recommendations to minimize them. We further discuss practical applications of these results in creating risk-aware robotic planners capable of trust calibration.
The robot soccer game has been considered as an illustrative scenario to test the performance of research outcomes on multi-agent systems (MAS). While various algorithm has been developed for a robot soccer game and i...
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ISBN:
(纸本)9798350385731;9798350385724
The robot soccer game has been considered as an illustrative scenario to test the performance of research outcomes on multi-agent systems (MAS). While various algorithm has been developed for a robot soccer game and implemented in the RoboCup competition, relatively little collaboration can be found in existing results, e.g., the formation for passing and dribbling during the offense or the collaborative obstruction during defense, which are very common in the teamwork of human soccer game. This paper focuses on the collaboration problem in robot soccer games and proposes a dynamic formation planning and control strategy. Specifically, the offensive team maintains a desired formation and transits among the tasks of catching, passing, dribbling, and kicking based on a behavioral model. The defensive team is regulated by a reinforcement-learning-based policy network and dynamically forms a barrier to obstruct the offender. The strategies of both teams will swap according to the evolving situation of the match. Both simulation studies and real-world experiments are carried out to illustrate the performance of the proposed method, and the convergence to the desired formation is also rigorously proved.
Rapid sampling from the environment to acquire available frontier points and timely incorporating them into subsequent planning to reduce fragmented regions are critical to improve the efficiency of autonomous explora...
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ISBN:
(纸本)9798350377712;9798350377705
Rapid sampling from the environment to acquire available frontier points and timely incorporating them into subsequent planning to reduce fragmented regions are critical to improve the efficiency of autonomous exploration. We propose HPHS, a fast and effective method for the autonomous exploration of unknown environments. In this work, we efficiently sample frontier points directly from the LiDAR data and the local map around the robot, while exploiting a hierarchical planning strategy to provide the robot with a global perspective. The hierarchical planning framework divides the updated environment into multiple subregions and arranges the order of access to them by considering the overall revenue of the global path. The combination of the hybrid frontier sampling method and hierarchical planning strategy reduces the complexity of the planning problem and mitigates the issue of region remnants during the exploration process. Detailed simulation and real-world experiments demonstrate the effectiveness and efficiency of our approach in various aspects. The source code will be released to benefit the further research(1).
The operating mode power and force limiting protects the human operator during physical human-robot collaboration by limiting contact-related quantities. Using biomechanical injury criteria and contact models, thresho...
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ISBN:
(纸本)9798350358513;9798350358520
The operating mode power and force limiting protects the human operator during physical human-robot collaboration by limiting contact-related quantities. Using biomechanical injury criteria and contact models, thresholds for the energy transferred to the body part during potential contacts can be estimated for unconstrained contacts. In turn, robot velocity limits can be derived from the transferred energy thresholds and can subsequently be taken into account in motion planning and safety functions. A common assumption is to consider the effective robot mass as constant, which was shown to not always be conservative. In this paper, trajectories are optimized for a fixed path constraining the transferred energy calculated with the configuration- and direction-dependent robot mass instead. This yields optimal motions, satisfying transferred energy thresholds in cases where the approximation is nonconservative. For cases in which the constant mass approximation is conservative, cycle time reductions are achieved. The cycle time can be reduced further by optimizing trajectories without prescribing the path a-priori showing the potential towards increased motion efficiency for applications with power and force limiting.
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.
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