2019 19th International Conference on Advanced Robotics (ICAR)最新文献_第7页

Hybrid Model for A Priori Performance Prediction of Multi-Job Type Swarm Search and Service Missions 多作业型群搜索与服务任务的先验性能预测混合模型

2019 19th International Conference on Advanced Robotics (ICAR) Pub Date : 2019-12-01 DOI: 10.1109/ICAR46387.2019.8981582

M. Chandarana, Dana Hughes, M. Lewis, K. Sycara, S. Scherer

{"title":"Hybrid Model for A Priori Performance Prediction of Multi-Job Type Swarm Search and Service Missions","authors":"M. Chandarana, Dana Hughes, M. Lewis, K. Sycara, S. Scherer","doi":"10.1109/ICAR46387.2019.8981582","DOIUrl":"https://doi.org/10.1109/ICAR46387.2019.8981582","url":null,"abstract":"In Swarm Search and Service (SSS) applications, swarm vehicles are responsible for concurrently searching an area while immediately servicing jobs discovered while searching. Multiple job types may be present in the environment. As vehicles move in and out of the swarm to service jobs, the coverage rate (i.e., area searched by the swarm per time step) changes dynamically to reflect the number of vehicles currently engaged in search. As a result, the arrival rates of jobs also changes dynamically. When planning SSS missions, the resource requirements, such as the swarm size needed to achieve a desired system performance, must be determined. The dynamically changing arrival rates make traditional queuing methods ill-suited to predict the performance of the swarm. This paper presents a hybrid method - Hybrid Model - for predicting the performance of the swarm a priori. It utilizes a Markov model, whose state representation captures the proportion of agents searching or servicing jobs. State-dependent queuing models are used to calculate the state transition function of the Markov states. The model has been developed as a prediction tool to assist mission planners in balancing complex trade-offs, but also provides a basis for optimizing swarm size where cost functions are known. The Hybrid Model is tested in previously considered constant coverage rate scenarios and the results are compared to a previously developed Queuing Model. Additional SSS missions are then simulated and their resulting performance is used to further evaluate the effectiveness of using the Hybrid Model as a prediction tool for swarm performance in more general scenarios with dynamically changing coverage rates.","PeriodicalId":6606,"journal":{"name":"2019 19th International Conference on Advanced Robotics (ICAR)","volume":"11 1","pages":"714-719"},"PeriodicalIF":0.0,"publicationDate":"2019-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"74674879","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 3

Computing multiple guiding paths for sampling-based motion planning 基于采样的多路径运动规划计算

2019 19th International Conference on Advanced Robotics (ICAR) Pub Date : 2019-12-01 DOI: 10.1109/ICAR46387.2019.8981589

Vojtěch Vonásek, Robert Pěnička, B. Kozlíková

{"title":"Computing multiple guiding paths for sampling-based motion planning","authors":"Vojtěch Vonásek, Robert Pěnička, B. Kozlíková","doi":"10.1109/ICAR46387.2019.8981589","DOIUrl":"https://doi.org/10.1109/ICAR46387.2019.8981589","url":null,"abstract":"Path planning of 3D solid objects leads to search in a six-dimensional configuration space, which can be solved by sampling-based motion planning. The well-known issue of sampling-based planners is the narrow passage problem, which is caused by the presence of small regions of the configuration space that are difficult to cover by random samples. Guided-based planners cope with this issue by increasing the probability of sampling along an estimated solution (a guiding path). In the case of six-dimensional configuration space, the guiding path needs to be computed in the configuration space rather than in the workspace. Fast computation of guiding paths can be achieved by solving a similar, yet simpler problem, e.g., by reducing the size of the robot. This results in an approximate solution (path) that is assumed to be located near the solution of the original problem. The guided sampling along this approximate solution may, however, fail if the approximate solution is too far from the desired solution. In this paper, we cope with this problem by sampling the configuration space along multiple approximate solutions. The approximate solutions are computed using a proposed iterative process: after a path (solution) is found, it forms a region where the subsequent search is inhibited, which boosts the search of new solutions. The performance of the proposed approach is verified in scenarios with multiple narrow passages and compared with the state-of-the-art planners.","PeriodicalId":6606,"journal":{"name":"2019 19th International Conference on Advanced Robotics (ICAR)","volume":"33 1","pages":"374-381"},"PeriodicalIF":0.0,"publicationDate":"2019-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"75090670","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 7

Augmented Vector Field Navigation Cost Mapping using Inertial Sensors 基于惯性传感器的增广矢量场导航成本映射

2019 19th International Conference on Advanced Robotics (ICAR) Pub Date : 2019-12-01 DOI: 10.1109/ICAR46387.2019.8981572

Felipe G. Oliveira, A. A. Neto, P. Borges, M. Campos, D. Macharet

引用次数: 3

A Visual Method to study the Error Function of ICP Algorithms 一种研究ICP算法误差函数的可视化方法

2019 19th International Conference on Advanced Robotics (ICAR) Pub Date : 2019-12-01 DOI: 10.1109/ICAR46387.2019.8981610

Sebastian Dingler, H. Burrichter

引用次数: 0

Auto-Tuning of GPC weights based on Particle Swarm Optimization applied to a Manipulator End-Effector Trajectory Tracking 基于粒子群优化的GPC权值自整定在机械臂末端执行器轨迹跟踪中的应用

2019 19th International Conference on Advanced Robotics (ICAR) Pub Date : 2019-12-01 DOI: 10.1109/ICAR46387.2019.8981607

Felipe J. S. Vasconcelos, Iury de Amorim Gaspar Filgueiras, W. Correia

引用次数: 1

Hand-Guidance of a Mobile Manipulator Using Online Effective Mass Optimization 基于在线有效质量优化的移动机械臂手动制导

2019 19th International Conference on Advanced Robotics (ICAR) Pub Date : 2019-12-01 DOI: 10.1109/ICAR46387.2019.8981657

Christian Ritter, Shashank Sharma

引用次数: 1

On the Robust Longitudinal Trajectory Tracking for Load Transportation Vehicles on Uneven Terrains 非平坦路面载重车辆的鲁棒纵向轨迹跟踪研究

2019 19th International Conference on Advanced Robotics (ICAR) Pub Date : 2019-12-01 DOI: 10.1109/ICAR46387.2019.8981641

Victor R. F. Miranda, L. Mozelli, A. A. Neto, G. Freitas

引用次数: 4

Voting Based System for Robust 3D Hand Pose Estimation and Tracking 基于投票的三维手部姿态估计与跟踪系统

2019 19th International Conference on Advanced Robotics (ICAR) Pub Date : 2019-12-01 DOI: 10.1109/ICAR46387.2019.8981614

Mohammad Asif, Andreas Daasch, Hendrik Unger, M. Schultalbers

引用次数: 0

Safer UAV Piloting: A Robust Sense-and-Avoid Solution for Remotely Piloted Quadrotor UAVs in Complex Environments 更安全的无人机驾驶:复杂环境下遥控四旋翼无人机的鲁棒感知与避免解决方案

2019 19th International Conference on Advanced Robotics (ICAR) Pub Date : 2019-12-01 DOI: 10.1109/ICAR46387.2019.8981576

Min Wang, H. Voos

引用次数: 2

Experimental study on model- vs. learning-based slip detection 基于模型与基于学习的滑动检测的实验研究

2019 19th International Conference on Advanced Robotics (ICAR) Pub Date : 2019-12-01 DOI: 10.1109/ICAR46387.2019.8981630

L. Rosset, Monika Florek-Jasinska, M. Suppa, M. Roa

引用次数: 4