This paper introduces a novel motion planner, incrementally stochastic and accelerated gradient information mixed optimization (iSAGO), for robotic manipulators in a narrow workspace. Primarily, we propose the overall scheme of iSAGO informed by the mixed momenta for an efficient constrained optimization based on the penalty method. In the stochastic part, we generate the adaptive stochastic momenta via the random selection of sub-functionals based on the adaptive momentum (Adam) method to solve the body-obstacle stuck case. Due to the slow convergence of the stochastic part, we integrate the accelerated gradient descent (AGD) to improve the planning efficiency. Moreover, we adopt the Bayesian tree inference (BTI) to transform the whole trajectory optimization (SAGO) into an incremental sub-trajectory optimization (iSAGO), which improves the computation efficiency and success rate further. Finally, we tune the key parameters and benchmark iSAGO against the other 5 planners on LBR-iiwa in a bookshelf and AUBO-i5 on a storage shelf. The result shows the highest success rate and moderate solving efficiency of iSAGO.
A study on a Real-Time VR-Based Teleoperation Framework for Manipulator in Dynamic Environment
Department of Robotics, Hanyang University（韩世大学机器人系）；Department of Smart Construction Engineering, Hanyang University（韩世大学智能建造工程系）；Department of Interdisciplinary Robot Engineering Systems, Hanyang University（韩世大学跨学科机器人工程系统系）；School of Smart Convergence Engineering, Hanyang University, Ansan（韩世大学智能融合工程学院，安山）
We propose a new formulation of optimal motion planning (OMP) algorithm for robots operating in a hazardous environment, called adaptive Gaussian-process based stochastic trajectory optimization (AGP-STO). It first restarts the accelerated gradient descent with the reestimated Lipschitz constant (L-reAGD) to improve the computation efficiency, only requiring 1st-order momentum. However, it still cannot infer a global optimum of the nonconvex problem, informed by the prior information of Gaussian-process (GP) and obstacles. So it then integrates the adaptive stochastic trajectory optimization (…
Institut de Robòtica i Informàtica Industrial, CSIC-UPC（工业机器人信息研究所，CSIC-UPC）
This paper presents a motion planner for systems subject to kinematic and dynamic constraints. The former appear when kinematic loops are present in the system, such as in parallel manipulators, in robots that cooperate to achieve a given task, or in situations involving contacts with the environment. The latter are necessary to obtain realistic trajectories, taking into account the forces acting on the system. The kinematic constraints make the state space become an implicitly-defined manifold, which complicates the application of common motion planning techniques. To address this issue, the …
GATO: GPU-Accelerated and Batched Trajectory Optimization for Scalable Edge Model Predictive Control
School of Engineering and Applied Science, Columbia University（哥伦比亚大学工程与应用科学学院）；University of Michigan（密歇根大学）；Barnard College, Columbia University and Dartmouth College（哥伦比亚大学巴纳德学院和达特茅斯学院）
University of North Carolina at Chapel Hill（北卡罗来纳大学教堂山分校）；Carnegie Mellon University（卡内基梅隆大学）
Inspection planning, the task of planning motions that allow a robot to inspect a set of points of interest, has applications in domains such as industrial, field, and medical robotics. Inspection planning can be computationally challenging, as the search space over motion plans that inspect the points of interest grows exponentially with the number of inspected points. We propose a novel method, Incremental Random Inspection-roadmap Search (IRIS), that computes inspection plans whose length and set of inspected points asymptotically converge to those of an optimal inspection plan. IRIS increm…
Correct-by-construction manipulation planning in a dynamic environment, where other agents can manipulate objects in the workspace, is a challenging problem. The tight coupling of actions and motions between agents and complexity of mission specifications makes the problem computationally intractable. This paper presents a reactive integrated mission and motion planning for mobile-robot manipulator systems operating in a partially known environment. We introduce a multi-layered synergistic framework that receives high-level mission specifications expressed in linear temporal logic and generate…
Multi-Robot Motions in Milliseconds: Vector-Accelerated Primitives for Sampling-Based Planning
Parasol Lab, School of Computing and Data Science, University of Illinois at Urbana Champaign（帕索尔实验室，计算与数据科学学院，伊利诺伊大学厄巴纳-香槟分校）；Department of Computer Science at Instituto Tecnológico Autónomo de México (ITAM)（墨西哥自治理工学院（ITAM）计算机科学系）
Earlier work has shown that reusing experience from prior motion planning problems can improve the efficiency of similar, future motion planning queries. However, for robots with many degrees-of-freedom, these methods exhibit poor generalization across different environments and often require large datasets that are impractical to gather. We present SPARK and FLAME , two experience-based frameworks for sampling-based planning applicable to complex manipulators in 3 D environments. Both combine samplers associated with features from a workspace decomposition into a global biased sampling distri…
Department of Electrical and Computer Engineering, University of California San Diego（加利福尼亚大学圣地亚哥分校电气与计算机工程系）；University of California San Diego（加利福尼亚大学圣地亚哥分校）；Electrical and Computer Engineering, University of California San Diego（加利福尼亚大学圣地亚哥分校电气与计算机工程）
This paper demonstrates that collision detection-intensive applications such as robotic motion planning may be accelerated by performing collision checks with a machine learning model. We propose Fastron, a learning-based algorithm to model a robot's configuration space to be used as a proxy collision detector in place of standard geometric collision checkers. We demonstrate that leveraging the proxy collision detector results in up to an order of magnitude faster performance in robot simulation and planning than state-of-the-art collision detection libraries. Our results show that Fastron lea…
Incremental Language Understanding for Online Motion Planning of Robot Manipulators
Human-Robot Interaction Lab, Tufts University（塔夫茨大学人机交互实验室）；Automation and Control Institute (ACIN), TU Wien（自动化与控制研究所（ACIN））；center for Vision, Automation & Control, AIT Austrian Institute of Technology GmbH（视觉、自动化与控制中心，奥地利技术研究院）
University of Oxford（牛津大学）；University of Zagreb Faculty of Electrical Engineering and Computing（萨格勒布大学电气工程与计算学院）
Over the years, the separate fields of motion planning, mapping, and human trajectory prediction have advanced considerably. However, the literature is still sparse in providing practical frameworks that enable mobile manipulators to perform whole-body movements and account for the predicted motion of moving obstacles. Previous optimisation-based motion planning approaches that use distance fields have suffered from the high computational cost required to update the environment representation. We demonstrate that GPU-accelerated predicted composite distance fields significantly reduce the comp…
From Perception to Assistance: Open-Vocabulary Shared Autonomy for Robotic Manipulation
University of São Paulo（圣保罗大学）；Instituto Israelita de Ensino e Pesquisa Albert Einstein（以色列爱因斯坦教育与研究机构）；Graduate School of Information Sciences, Tohoku University（东北大学信息科学研究生院）
Morphogenetic Assembly and Adaptive Control for Heterogeneous Modular Robots
Shanghai Research Institute for Intelligent Autonomous Systems, Tongji University, Shanghai, China（上海智能自主系统研究院，同济大学，上海，中国）；Department of Mechanical and Automation Engineering and T Stone Robotics Institute, The Chinese University of Hong Kong, Hong Kong（机械与自动化工程系和T Stone机器人研究院，香港中文大学，香港）
