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Robot motion planning involves computing a sequence of valid robot configurations that take the robot from its initial state to a goal state. Solving a motion planning problem optimally using analytical methods is proven to be PSPACE-Hard.…

机器人学 · 计算机科学 2021-07-26 Naman Shah , Abhyudaya Srinet , Siddharth Srivastava

Constrained robot motion planning is a ubiquitous need for robots interacting with everyday environments, but it is a notoriously difficult problem to solve. Many sampled points in a sample-based planner need to be rejected as they fall…

机器人学 · 计算机科学 2023-09-28 Jacob J. Johnson , Ahmed H. Qureshi , Michael C. Yip

In this paper, we propose an online path planning architecture that extends the model predictive control (MPC) formulation to consider future location uncertainties for safer navigation through cluttered environments. Our algorithm combines…

Robotic manipulation relies on analytical or learned models to simulate the system dynamics. These models are often inaccurate and based on offline information, so that the robot planner is unable to cope with mismatches between the…

机器人学 · 计算机科学 2024-03-13 Marco Faroni , Dmitry Berenson

Real-time motion planning is a vital function of robotic systems. Different from existing roadmap algorithms which first determine the free space and then determine the collision-free path, researchers recently proposed several convex…

机器人学 · 计算机科学 2019-03-01 Chaoyi Sun , Qing Li , Li Li

We consider the problem of planning a collision-free path for a high-dimensional robot. Specifically, we suggest a planning framework where a motion-planning algorithm can obtain guidance from a user. In contrast to existing approaches that…

机器人学 · 计算机科学 2018-07-24 Fahad Islam , Oren Salzman , Maxim Likhachev

Consider a robot operating in an uncertain environment with stochastic, dynamic obstacles. Despite the clear benefits for trajectory optimization, it is often hard to keep track of each obstacle at every time step due to sensing and…

系统与控制 · 电气工程与系统科学 2022-03-08 Michael Hibbard , Abraham P. Vinod , Jesse Quattrociocchi , Ufuk Topcu

Motivated by the requirements for effectiveness and efficiency, path-speed decomposition-based trajectory planning methods have widely been adopted for autonomous driving applications. While a global route can be pre-computed offline,…

机器人学 · 计算机科学 2025-05-07 Faizan M. Tariq , Zheng-Hang Yeh , Avinash Singh , David Isele , Sangjae Bae

Latest research in industrial robotics is aimed at making human robot collaboration possible seamlessly. For this purpose, industrial robots are expected to work on the fly in unstructured and cluttered environments and hence the subject of…

机器人学 · 计算机科学 2019-11-13 Indraneel Patil , B. K. Rout , V. Kalaichelvi

This paper presents a novel method for reformulating non-differentiable collision avoidance constraints into smooth nonlinear constraints using strong duality of convex optimization. We focus on a controlled object whose goal is to avoid…

最优化与控制 · 数学 2018-06-12 Xiaojing Zhang , Alexander Liniger , Francesco Borrelli

This article presents a multi-robot trajectory planning method which not only guarantees optimization feasibility and but also resolves deadlocks in obstacle-dense environments. The method is proposed via formulating a recursive…

机器人学 · 计算机科学 2023-02-23 Yuda Chen , Chenghan Wang , Meng Guo , Zhongkui Li

Tendon-driven continuum robots (TDCRs), with their flexible backbones, offer the advantage of being used for navigating complex, cluttered environments. However, to do so, they typically require multiple segments, often leading to complex…

机器人学 · 计算机科学 2024-02-23 Priyanka Rao , Oren Salzman , Jessica Burgner-Kahrs

Motion planning is an essential process for the navigation of unmanned aerial vehicles (UAVs) where they need to adapt to obstacles and different structures of their operating environment to reach the goal. This paper presents an optimal…

机器人学 · 计算机科学 2024-10-15 Duy-Nam Bui , Thu Hang Khuat , Manh Duong Phung , Thuan-Hoang Tran , Dong LT Tran

We propose an efficient motion planning method designed to efficiently find collision-free trajectories for multiple manipulators. While multi-manipulator systems offer significant advantages, coordinating their motions is computationally…

机器人学 · 计算机科学 2025-09-18 Junhwa Hong , Beomjoon Lee , Woojin Lee , Changjoo Nam

The prospect of using autonomous robots to enhance the capabilities of physicians and enable novel procedures has led to considerable efforts in developing medical robots and incorporating autonomous capabilities. Motion planning is a core…

机器人学 · 计算机科学 2022-10-21 Inbar Fried , Jason A. Akulian , Ron Alterovitz

Multi-robot systems have begun to permeate into a variety of different fields, but collision-free navigation in a decentralized manner is still an arduous task. Typically, the navigation of high speed multi-robot systems demands replanning…

In this paper, we consider a robot navigation problem in environments populated by humans. The goal is to determine collision-free and dynamically feasible trajectories that also maximize human satisfaction. This is because they may drive…

机器人学 · 计算机科学 2020-11-04 Yijie Zhou , Yan Zhang , Xusheng Luo , Michael M. Zavlanos

Traditional offline redundancy resolution of trajectories for redundant manipulators involves computing inverse kinematic solutions for Cartesian space paths, constraining the manipulator to a fixed path without real-time adjustments.…

机器人学 · 计算机科学 2025-03-19 Zhihang Yin , Fa Wu , Ziqian Wang , Jianmin Yang , Jiyong Tan , Dexing Kong

This paper deals with a path planning and intelligent control of an autonomous robot which should move safely in partially structured environment. This environment may involve any number of obstacles of arbitrary shape and size; some of…

机器人学 · 计算机科学 2007-05-23 Danica Janglova

Existing motion planning methods often struggle with rapid-motion obstacles due to an insufficient understanding of environmental changes. To address this, we propose integrating motion planners with Doppler LiDARs, which provide not only…

机器人学 · 计算机科学 2026-05-26 Wei Zuo , Zeyi Ren , Chengyang Li , Yikun Wang , Mingle Zhao , Shuai Wang , Wei Sui , Fei Gao , Yik-Chung Wu , Chengzhong Xu