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Local navigation is one of the fundamental problems in robot navigation, and numerous approaches have been proposed over the years, including methods such as the Dynamic Window Approach, Model Predictive Control, and more recently, Control…

机器人学 · 计算机科学 2026-05-18 Scott Fredriksson , Akshit Saradagi , George Nikolakopoulos

In this paper we present a novel method for obstacle avoidance using the stereo camera. The conventional obstacle avoidance methods and their limitations are discussed. A new algorithm is developed for the real-time obstacle avoidance which…

计算机视觉与模式识别 · 计算机科学 2017-05-17 Akkas Uddin Haque , Ashkan Nejadpak

Over the past few years, the use of camera-equipped robotic platforms for data collection and visually monitoring applications has exponentially grown. Cluttered construction sites with many objects (e.g., bricks, pipes, etc.) on the ground…

机器人学 · 计算机科学 2019-01-25 Khashayar Asadi , Rahul Jain , Ziqian Qin , Mingda Sun , Mojtaba Noghabaei , Jeremy Cole , Kevin Han , Edgar Lobaton

We propose a novel receding horizon planner for an autonomous surface vehicle (ASV) performing path planning in urban waterways. Feasible paths are found by repeatedly generating and searching a graph reflecting the obstacles observed in…

机器人学 · 计算机科学 2020-09-02 Tixiao Shan , Wei Wang , Brendan Englot , Carlo Ratti , Daniela Rus

Obstacle avoidance and path planning are essential for guiding unmanned ground vehicles (UGVs) through environments that are densely populated with dynamic obstacles. This paper develops a novel approach that combines tangentbased path…

机器人学 · 计算机科学 2025-11-12 Okan Arif Guvenkaya , Selim Ahmet Iz , Mustafa Unel

In this paper, we present an on-board vision-based approach for avoidance of moving obstacles in dynamic environments. Our approach relies on an efficient obstacle detection and tracking algorithm based on depth image pairs, which provides…

机器人学 · 计算机科学 2020-02-14 Jiahao Lin , Hai Zhu , Javier Alonso-Mora

This technical report presents the construction and analysis of polynomial navigation functions for motion planning in 3-D workspaces populated by spherical and cylindrical obstacles. The workspace is modeled as a bounded spherical region,…

机器人学 · 计算机科学 2026-01-15 Ro'i Lang , Elon Rimon

The safe and efficient operation of Autonomous Mobile Robots (AMRs) in complex environments, such as manufacturing, logistics, and agriculture, necessitates accurate multi-object tracking and predictive collision avoidance. This paper…

机器人学 · 计算机科学 2025-09-03 Bruk Gebregziabher , Hadush Hailu

In practical applications, autonomous quadrotors are still facing significant challenges, such as the detection and avoidance of very small and even dynamic obstacles (e.g., tree branches, power lines). In this paper, we propose a compact,…

机器人学 · 计算机科学 2021-03-02 Fanze Kong , Wei Xu , Fu Zhang

This paper presents a novel method to generate spatial constraints for motion planning in dynamic environments. Motion planning methods for autonomous driving and mobile robots typically need to rely on the spatial constraints imposed by a…

机器人学 · 计算机科学 2021-10-29 Han Hu , Peyman Yadmellat

With the incremental development of robotic platforms to automate the manual processes, path planning has become a critical domain with or without the knowledge of the indoor and outdoor environment. The algorithms can be intelligent or…

机器人学 · 计算机科学 2020-12-22 R. N. Somarathna

This paper proposes a 2-D autonomous exploration and mapping framework for LiDAR-based SLAM mobile robots, designed to address the major challenges on low-cost platforms, including process instability, map drift, and increased risks of…

机器人学 · 计算机科学 2025-11-18 Muhua Zhang , Lei Ma , Ying Wu , Kai Shen , Yongkui Sun , Henry Leung

Autonomous navigation in unknown environments is a fundamental challenge in robotics, particularly in coordinating ground and aerial robots to maximize exploration efficiency. This paper presents a novel approach that utilizes a…

Mobile robot navigation is typically regarded as a geometric problem, in which the robot's objective is to perceive the geometry of the environment in order to plan collision-free paths towards a desired goal. However, a purely geometric…

机器人学 · 计算机科学 2020-04-17 Gregory Kahn , Pieter Abbeel , Sergey Levine

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

It is challenging for a mobile robot to navigate through human crowds. Existing approaches usually assume that pedestrians follow a predefined collision avoidance strategy, like social force model (SFM) or optimal reciprocal collision…

机器人学 · 计算机科学 2021-09-07 Shunyi Yao1 , Guangda Chen , Quecheng Qiu , Jun Ma , Xiaoping Chen , Jianmin Ji

Autonomous navigation in unknown 3D environments is a key issue for intelligent transportation, while still being an open problem. Conventionally, navigation risk has been focused on mitigating collisions with obstacles, neglecting the…

机器人学 · 计算机科学 2024-03-06 Elie Randriamiarintsoa , Johann Laconte , Benoit Thuilot , Romuald Aufrère

Algorithms for motion planning in unknown environments are generally limited in their ability to reason about the structure of the unobserved environment. As such, current methods generally navigate unknown environments by relying on…

机器人学 · 计算机科学 2019-10-21 Amine Elhafsi , Boris Ivanovic , Lucas Janson , Marco Pavone

Safe control in unknown environments is a significant challenge in robotics. While Control Barrier Functions (CBFs) are widely used to guarantee system safety, they often assume known environments with predefined obstacles. The proposed…

机器人学 · 计算机科学 2024-09-16 Golnaz Raja , Teemu Mökkönen , Reza Ghabcheloo

We present a novel approach to perform probabilistic collision detection between a high-DOF robot and high-DOF obstacles in dynamic, uncertain environments. In dynamic environments with a high-DOF robot and moving obstacles, our approach…

机器人学 · 计算机科学 2016-07-19 Chonhyon Park , Jae Sung Park , Dinesh Manocha