Dynamic Obstacle Avoidance

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12 papers in the last four weeks, against 1 the four weeks before. 0.1% of all new papers.

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Time-to-Collision Based Dynamic Obstacle Avoidance Using Pretrained Vision Models for Robots in Unstructured Environments
Jul 8, 2026Erik Jagnandan, Mulugeta Haile, Gregory Barber +1Collision Avoidance Dynamic Obstacle Avoidance
GRASP Laboratory, University of Pennsylvania, PA · U.S. Army Research Laboratory, Aberdeen Proving Ground, MD
Clustering-Embedded Model Predictive Path Integral Control: Avoiding Averaging-Induced Failure and Enabling Efficient Cluster Selection for Dynamic Obstacles
Jul 7, 2026Zidong Liu, Kaixin Chang, Xu ChenMPPI Control Dynamic Obstacle Avoidance
Department of Mechanical Engineering, University of Washington, Seattle, WA, USA.
Path planning for unmanned naval surface vehicles
Jul 2, 2026Daniel G. SchwartzRobot Navigation Dynamic Obstacle Avoidance
Department of Computer Science, Florida State University, Tallahassee, FL 32306, USA
From Prediction Uncertainty to Conformalized Distance Fields for Safe Motion Planning
Jul 1, 2026Jaeuk Shin, Yoonseok Ra, Insoon YangConstrained Motion Planning Conformal Prediction
Department of Electrical and Computer Engineering, ASRI, Seoul National University, Seoul 08826, South Korea
Optimal any-angle path planning in static and dynamic environments
Jun 30, 2026Yiyuan Zou, Clark BorstGraph-Based Planning Motion Planning
Control and Simulation, Faculty of Aerospace Engineering, Delft University of Technology, Delft, The Netherlands
RoboNav-Arm: Agentic AI-Driven Navigation and Obstacle Avoidance for Robotic Manipulator in Cluttered Environments
Jun 25, 2026Aachal Sharma, Narendra Kumar DharRobotics Robotic Manipulation
Centre for Artificial Intelligence and Robotics (CAIR), Indian Institute of Technology Mandi, Himachal Pradesh 175005, India.
Event-Adaptive Motion Planning with Distilled Vision-Language Model in Safety-Critical Situations
Jun 24, 2026Zhenwei Huang, Changsheng You, Shuai Wang +3Safe Robot Navigation Model Predictive Control
Southern University of Science and Technology · Shenzhen Institute of Advanced Technology, Chinese Academy of Sciences · Manifold Tech Limited
LP-NavOA: Integrated Local Navigation and Obstacle Avoidance for Humanoid Robots under Limited Perception
Jun 22, 2026Yukun Luo, Jianjun Ma, Yuyao Min +3Humanoid Robot Locomotion Robot Navigation
College of Intelligence Science and Technology National University of Defense Technology Changsha, China
Two-Phase Bilevel Search for the Moving-Target Traveling Salesman Problem with Moving Obstacles
Jun 17, 2026Allen George Philip, Anoop Bhat, Sivakumar Rathinam +1Traveling Salesperson Problem Combinatorial Optimization
Mechanical Engineering, Texas A&M University, College Station, TX 77843-3123 · Robotics Institute, Carnegie Mellon University, 5000 Forbes Ave., Pittsburgh, PA 15213, USA · Mechanical Engineering, and Computer Science and Engineering, Texas A&M University, College Station, TX 77843-3123
VEGA: Learning Navigation VLAs from In-the-Wild Egocentric Video with Geometric Trajectory Supervision
Jun 16, 2026Gershom Seneviratne, Yohan Abeysinghe, Jianyu An +2Robot Policy Learning Trajectory Generation
University of Maryland, College Park
SidewalkBench: Benchmarking Visual Navigation on Urban Sidewalks
Jun 15, 2026Zhizheng Liu, Honglin He, Vivek Alumootil +4Visual Navigation Long-Horizon Agent Evaluation
University of California, Los Angeles · Coco Robotics
JPPD: Joint Prediction_Planning Diffusion with Differentiable Safety Guidance for Dynamic Obstacle Avoidance in Intelligent Transportation Systems
Jun 14, 2026Jiahao Wu, Shengwen YuTrajectory Generation Diffusion-Based Planning
Department of Engineering, Mechanical Engineering, The University of Hong Kong, Hong Kong
Your Model Already Knows: Attention-Guided Safety Filter for Vision-Language-Action Models
Jun 8, 2026Seongbin Park, Fan Zhang, Baharan Mirzasoleiman +2Robot Safety Safety Filtering
University of California Los Angeles United States
Safe Polytope-in-Polytope Motion Planning and Control with Control Barrier Functions
Jun 8, 2026Alejandro Gonzalez-Garcia, Dries Dirckx, Jan Swevers +1Constrained Motion Planning Control Barrier Functions
Bayesian Optimization for Learning Nonlinear MPC in Autonomous Agent Navigation
Jun 8, 2026Lorenzo Ortolani, Gabriel Voss, Gabriele Beltrami +2Constrained Motion Planning Nonlinear MPC
Talos Robotics AI Milan, Italy
Learning Predictive Control with Deep Koopman Operators for Autonomous Vehicle Motion Planning
Jun 6, 2026Xinglong Zhang, Yongqian Xiao, Haotian Cao +3Model Predictive Control Autonomous Driving Planning
MinNav: Minimalist Navigation Using Optical Flow For Active Tiny Aerial Robots
Jun 5, 2026Aniket Patil, Mandeep Singh, Uday Girish Maradana +1Optical Flow Visual Navigation
1Perception and Autonomous Robotics (PeAR) Group, Robotics Engineering Department, Worcester Polytechnic Institute.
On dynamic multi-agent pathfinding methods: review, simulations and modifications
Jun 2, 2026Gabriel Fejziaj, Salama Hassona, Wieslaw MarszalekMotion Planning Dynamic Obstacle Avoidance
Department of Computer Science, Opole University of Technology, Opole, 45-758, Poland.
Motion Planning in Dynamic Environments: A Survey from Classical to Modern Methods
Jun 1, 2026Zongyuan Shen, Yaming Ou, Shalabh Gupta +6Model Predictive Control Motion Planning
College of Information Science and Technology, Jinan University, Guangzhou 510632, China. · School of Artificial Intelligence, University of Chinese Academy of Sciences, Beijing 100049, China. · Department of Electrical and Computer Engineering, University of Connecticut, Storrs, CT 06269, USA. +2
DeepIPCv3: Event-Aware Multi-Modal Sensor Fusion for Sudden Pedestrian Crossing Avoidance
May 31, 2026Oskar Natan, Andi Dharmawan, Aufaclav Zatu Kusuma Frisky +2Collision Avoidance Camera-LiDAR Fusion
Department of Computer Science and Electronics, Universitas Gadjah Mada, Yogyakarta 55281, Indonesia
Tether-Aware Dynamic Collision Avoidance for USV-HROV Systems
May 31, 2026Yang Gu, Ziyang Hong, Xuanlin Chen +4Collision Avoidance Multi-Robot Coordination
Robust Integrated Planning and Control for Quadrotors in Dynamic Environments via NMPC with CBF Penalties
May 31, 2026Zeinab Shayan, Mohammadreza Izadi, Reza FaieghiNonlinear MPC Control Barrier Functions
Autonomous Vehicles Laboratory, Department of Aerospace Engineering, Toronto Metropolitan University, Toronto, Canada.
OSCAR: Obstacle Survival Curves for Adaptive Robot Navigation
May 31, 2026Hshmat Sahak, Aoran Jiao, Nicholas Rhinehart +1Robot Navigation Graph-Based Planning
University of Toronto, Canada
A study on a Real-Time VR-Based Teleoperation Framework for Manipulator in Dynamic Environment
May 29, 2026InGyu Choi, GeonYeong Go, SunWoo Ahn +2Robot Teleoperation Dynamic Obstacle Avoidance
Department of Robotics, Hanyang University · Department of Smart Construction Engineering, Hanyang University · Department of Interdisciplinary Robot Engineering Systems, Hanyang University +1
Chance-Constrained MPPI under State and Dynamic Object Prediction Uncertainty and the Evaluation of Collision Risk Calibration
May 27, 2026Benjamin Serfling, Konrad Doll, Kati Radkhah-LensSafe Motion Planning Motion Planning
University of Applied Sciences Aschaffenburg, Aschaffenburg, Germany
RCSP: Risk-Sensitive Conjectural Scenario Planning for Safe Dynamic Robot Navigation
May 25, 2026Zhengye Han, Quanyan ZhuSafe Robot Navigation Robot Safety
Department of Electrical and Computer Engineering New York University
Vision-Guided Outdoor Flight and Obstacle Evasion via Reinforcement Learning
May 23, 2026Shiladitya Dutta, Aayush Gupta, Varun Saran +1RL for Robotics UAV Navigation
College of Engineering, Department of Electrical Engineering and Computer Science, University of California Berkeley
Pedestrian-Aware LLM-Driven Behavioral Planning for Autonomous Vehicles
May 16, 2026Aidana Baimbetova, Haruki Yonekura, Hamada Rizk +1Pedestrian Intention Prediction Autonomous Driving Planning
1The University of Osaka, Japan · 2RIKEN Center for Computational Science, Japan · 3Tanta University, Egypt
Reactive Robot-Centric Safety for Autonomous Navigation in Constrained and Dynamic Environments
May 15, 2026Viswa Narayanan Sankaranarayanan, Vignesh K. Viswanathan, Akshit Saradagi +2Safety-Critical Control Control Barrier Functions
Robotics and Artificial Intelligence Group of the Department of Computer Science, Electrical and Space Engineering at Luleå University of Technology, Sweden.
Neural Distance-Guided Path Integral Control for Tractor-Trailer Navigation
May 11, 2026Peng Wei, Chen Peng, Stavros VougioukasRobot Navigation MPPI Control
Department of Biological and Agricultural Engineering, University of California Davis, Davis, CA 95616 USA · ZJU-Hangzhou Global Scientific and Technological Innovation Center, Zhejiang University, Hangzhou, 311215, China

Jul 8, 2026, cs.ROJ/K move · Enter open · S save

Time-to-Collision Based Dynamic Obstacle Avoidance Using Pretrained Vision Models for Robots in Unstructured Environments

Erik Jagnandan, Mulugeta Haile, Gregory Barber, Pratik Chaudhari

GRASP Laboratory, University of Pennsylvania, PA · U.S. Army Research Laboratory, Aberdeen Proving Ground, MD

Collision Avoidance Dynamic Obstacle Avoidance Robot Collision Avoidance

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Dynamic obstacle avoidance in unstructured outdoor environments remains a critical challenge for autonomous mobile robots, particularly when large-scale robot-specific training data and simulation-based policies are impractical. We present a data-efficient, interpretable method for vision-based dynamic obstacle avoidance that operates entirely on real-world data, avoiding the sim-to-real transfer problem inherent in simulation-trained policies. Our approach leverages UniDepth, a large pretrained monocular depth estimation model, to produce dense depth maps from RGB video without requiring stereo cameras or LiDAR at inference time. Dynamic obstacle avoidance is achieved by extending the SuperPoint and SuperGlue feature correspondence pipeline to track keypoints across long frame sequences, projecting their 2D pixel-space positions into 3D using camera intrinsics and predicted depth, running bundle adjustment initialized from these 3D keypoints, and computing per-keypoint time-to-collision (TTC). A 2D motion primitive in the ground plane is then selected to move the robot away from the closest point of approach of the minimum-TTC keypoint. Evaluated on real-world data from the M3ED dataset, our pipeline achieves a precision of 0.49 and a recall of 0.38 in identifying frames with a ground truth TTC below 1 second, and correctly generates the evasive motion direction in 84% of true positive detections. Crucially, it detects at least one frame with TTC less than 1 second for 20 out of 22 unique physical obstacles present in our test sequences. Unlike end-to-end learned methods that demand thousands of hours of robot-specific training data, our approach eliminates model training entirely, requiring only 74 seconds of data for hyperparameter tuning. This demonstrates exceptional data efficiency while preserving interpretable and generalizable behavior across diverse obstacle types.

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