Safe Motion Planning

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21 papers in the last four weeks, up 110% on the four weeks before. 0.2% of all new papers.

Jul 13Week of Sep 28

Latest papers 152

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Path Planning Using Deep Deterministic Policy Gradient: A Reinforcement Learning Approach
Jun 5, 2026Qiang Le, Yaguang Yang, Isaac E. WeintraubConstrained Motion Planning Safe Motion Planning
Hampton University, Hampton, VA 23669, USA · Air Force Research Laboratory, Wright-Patterson AFB, OH 45433, USA
Distribution-Free Risk-Aware Planning and Control Under Uncertainty Using Conformal Spectral Risk Control
Jun 2, 2026Junsik Eom, Tulga ErsalModel Predictive Control Conformal Risk Control
Department of Mechanical Engineering, University of Michigan, Ann Arbor, MI 48109
Multi-Agent Next-Best-View Optimization for Risk-Averse Planning
Jun 2, 2026Amirhossein Mollaei Khass, Vivek Pandey, Guangyi Liu +3Next-Best-View Planning Multi-Robot Exploration
Department of Mechanical Engineering and Mechanics, Lehigh University · Amazon Robotics
Neural Navigation Functions for Zero-Shot Generalizable Motion Planning
Jun 2, 2026Benjamin D. Shaffer, Pei-An Hsieh, Brooks Kinch +2Zero-Shot Learning Roadmap-Based Motion Planning
University of Pennsylvania, United States 1Department of Mechanical Engineering and Applied Mechanics · University of Pennsylvania, United States 2Department of Electrical and Systems Engineering
Bridging Predictive Uncertainty and Safe Action: Sample-Conditioned Differentiable Planning for Autonomous Driving
Jun 2, 2026Chengzhen Meng, Pei Liu, Zhiyu Huang +2Diffusion-Based Planning Safe Motion Planning
Robotics and Autonomous Systems Thrust, The Hong Kong University of Science and Technology, Guangzhou, China, and also the Department of Electronic and Computer Engineering, The Hong Kong University of Science and Technology, Hong Kong SAR, China · Department of Civil and Environmental Engineering, University of California, Los Angeles, CA 90095 USA · School of Mechanical and Aerospace Engineering, Nanyang Technological University, Singapore 639798
DriveAnchor: Progressive Anchor-based Flow Learning for Autonomous Driving Planning
May 30, 2026Limin Yan, Haoyun Tang, Yutao Qiu +2Flow Matching Trajectory Generation
Meituan Autonomous Driving · Xi’an Jiaotong University · Beijing Institute of Technology
Predicted-Flow Control Barrier Functions for Real-Time Safe Optimal Control
May 29, 2026Amirsaeid Safari, Jesse B. HoaggSafety-Critical Control Control Barrier Functions
Department of Mechanical and Aerospace Engineering, University of Kentucky, Lexington, KY 40506 USA
LiftNav: Path Planning via Semantic Lifting in TSDF-Guided Gaussian Splatting
May 29, 2026Hannah Schieber, Dominik Frischmann, Victor Schaack +2Trajectory Optimization Robot Navigation
Technical University of Munich, Human-Centered Computing and Extended Reality Lab, TUM University Hospital, Clinic for Orthopedics and Sports Orthopedics, Munich Institute of Robotics and Machine Intelligence (MIRMI) · Technical University of Munich, Learning Systems and Robotics Lab, Munich Institute of Robotics and Machine Intelligence (MIRMI)
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
VLM-Based Advanced Rider Assistance System for Motorcycle Safety
May 27, 2026Mohamed Elnoor, Francesca Baldini, Ananya Trivedi +6Safe Motion Planning Autonomous Driving Planning
Honda Research Institute, USA, San Jose, CA, 95134. · University of Maryland, College Park, MD 20742, USA. · Northeastern University, Boston, MA 02115, USA.
Simulation-Informed Diffusion for Decentralized Multi-robot Motion Planning
May 26, 2026Jinhao Liang, Sven Koenig, Ferdinando FiorettoMulti-Robot Motion Planning Constrained Motion Planning
University of Virginia · University of California, Irvine
Provably Safe Motion Planning Under Unknown Disturbances
May 26, 2026Ibon Gracia, Qi Heng Ho, Luca Laurenti +1Constrained Motion Planning Robot Safety
Department of Aerospace Engineering Sciences at the University of Colorado Boulder, CO, USA · Department of Aerospace and Ocean Engineering, Virginia Tech, VA, USA · Delft University of Technology, the Netherlands, and the Italian Institute of Artificial Intelligence, Italy
Verified Task-Space Motion Planning Under Joint-Space Constraints
May 21, 2026Hanjiang Hu, Changliu Liu, Yebin WangConstrained Motion Planning Safe Motion Planning
Robotics Institute, Carnegie Mellon University · Mitsubishi Electric Research Laboratories (MERL), Cambridge, MA 02139, USA
MC-Risk: Multi-Component Risk Fields for Risk Identification and Motion Planning
May 20, 2026Maximilian Link, Yingjie Xu, Yingbai Hu +1Safe Motion Planning Autonomous Driving Planning
Technical University of Munich · Munich, Germany · The Chinese University of Hong Kong +3
Conflict-Aware Active Perception and Control in 3D Gaussian Splatting Fields via Control Barrier Functions
May 19, 2026Amirhossein Mollaei Khass, Athanasios Cosse, Vivek Pandey +1Active Perception Next-Best-View Planning
Department of Mechanical Engineering and Mechanics, Lehigh University, Bethlehem, PA, 18015, USA
KIO-planner: Attention-Guided Single-Stage Motion Planning with Dual Mapping for UAV Navigation
May 19, 2026Dexing Yao, Haochen Li, Junhao Wei +11Constrained Motion Planning Kinodynamic Motion Planning
Faculty of Applied Sciences Macao Polytechnic University Macao, China · College of Animal Science and Technology Zhongkai University of Agriculture and Engineering Guangzhou, China · School of Economics and Management South China Normal University Macao, China +1
Probabilistic Recursively Feasible Motion Planning Under Uncertain Environments
May 18, 2026Hyeontae Sung, Hyeongchan Ham, Junyoung Park +2Constrained Motion Planning Model Predictive Control
School of Electrical Engineering, Korea Advanced Institute of Science and Technology, Daejeon, Korea · The SYCAMORE Lab, ´Ecole Polytechnique F´ed´erale de Lausanne (EPFL), Switzerland
DriveSafer: End-to-End Autonomous Driving with Safety Guidance
May 16, 2026Shounak Sural, Raj RajkumarEnd-to-End Autonomous Driving Diffusion-Based Planning
Carnegie Mellon University Pittsburgh, PA
Fast Expanding Safe Circular Regions for Efficient Local Path Planning
May 15, 2026Scott Fredriksson, Akshit Saradagi, George NikolakopoulosRobot Navigation Safe Motion Planning
Robotics and Artificial Intelligence Team Lule˚a University of Technology, Lule˚a, Sweden
Constrained MPC-Based Motion Planning for Morphing Quadrotors in Ultra-Narrow Passages under Limited Perception
May 15, 2026Harsh Modi, Xiao Liang, Minghui ZhengAerial Robotics Constrained Motion Planning
Department of Mechanical Engineering, Texas A&M University, College Station, TX 77843, USA · Department of Civil & Environmental Engineering, Texas A&M University, College Station, TX 77843, USA
Motion Planning for Autonomous Vehicles using Optimization over Graphs of Convex Sets
May 13, 2026Matheus Wagner, Antônio Augusto FröhlichTrajectory Optimization Autonomous Driving
Software/Hardware Integration Lab, Federal University of Santa Catarina, Florian´opolis, Brazil
Loiter UAV Reinsertion Guidance for Fixed-wing UAV Corridors
May 13, 2026Pradeep J, Kedarisetty Siddhardha, Ashwini RatnooCollision Avoidance UAV Navigation
B.Tech (Honours) Student, Department of Mechatronics Engineering · Assistant Professor, Department of Mechatronics Engineering · Professor, Department of Aerospace Engineering
TinySDP: Real Time Semidefinite Optimization for Certifiable and Agile Edge Robotics
May 13, 2026Ishaan Mahajan, Jon Arrizabalaga, Andrea Grillo +4Semidefinite Programming Constrained Motion Planning
Columbia University, USA · Massachusetts Institute of Technology, USA · ´Ecole Polytechnique F´ed´erale de Lausanne (EPFL), Switzerland +1
Safe Aerial 3D Path Planning for Autonomous UAVs using Magnetic Potential Fields
May 11, 2026Haechan Mark Bong, Giovanni BeltrameUAV Navigation Convolutional Autoencoder
Department of Computer Engineering and Software Engineering, Polytechnique Montréal, QC., Canada. · MILA, QC., Canada.
Decentralized Contingency MPC based on Safe Sets for Nonlinear Multi-agent Collision Avoidance
May 11, 2026Max Studt, Georg SchildbachCollision Avoidance Model Predictive Control
Institute for Electrical Engineering in Medicine, University of Luebeck, Luebeck, Germany
MORPH-U: Multi-Objective Resilient Motion Planning for V2X-Enabled Autonomous Driving in High-Uncertainty Environments via Simulation
May 8, 2026Shih-Yu LaiV2X Communication Safe Motion Planning
National Taiwan University Taipei, Taiwan
Feasibility-aware Hybrid Control for Motion Planning under Signal Temporal Logics
May 5, 2026Panagiotis Rousseas, Dimos V. DimarogonasConstrained Motion Planning Signal Temporal Logic
∗KTH Royal Institute of Technology, School of Electrical Engineering and Computer Science, Division of Decision and Control Systems
Semantic Risk-Aware Heuristic Planning for Robotic Navigation in Dynamic Environments: An LLM-Inspired Approach
May 4, 2026Hamza Ahmed Durrani, Rafay Suleman DurraniSafe Robot Navigation Robot Safety
Sejong University, Computer Science Engineering · Technische Universität Ilmenau, Computer Engineering
SAGA: A Robust Self-Attention and Goal-Aware Anchor-based Planner for Safe UAV Autonomous Navigation
May 4, 2026Junhao Wei, Yanxiao Li, Dexing Yao +9Self-Attention Roadmap-Based Motion Planning
Faculty of Applied Sciences, Macao Polytechnic University, Macao 999078, China · Pazhou Lab (Huangpu), Guangzhou 510555, China · College of Animal Science and Technology, Zhongkai University of Agriculture and Engineering, Guangzhou 510225, China +3
A Principled Approach for Creating High-fidelity Synthetic Demonstrations for Imitation Learning
May 2, 2026Moniruzzaman Akash, Momotaz BegumSynthetic Data Generation for Robotics Robot Motion Generation
Department of Computer Science · University of New Hampshire

Jun 5, 2026, cs.ROJ/K move · Enter open · S save

Path Planning Using Deep Deterministic Policy Gradient: A Reinforcement Learning Approach

Qiang Le, Yaguang Yang, Isaac E. Weintraub

Hampton University, Hampton, VA 23669, USA · Air Force Research Laboratory, Wright-Patterson AFB, OH 45433, USA

Constrained Motion Planning Safe Motion Planning Autonomous Driving Planning RL for Autonomous Driving Deep Deterministic Policy Gradient

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Path-planning for autonomous vehicles in threat-laden environments is a fundamental challenge because the problem is nonlinear and nonconvex even in simplest scenarios. While traditional optimal control methods can be used to find ideal paths, the computational time is often too slow for real-time decision-making. To solve this challenge, we propose a method based on Deep Deterministic Policy Gradient (DDPG) and model the threat as possibly multiple circular 'no-go' zones. A mission is regarded as a failure if the vehicle enters this restricted zone at any time or does not reach a neighborhood of the destination. The DDPG agent is trained through trial and error in a simulated environment, learning a direct mapping from its current state (position and heading) to a series of feasible actions that guide the agent to safely reach its destination. The reword function has three parts: (a) an attractive field centered at the final destination, (b) some repulsive fields centered at the origins of circular obstacles, and (c) a penalty of control energy consumption (the magnitude of heading change) that indirectly in favor for straight path. The DDPG trains the agent using these incentives to find the largest possible set of starting points wherein a safe path to the destination is guaranteed. This provides critical information for mission planning, showing beforehand whether a task is achievable from a given starting point, assisting pre-mission planning activities. The approach is validated in simulation. A comparison between the DDPG method and a traditional optimal control (pseudo-spectral) method is carried out. The results show that the learning-based agent produces effective paths while being significantly faster, making it a better fit for real-time applications.

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