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Reactive trajectory optimization for robotics presents formidable challenges, demanding the rapid generation of purposeful robot motion in complex and swiftly changing dynamic environments. While much existing research predominantly…

Robotics · Computer Science 2023-10-04 Apan Dastider , Hao Fang , Mingjie Lin

Navigating an arbitrary-shaped ground robot safely in cluttered environments remains a challenging problem. The existing trajectory planners that account for the robot's physical geometry severely suffer from the intractable runtime. To…

Robotics · Computer Science 2026-01-07 Yisheng Li , Longji Yin , Yixi Cai , Jianheng Liu , Fangcheng Zhu , Mingpu Ma , Siqi Liang , Haotian Li , Fu Zhang

Iterative trajectory optimization techniques for non-linear dynamical systems are among the most powerful and sample-efficient methods of model-based reinforcement learning and approximate optimal control. By leveraging time-variant local…

Systems and Control · Electrical Eng. & Systems 2019-08-01 Onur Celik , Hany Abdulsamad , Jan Peters

Recent papers have shown optimally-competitive on-line strategies for a robot traveling from a point $s$ to a point $t$ in certain unknown geometric environments. We consider the question: Having gained some partial information about the…

Combinatorics · Mathematics 2016-09-06 Avrim Blum , Prasad Chalasani

In this paper, a novel real-time acceleration-continuous path-constrained trajectory planning algorithm is proposed with an appealing built-in tradability mechanism between cruise motion and time-optimal motion. Different from existing…

Robotics · Computer Science 2018-12-11 Peiyao Shen , Xuebo Zhang , Yongchun Fang

The problem of maneuvering a vehicle through a race course in minimum time requires computation of both longitudinal (brake and throttle) and lateral (steering wheel) control inputs. Unfortunately, solving the resulting nonlinear optimal…

Robotics · Computer Science 2019-02-05 Nitin R. Kapania , John Subosits , J Christian Gerdes

We consider online convex optimization with time-varying stage costs and additional switching costs. Since the switching costs introduce coupling across all stages, multi-step-ahead (long-term) predictions are incorporated to improve the…

Machine Learning · Computer Science 2020-11-26 Yingying Li , Na Li

This paper considers the problem of online trajectory design under time-varying environments. We formulate the general trajectory optimization problem within the framework of time-varying constrained convex optimization and proposed a novel…

Optimization and Control · Mathematics 2020-01-09 Mohan Krishna Nutalapati , Amrit Singh Bedi , Ketan Rajawat , Marceau Coupechoux

This paper presents a novel method for efficiently solving a trajectory planning problem for swarm robotics in cluttered environments. Recent research has demonstrated high success rates in real-time local trajectory planning for swarm…

Robotics · Computer Science 2023-11-08 Pengda Mao , Rao Fu , Quan Quan

Trajectory retiming is the task of computing a feasible time parameterization to traverse a path. It is commonly used in the decoupled approach to trajectory optimization whereby a path is first found, then a retiming algorithm computes a…

Robotics · Computer Science 2023-09-20 Gerry Chen , Frank Dellaert , Seth Hutchinson

The Team Orienteering Problem (TOP) generalizes many real-world multi-robot scheduling and routing tasks that occur in autonomous mobility, aerial logistics, and surveillance applications. While many flavors of the TOP exist for planning in…

Robotics · Computer Science 2025-10-29 Malintha Fernando , Petter Ögren , Silun Zhang

This paper presents a trajectory generation method for contact-constrained robotic systems such as manipulators and legged robots. Contact-constrained systems are affected by the interaction forces between the robot and the environment. In…

Robotics · Computer Science 2018-09-28 Jaemin Lee , Efstathios Bakolas , Luis Sentis

Control design for robotic systems is complex and often requires solving an optimization to follow a trajectory accurately. Online optimization approaches like Model Predictive Control (MPC) have been shown to achieve great tracking…

Sampling-based motion-planning algorithms typically rely on nearest-neighbor (NN) queries when constructing a roadmap. Recent results suggest that in various settings NN queries may be the computational bottleneck of such algorithms.…

Robotics · Computer Science 2014-09-30 Michal Kleinbort , Oren Salzman , Dan Halperin

Real-time constraint satisfaction for robots can be quite challenging due to the high computational complexity that arises when accounting for the system dynamics and environmental interactions, often requiring simplification in modelling…

Robotics · Computer Science 2021-05-24 Pravin Dangol , Alireza Ramezani

Trajectory data mining is crucial for smart city management. However, collecting large-scale trajectory datasets is challenging due to factors such as commercial conflicts and privacy regulations. Therefore, we urgently need trajectory…

Machine Learning · Computer Science 2025-02-04 Jingyuan Wang , Yujing Lin , Yudong Li

Legged robot locomotion requires the planning of stable reference trajectories, especially while traversing uneven terrain. The proposed trajectory optimization framework is capable of generating dynamically stable base and footstep…

Robotics · Computer Science 2021-03-24 Oguzhan Cebe , Carlo Tiseo , Guiyang Xin , Hsiu-chin Lin , Joshua Smith , Michael Mistry

In many mobile robotics scenarios, such as drone racing, the goal is to generate a trajectory that passes through multiple waypoints in minimal time. This problem is referred to as time-optimal planning. State-of-the-art approaches either…

Robotics · Computer Science 2020-08-04 Philipp Foehn , Davide Scaramuzza

In this work, we address the problem of computation time for trajectory generation in quadrotors. Most trajectory generation methods for waypoint navigation of quadrotors, for example minimum snap/jerk and minimum-time, are structured as…

Robotics · Computer Science 2023-03-29 Srinath Tankasala , Mitch Pryor

Offline reinforcement learning (RL) recently gains growing interests from RL researchers. However, the performance of offline RL suffers from the out-of-distribution problem, which can be corrected by feedback in online RL. Previous offline…

Machine Learning · Computer Science 2025-06-25 Shuncheng He , Hongchang Zhang , Jianzhun Shao , Yuhang Jiang , Xiangyang Ji