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Geometric motion planning offers effective and interpretable gait analysis and optimization tools for locomoting systems. However, due to the curse of dimensionality in coordinate optimization, a key component of geometric motion planning,…

Robotics · Computer Science 2024-03-08 Yanhao Yang , Capprin Bass , Ross L. Hatton

State-of-the-art approaches to legged locomotion are widely dependent on the use of models like the linear inverted pendulum (LIP) and the spring-loaded inverted pendulum (SLIP), popular because their simplicity enables a wide array of…

Robotics · Computer Science 2019-09-24 Yu-Ming Chen , Michael Posa

This paper introduces a new algorithm for trajectory optimization, Decoupled Reduced-space and Adaptive Feasibility-repair Trajectory Optimization (DRAFTO). It first constructs a constrained objective that accounts for smoothness, safety,…

Robotics · Computer Science 2026-03-13 Yichang Feng , Xiao Liang , Minghui Zheng

Computing stabilizing and optimal control actions for legged locomotion in real time is difficult due to the nonlinear, hybrid, and high dimensional nature of these robots. The hybrid nature of the system introduces a combination of…

Robotics · Computer Science 2025-08-26 Zachary Olkin , Aaron D. Ames

Simplified models are useful to increase the computational efficiency of a motion planning algorithm, but their lack of accuracy have to be managed. We propose two feasibility constraints to be included in a Single Rigid Body Dynamicsbased…

Contact planning is crucial in locomoting systems.Specifically, appropriate contact planning can enable versatile behaviors (e.g., sidewinding in limbless locomotors) and facilitate speed-dependent gait transitions (e.g., walk-trot-gallop…

In this paper we propose a method to improve the accuracy of trajectory optimization for dynamic robots with intermittent contact by using orthogonal collocation. Until recently, most trajectory optimization methods for systems with…

Robotics · Computer Science 2019-04-03 Amir Patel , Stacey Shield , Saif Kazi , Aaron M. Johnson , Lorenz T. Biegler

We present a versatile framework for the computational co-design of legged robots and dynamic maneuvers. Current state-of-the-art approaches are typically based on random sampling or concurrent optimization. We propose a novel bilevel…

Robotics · Computer Science 2022-07-18 Traiko Dinev , Carlos Mastalli , Vladimir Ivan , Steve Tonneau , Sethu Vijayakumar

Legged robots, specifically quadrupeds, are becoming increasingly attractive for industrial applications such as inspection. However, to leave the laboratory and to become useful to an end user requires reliability in harsh conditions. From…

Robotics · Computer Science 2019-08-13 David Wisth , Marco Camurri , Maurice Fallon

In this paper, we present an efficient Dynamic Programing framework for optimal planning and control of legged robots. First we formulate this problem as an optimal control problem for switched systems. Then we propose a multi--level…

Systems and Control · Computer Science 2018-01-31 Farbod Farshidian , Michael Neunert , Alexander W. Winkler , Gonzalo Rey , Jonas Buchli

The adaptability of soft robots makes them ideal candidates to maneuver through unstructured environments. However, locomotion challenges arise due to complexities in modeling the body mechanics, actuation, and robot-environment dynamics.…

To construct a robot that can walk as efficiently and steadily as humans or other legged animals, we develop an enhanced elitist-mutated ant colony optimization~(EACO) algorithm with genetic and crossover operators in real-time applications…

Neural and Evolutionary Computing · Computer Science 2020-10-12 Jingan Yang , Yang Peng

In this paper, we propose a novel approach on controlling wheel-legged quadrupedal robots using pose optimization and force control via quadratic programming (QP). Our method allows the robot to leverage the whole-body motion and the wheel…

Robotics · Computer Science 2022-03-11 Junheng Li , Junchao Ma , Quan Nguyen

Multi-robot systems offer enhanced capability over their monolithic counterparts, but they come at a cost of increased complexity in coordination. To reduce complexity and to make the problem tractable, multi-robot motion planning (MRMP)…

Robotics · Computer Science 2021-11-30 Changrak Choi , Muhammad Adil , Amir Rahmani , Ramtin Madani

Tethered robots play a pivotal role in specialized environments such as disaster response and underground exploration, where their stable power supply and reliable communication offer unparalleled advantages. However, their motion planning…

Robotics · Computer Science 2025-07-17 Jinyuan Liu , Minglei Fu , Ling Shi , Chenguang Yang , Wenan Zhang

Serially connected robots are promising candidates for performing tasks in confined spaces such as search-and-rescue in large-scale disasters. Such robots are typically limbless, and we hypothesize that the addition of limbs could improve…

Robotic locomotion often relies on sequenced gaits to efficiently convert control input into desired motion. Despite extensive studies on gait optimization, achieving smooth and efficient gait transitions remains challenging. In this paper,…

Robotics · Computer Science 2024-09-17 Jinwoo Choi , Ross L. Hatton

Legged robots have the ability to adapt their walking posture to navigate confined spaces due to their high degrees of freedom. However, this has not been exploited in most common multilegged platforms. This paper presents a deformable…

We propose a method to generate actuation plans for a reduced order, dynamic model of bipedal running. This method explicitly enforces robustness to ground uncertainty. The plan generated is not a fixed body trajectory that is aggressively…

Robotics · Computer Science 2020-01-30 Kevin Green , Ross L. Hatton , Jonathan Hurst

In this paper, we present a concurrent and scalable trajectory optimization method to improve the quality of robot-assisted manufacturing. Our method simultaneously optimizes tool orientations, kinematic redundancy, and waypoint timing on…

Robotics · Computer Science 2024-12-23 Yongxue Chen , Tianyu Zhang , Yuming Huang , Tao Liu , Charlie C. L. Wang