Article
Robotics
Jixuan Zhi, Lap-Fai Yu, Jyh-Ming Lien
Summary: The environment plays a significant role in human-robot interactions, impacting safety and comfort for humans and effectiveness and efficiency for robots. Redesigning spaces can enhance collaboration and interactions between humans and robots.
IEEE ROBOTICS AND AUTOMATION LETTERS
(2021)
Article
Computer Science, Artificial Intelligence
Chinmaya Sahu, Dayal R. Parhi
Summary: In this paper, a novel hybridized navigational controller is proposed using the logic of both statistical technique and computational intelligence method for path planning of bipeds. The experimental results demonstrate that the controller can achieve smooth navigation of bipeds in cluttered environments.
Article
Computer Science, Theory & Methods
Luke Antonyshyn, Jefferson Silveira, Sidney Givigi, Joshua Marshall
Summary: With recent advances in mobile robotics, autonomous systems, and artificial intelligence, there is a growing expectation for robots to solve complex problems, particularly in multi-robot systems. Many recent works in the field of combined task and motion planning for multiple mobile robots have integrated task and motion planning to address these complex tasks. By categorizing works based on their underlying problem representations, the authors survey the recent contributions and propose a taxonomy for task and motion planning applicable to both multi-robot and single-robot systems.
ACM COMPUTING SURVEYS
(2023)
Article
Robotics
Aamodh Suresh, Angelique Taylor, Laurel D. D. Riek, Sonia Martinez
Summary: This research aims to understand human preferences and behaviors in risky and crowded environments, specifically in navigational settings. The study shows that individuals have diverse path preferences ranging from risky and urgent to safe and relaxed. It also reveals that self-assessed risk and time-urgency do not correlate with path preferences. Additionally, participants express a high interest in understanding robot intentions and decision-making through various modalities like speech, touchscreen, and gestures. These findings provide crucial insights for the design of explainable AI in robots deployed in risky and crowded environments.
IEEE ROBOTICS AND AUTOMATION LETTERS
(2023)
Article
Robotics
Tuan Dam, Georgia Chalvatzaki, Jan Peters, Joni Pajarinen
Summary: Path planning is an important algorithmic approach for designing robot behaviors. This study introduces Monte-Carlo Path Planning (MCPP), a new algorithm based on the Monte-Carlo tree search (MCTS) algorithm, which can find optimal and feasible paths in both fully observable and partially observable environments. Experimental evaluations demonstrate the superiority of MCPP in partially observable scenarios.
IEEE ROBOTICS AND AUTOMATION LETTERS
(2022)
Article
Automation & Control Systems
Shuai Liu, Pengcheng Liu
Summary: This paper optimizes sampling-based motion planning algorithms to find the most suitable motion planner for different scenes and queries. STOMP performs well in low-complexity scenes, but the optimization performance in more complex scenes may not be as good as the original algorithm.
INTERNATIONAL JOURNAL OF ADVANCED MANUFACTURING TECHNOLOGY
(2022)
Article
Chemistry, Multidisciplinary
Long Li, Zhongqu Xie, Xiang Luo, Juanjuan Li
Summary: This study focused on the impact of gait pattern generation on the walking quality of biped robots, particularly comparing the energy efficiency of maintaining a vertical torso versus having torso pitch motion during walking. Results showed that torso pitch motion saves over 12% energy compared to maintaining a vertical torso, with the main energy-saving factor being the reduction of energy consumption of the swing knee in the double support phase.
APPLIED SCIENCES-BASEL
(2021)
Article
Engineering, Mechanical
Yongming Bian, Jie Shao, Jixiang Yang, Ao Liang
Summary: The paper proposes a method of hip and knee joints coordination control planning based on virtual force, which optimizes motion posture and reduces the burden on the knee joint. By establishing the kinematic model and equations of the biped robot, the method is implemented and validated through simulation data.
JOURNAL OF MECHANICAL SCIENCE AND TECHNOLOGY
(2021)
Article
Computer Science, Artificial Intelligence
Venkatraman Renganathan, Sleiman Safaoui, Aadi Kothari, Benjamin Gravell, Iman Shames, Tyler Summers
Summary: Robust autonomy stacks require tight integration of perception, motion planning, and control layers, but these layers often inadequately incorporate inherent perception and prediction uncertainties. To address this issue, we propose a framework that explicitly incorporates perception and prediction uncertainties into planning, mitigating the risks of constraint violation.
ARTIFICIAL INTELLIGENCE
(2023)
Article
Robotics
Parikshit Maini, Burak M. Gonultas, Volkan Isler
Summary: This paper presents the design of Cowbot, an autonomous weed mowing robot for maintaining cow pastures. The robot utilizes online planning to detect and mow weeds, optimizing path length based on real-time information. Field experiments show a 60% reduction in path length compared to traditional methods.
IEEE ROBOTICS AND AUTOMATION LETTERS
(2022)
Article
Engineering, Multidisciplinary
Peng Sun, Yunfei Gu, Haoyu Mao, Zhao Chen, Yanbiao Li
Summary: A kinematics analysis was conducted on a new hybrid mechanical leg for bipedal robots, and the walking gait of the robot on flat ground was planned. The kinematics of the hybrid mechanical leg were analyzed and relevant models were established. The robot walking was divided into three stages using the inverted pendulum model for gait planning, and the trajectories of the robot centroid motion and swinging leg joints were calculated. The feasibility of the mechanism design and gait planning was verified through dynamic simulation of the robot's virtual prototype. This study provides a reference for the gait planning of hybrid mechanical legged bipedal robots and lays the foundation for further research on the robots involved in this thesis.
Article
Automation & Control Systems
Wansong Liu, Xiao Liang, Minghui Zheng
Summary: This article presents a new method for generating task-constrained and collision-free motion for a collaborative robot operating in a dynamic environment involving human movement. The method takes into consideration the high degree of freedom of the corobot and the uncertainty nature of human motion to ensure efficient and safe collaboration.
IEEE-ASME TRANSACTIONS ON MECHATRONICS
(2023)
Article
Robotics
Teguh Santoso Lembono, Emmanuel Pignat, Julius Jankowski, Sylvain Calinon
Summary: The study introduces a method using generative adversarial network to learn the distribution of valid robot configurations under constraints, applied in inverse kinematics and motion planning with simulation validation.
IEEE ROBOTICS AND AUTOMATION LETTERS
(2021)
Article
Robotics
Aykut Isleyen, Nathan van de Wouw, Omur Arslan
Summary: This letter introduces a novel feedback motion planning framework that extends the applicability of low-order reference motion planners to high-order robot models using motion prediction and reference governors. Accurate motion prediction is crucial for closing the gap between high-level planning and low-level control.
IEEE ROBOTICS AND AUTOMATION LETTERS
(2022)
Article
Computer Science, Artificial Intelligence
Steve Macenski, Shrijit Singh, Francisco Martin, Jonatan Gines
Summary: The accelerated deployment of service robots has led to the development of various algorithm variations to better handle real-world conditions. Many local trajectory planning techniques have been successfully implemented in practical robot systems. However, the use of pure path tracking algorithms is still prevalent, with few variants considering variable linear velocities. This paper introduces a regulated variant of the Pure Pursuit algorithm that incorporates additional heuristics to adjust linear velocities, with a focus on safety in constrained and partially observable spaces commonly encountered by deployed robots.
Article
Automation & Control Systems
V. Janardhan, R. Prasanth Kumar
ROBOTICS AND AUTONOMOUS SYSTEMS
(2017)
Article
Automation & Control Systems
Jungwon Yoon, R. Prasanth Kumar, Abdullah Oezer
INTERNATIONAL JOURNAL OF CONTROL AUTOMATION AND SYSTEMS
(2011)
Article
Engineering, Mechanical
Christiand, Jungwon Yoon, Prasanth Kumar
JOURNAL OF MECHANICAL SCIENCE AND TECHNOLOGY
(2009)
Article
Acoustics
Abdullah Oezer, S. Eren Semercigil, R. Prasanth Kumar, Pulas Yowat
JOURNAL OF SOUND AND VIBRATION
(2013)
Article
Engineering, Marine
R. P. Kumar, C. S. Kumar, D. Sen, A. Dasgupta
Article
Robotics
R. Prasanth Kumar, Jungwon Yoon, Christiand, Gabsoon Kim
Article
Robotics
R. Prasanth Kumar, Abdullah Oezer, Gabsoon Kim, Jungwon Yoon
Article
Robotics
P. Murali Krishna, R. Prasanth Kumar
Article
Robotics
V Janardhan, Prasanth R. Kumar
Article
Engineering, Biomedical
Mangesh D. Ratolikar, Prasanth R. Kumar
Summary: This study presents optimized dimensions for a 5R planar parallel mechanism for quadruped robot locomotion. The optimization problem is solved using genetic algorithm to minimize the peak torque required for link displacement. After analyzing different working modes, the best mode is selected for the quadruped legs.
JOURNAL OF VIBROENGINEERING
(2022)
Proceedings Paper
Automation & Control Systems
Krishna Prakash Yadav, R. Prasanth Kumar
Summary: This paper presents a robust biped dynamic walker based on the virtual slope, where a feedback controller is developed based on the relation between step length and slope. The stability of the walker is analyzed using numerical solutions and methods such as Poincare maps and basin of attraction plots.
2021 THE 9TH INTERNATIONAL CONFERENCE ON CONTROL, MECHATRONICS AND AUTOMATION (ICCMA 2021)
(2021)
Proceedings Paper
Automation & Control Systems
A. Poorna Hima Vamsi, Mangesh D. Ratolikar, R. Prasanth Kumar
Summary: Underactuated systems are common in robotics and legged locomotion. Balancing an unactuated pendulum on an actuated cart is a classic example used for designing and testing control algorithms. This paper introduces a model environment for a pendulum on a vertically moving cart and trains a neural network controller using reinforcement learning to balance it without exceeding displacement limits. Results show that the neural network controllers can successfully swing up and balance the pendulum for both continuous and discrete force control inputs.
2021 IEEE INTERNATIONAL CONFERENCE ON ROBOTICS AND BIOMIMETICS (IEEE-ROBIO 2021)
(2021)
Proceedings Paper
Engineering, Electrical & Electronic
Aditya Sripada, Janardhan Vistapalli, R. Prasanth Kumar
2018 IEEE INTERNATIONAL CONFERENCE ON ROBOTICS AND BIOMIMETICS (ROBIO)
(2018)
Article
Acoustics
Rahul Dixit, R. Prasanth Kumar
ADVANCES IN ACOUSTICS AND VIBRATION
(2016)