Article
Robotics
Nikhil Das, Michael C. Yip
Summary: This study proposes the use of Gaussian process regression and forward kinematics kernel to efficiently and accurately estimate collision distance for robot manipulators. The GP model with FK kernel achieves significantly faster distance evaluations compared to standard geometric techniques and more accurate evaluations than other regression models. A confidence-based hybrid model is also introduced, showing usefulness in different areas by switching between model-based predictions and sensor-based approaches.
IEEE ROBOTICS AND AUTOMATION LETTERS
(2021)
Article
Robotics
Zhuoqi Zheng, Chao Cao, Jia Pan
Summary: This paper proposes a hierarchical approach for effective exploration and collision-free navigation of mobile robots in crowded environments. By combining local and global information and utilizing a reinforcement learning-based obstacle avoidance algorithm, the proposed method enables safe exploration in pedestrian crowds.
IEEE ROBOTICS AND AUTOMATION LETTERS
(2022)
Article
Agriculture, Multidisciplinary
Lei Ye, Jieli Duan, Zhou Yang, Xiangjun Zou, Mingyou Chen, Sheng Zhang
Summary: The study utilized the APSO algorithm and the AtBi-RRT algorithm for collision-free motion planning, effectively avoiding collisions between the picking robot and obstacles during picking process, and improving the success rate of picking.
COMPUTERS AND ELECTRONICS IN AGRICULTURE
(2021)
Article
Chemistry, Analytical
Panagiotis Vlantis, Charalampos P. Bechlioulis, Kostas J. Kyriakopoulos
Summary: In this work, we propose a transformation algorithm that converts a static, compact, planar workspace of arbitrary connectedness and shape to a disk, simplifying the navigation problem. Our solution utilizes a fine representation of the workspace boundary obtained through SLAM. By combining this transformation with a workspace decomposition strategy, we achieved excellent performance in complex workspaces. Additionally, we provide a motion control scheme for non-holonomic robots commonly used in industrial applications, with easy parameter tuning and validated efficacy through extensive simulations and experiments.
Article
Engineering, Multidisciplinary
Wu HaoRan, Yu JingJun, Pan Jie, Pei Xu
Summary: This paper introduces a novel obstacle avoidance heuristic algorithm based on the FABRIK algorithm, which models the update of key nodes as the movement of charges in an electric field to achieve robustness in inverse kinematics and path tracking. The algorithm exhibits high convergence rate, low computational cost, and real-time applicability.
SCIENCE CHINA-TECHNOLOGICAL SCIENCES
(2022)
Article
Computer Science, Artificial Intelligence
Xiaotong Hua, Guolei Wang, Jing Xu, Ken Chen
Summary: This paper presents a path planner based on reinforcement learning skills, which achieves robust planning for duct-enter tasks by incorporating optimization functions, robot end orientation, and path guide points into the action part. Experimental results show a significant improvement in success rate compared to traditional methods.
JOURNAL OF INTELLIGENT MANUFACTURING
(2021)
Article
Robotics
Sagar Suhas Joshi, Seth Hutchinson, Panagiotis Tsiotras
Summary: Sampling-based methods are effective for solving kinodynamic motion planning problems, but require an intelligent exploration strategy to accelerate convergence. This work introduces a Time-Informed Set (TIS) that focuses the search for time-optimal solutions, speeding up the process by targeting trajectories that can improve the current best solution. Benchmark experiments demonstrate the acceleration in convergence achieved by this strategy.
IEEE ROBOTICS AND AUTOMATION LETTERS
(2021)
Article
Robotics
Liangliang Zhao, Jingdong Zhao, Ziyi Liu, Dapeng Yang, Hong Liu
Summary: This paper presents a novel algorithm for real-time motion planning of non-holonomic robots in dynamic scenes. The algorithm decomposes the robot and obstacles into superquadric objects and uses expanded Minkowski sums to construct the velocity obstacle. It also extends to collision avoidance for different types and numbers of obstacles.
IEEE ROBOTICS AND AUTOMATION LETTERS
(2022)
Article
Robotics
Adarsh Jagan Sathyamoorthy, Utsav Patel, Moumita Paul, Nithish K. Sanjeev Kumar, Yash Savle, Dinesh Manocha
Summary: This paper presents a novel approach called CoMet for computing a group's cohesion and using it to improve a robot's navigation in crowded scenes. The authors propose a cohesion-metric that builds on prior work in social psychology and compute this metric by utilizing visual features of pedestrians. They design and improve a navigation scheme based on this cohesion-metric and evaluate its performance on various metrics, showing significant decreases in freezing rate, deviation, and path length of the trajectory.
IEEE ROBOTICS AND AUTOMATION LETTERS
(2022)
Article
Robotics
Rahul Peddi, Nicola Bezzo
Summary: The majority of collision avoidance and motion planning algorithms on autonomous mobile robots are reactive to the presence of dynamic actors, but do not necessarily follow social norms. Humans can reason about interference with others and adapt motion based on different priorities, affecting how interactions occur. The proposed approach aims to generate non-interfering, priority-based behaviors for robots to accommodate dynamic actors by combining a virtual physics-based planner with predictive models and corrective actions.
IEEE ROBOTICS AND AUTOMATION LETTERS
(2022)
Article
Robotics
Zhuo Yao, Wei Wang, Jiadong Zhang, Yan Wang, Jinjiang Li
Summary: Line-Of-Sight (LOS) check is crucial for collision avoidance and time-consuming, especially in scenarios with large-scale maps and sparse obstacles. A new efficient LOS checker, Jump Over Block (JOB), is proposed to reduce the number of examined grids and improve efficiency. Comparative experiments show that JOB only incurs 1/6 to 1/5 of the computational cost of traditional LOS checks, making it a valuable tool for researchers and practitioners.
IEEE ROBOTICS AND AUTOMATION LETTERS
(2023)
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
Agronomy
Yufeng Li, Jingbin Li, Wenhao Zhou, Qingwang Yao, Jing Nie, Xiaochen Qi
Summary: This study proposes a robotic path planning and navigation method based on the improved A* and dynamic window approach algorithms. It can accurately determine the robot's navigation paths, and has high navigation accuracy and stability in specific path navigation.
Article
Robotics
Mrinal Verghese, Nikhil Das, Yuheng Zhi, Michael Yip
Summary: Real-time motion planning for robots in complex high-dimensional environments is still an open problem. In this study, we propose a novel approach using K-Means clustering in the Forward Kinematics space to accelerate collision checking. By training individual configuration space models, we obtain compact yet highly accurate models that can be queried rapidly in more complex environments. Experimental results demonstrate that our method, called Decomposed Fast Perceptron (D-Fastron), achieves significantly faster collision checks and motion planning compared to state-of-the-art geometric collision checkers.
IEEE ROBOTICS AND AUTOMATION LETTERS
(2022)
Article
Robotics
Chen Zhang, Yibin Li, Lelai Zhou
Summary: In this study, a novel Optimal Path and Timetable Planning (OPTP) method is proposed for multi-robot collaborative path planning. The OPTP generates near-shortest paths for each robot using an RRT*-based planner and creates timetables for each robot using an improved Particle Swarm Optimization (PSO) method. The OPTP achieves near-shortest moving distance and near-optimal navigation makespan for the multi-robot team in complex environments.
IEEE ROBOTICS AND AUTOMATION LETTERS
(2022)
