Wansoo Kim

This paper proposes a real-time trajectory planning method using nonlinear optimization and whole-body control for cooperative transport with heterogeneous mobile manipulators. Unlike conventional approaches that rely on passive path following, the proposed method enables the follower robot to actively generate its own trajectory by laterally offsetting the leader’s path and refining it through nonlinear optimization. The optimization process simultaneously considers inter-robot distance maintenance, geometric consistency, and obstacle avoidance in cluttered environments. The optimized trajectory is executed through a generalized hierarchical control framework that integrates impedance-based coordination between the mobile base and manipulator. Experimental validation with two mobile manipulators—, featuring different locomotion and control characteristics—, demonstrates that the system maintains a consistent end-effector distance while adapting to environmental changes. The proposed method achieves high-fidelity trajectory tracking (average error: 0.037 m, standard deviation: 0.039 m) and maintains inter-robot spacing within a 1.05–1.65 m range for 96.09% of the task duration. The results demonstrate the framework’s relevance to real-world cooperative transport situations, emphasizing the need for adaptability, precision, and inter-robot coordination.

Gait is intricately linked to human health, with key gait indexes showing strong correlations. To overcome limitations of flat-surface-focused evaluations, we propose a Modified Gait Index (MGI) that adapts to diverse terrains such as ramps and stairs. Gait data were collected from 15 healthy adults using motion capture, IMU (Inertial Measurement Unit), EMG (Electromyography), and force plates. For each terrain, principal component analysis (PCA) identified key contributing features based on loading vectors and contribution scores. MGI formulas were derived by combining these features with clinically relevant variables, including stance/swing ratio and joint flexion angles. Participants were divided into training and test groups based on PCA similarity, and generalization was evaluated using interquartile range-based normal thresholds. Across all terrains, the test group’s average GI values remained within the training-defined normal range: 1.05 - 2.82 for ramp ascent, 0.71 - 4.02 for ramp descent, 1.68 - 2.86 for stair ascent, 1.15 - 3.99 for stair descent. Although some test values approached quartile boundaries, no outliers were observed. These results demonstrate the MGI’s terrain-adaptive consistency and generalizability, supporting its application in exoskeleton control strategies for users with subclinical gait deviations or muscular overload.

Accurate three-dimensional (3D) localization is critical for robust human-robot collaboration (HRC) in dynamic indoor environments. However, realizing high-precision localization in complex scenarios still faces challenges such as multipath effects, field-of-view occlusion, etc. To address these limitations, we propose Geo-LSTM, a geometry-constrained long short-term memory (LSTM) framework that integrates ultra-wideband (UWB) sensors, inertial measurement unit (IMU), and barometric pressure (BMP) sensors. First, a Simplified Geometric Localization (SGL) algorithm is proposed, which uses dual-BMP sensors and IMU sensor to obtain precise height information and utilizes the geometric relationships between the UWB tag and anchors to compute an initial location estimate, serving as a priori input for the Geo-LSTM network. This Geo-LSTM algorithm then incorporates multi-source geometric information to extract time-series features from the UWB ranging data and the tag's a priori location, further enhancing 3D localization accuracy. The experimental results from the cluttered indoor environments, including real-world HRC tasks with occlusions, show that the Geo-LSTM algorithm achieves an average 3D localization root mean square error (RMSE) of 0.103 m, representing improvements of 38.60% and 31.20% over the weighted least squares (WLS) method and the range-based LSTM algorithm, respectively. These results demonstrate Geo-LSTM's potential for reliable multi-sensor 3D localization in HRC applications.

In physical human-robot interaction (pHRI), ensuring human safety in all tasks conducted by the robot is crucial. Traditional compliance control strategies, such as admittance and impedance control, often lead to unpredictable robot behavior due to incidents like contact loss or unexpected external forces, which can cause significant harm to humans. To overcome these limitations, this study introduces a multi-task energy-aware impedance controller for kinematically redundant robots. This controller extends the energy-aware impedance control strategy, which ensures the passivity and safety of a single task using a virtual global energy tank, to kinematically redundant robots performing multiple tasks. The proposed controller effectively regulates the power flow of all tasks performed by the robot through a single global energy tank, ensuring the safety and passivity of the tasks. Experimental results in a shared environment, where external forces are simultaneously applied to the end-effector and the third joint of the Franka Emika Panda, showed that the robot's energy and power, as well as the power of all tasks, consistently remained within predefined thresholds. Additionally, when comparing the proposed controllers with controller that do not consider null space projection in the power regulation stage and controller that do not regulate the robot's power, our approach effectively managed the robot's energy and power and the power of all tasks, ensuring passivity and enhanced safety.

This paper presents a novel algorithm that enhances the efficiency of robotic manipulation by enabling the robot to reach its target with fewer obstacles being removed, thus leading to faster performance times. The challenges faced by robotic manipulation in complex and cluttered environments (e.g., supermarkets, factories, etc.), where changes are constantly occurring, are well-known. To tackle these challenges, the robot should generate a collision-free path to reach the target while requiring minimal obstacle relocation to improve task efficiency. To achieve this, the proposed task planning algorithm incorporates the Rapidly-exploring Random Tree (RRT) algorithm and a modified Vector Field Histogram Plus (VFH+). The algorithm was tested in simulated service domain environments with objects placed on shelves followed by real-robot experiments. In both experiments, the results of the proposed algorithm demonstrated a lower number of obstacle removals (up to 37%) and consequently faster performance times compared to other existing methods (improved by up to 36%). Thus, the results showed that the proposed algorithm reduces the number of obstacle relocations and improves performance time compared to existing methods. As a result, the proposed method was evaluated as an improvement in task efficiency over previous approaches and has significant implications for the use of robotic manipulation in service domains with cluttered environments.