Autonomous navigation in mobile robots has made significant advancements; however, traditional methods often struggle to adapt in real-time to dynamic or unstructured environments. This paper presents the Online Planner's Parameter Adaptation (OPPA) framework, which enhances both adaptability and safety in mobile robot navigation by dynamically adjusting planner parameters. OPPA integrates a rule-based system for estimating tunnel width using 2D LiDAR and path data with a learning-based approach utilizing a shallow transformer model. By incorporating a human-in-the-loop process to refine training data, OPPA improves accuracy and reliability in complex environments. Designed for real-time efficiency on resource-constrained platforms, OPPA has been validated through simulation and real-world experiments, demonstrating its ability to enhance both safety and performance. These results highlight OPPA as a viable solution for dynamic and complex robotic applications.

In this paper, we propose a new and simple control strategy for gait assistance with a hip exoskeleton robot. This controller is based on the time delayed, self-feedback known for stabilizing oscillatory systems under certain conditions. In this controller, there are no separate estimators for the gait phase nor the environment, yet the controller can be generalized to operate under various walking conditions (e.g., stair and ramp walking). We first define a state variable representing the current leg's movement with hip joint angles. A simple assistance control can be described in closed-loop form with the delayed state feedback. By assigning the appropriate time-delay and self-feedback gain, we can generate assistive torques stably under the interaction between human and exoskeleton. The controller provides immediate and smooth assistance to user movement by reflecting the change of leg motion at every control period. The proposed joint-angle-based delayed-feedback assistance controller can operate under various walking speeds and environmental changes (e.g., stairs and ramps) without the need for additional sensors, computational processing, and parameter adjustment. Using a simple leg swing model, we perform a stability analysis under a simplified condition to provide insights into the effects of the time-delayed feedback in oscillatory systems. Then, we experimentally validate the efficacy of the proposed assistance controller by measuring the metabolic energy expenditure for level treadmill walking. We also test and analyze the generated assistive torques and power under the different walking conditions to show the generalizability of the controller.

We propose a simulation framework for gait assistance with ankle pathologies. We first construct the neu-romuscular walking model, then design the parameters for assistance torques for stance and swing legs. The parameter values are determined by performing dynamic optimizations which takes into account the human-exoskeleton interactive dynamics. The simulated energy expenditure and kinematic data are compared with the real data. Case studies involve abnormal gaits with 1) foot drop, 2) foot drop and plantarflexion failure. We evaluate the gait efficiency and walking speed for the different gait types. Our result shows that each gait type should have a different assistance strategy (timing and magnitude) compared to the assistance strategy of a normal gait.

We propose a novel two-tier gait recognizer using a minimal number of mechanical sensors built into a lower limb exoskeleton. The aim of this recognizer is to offer one-step selection of one of the actions ascending, descending, and level walking during five gaits (stair ascent/descent, slope ascent/descent, level walking). The proposed recognizer is executed at the moment of foot contact as estimated by an inertial measurement unit (IMU) on the pelvis without using direct foot sensors. The proposed recognizer selects the gait by using the relations between the angles formed by the hip and knee joints during the last step. As this study constitutes preliminary work for lower limb exoskeletons, we used a hip exoskeleton integrated with two wireless IMUs on the shank to set up the lower limb exoskeleton sensor configuration. Experiments were used to evaluate IMU-based foot contact estimation with respect to a foot sensor based on a force-sensitive resistor (FSR) for stairs, slopes, and level ground. In addition, we evaluated the performance of the proposed two-tier gait recognizer by using two healthy male subjects in various gait environments.

In this paper, we propose a novel assistance strategy for stair ascent walking using our robotic hip exoskeleton. Our strategy exploits foot contact event estimated by an inertial measurement unit (IMU) and can detect user intention as well as reflect user preference. In our strategy, a gait cycle is divided into 4 phases and the transitions between the phases are based on events that are unavoidable and can be detected reliably using the sensors available for our exoskeleton. The proposed strategy presents criteria for the initiation and termination of assistance by recognizing user intention, as well as methods to adjust the timing and the amount of assistance corresponding to user preference. When each step starts, the duration of assistance is determined and the torque profile for the whole step is planned ahead. We demonstrate the validity of the proposed strategy for stair ascent and examine its effects on hip joint angle trajectory through experimental results.

This paper presents a simulation framework for pathological gait assistance with a hip exoskeleton. Previously we had developed an event-driven controller for gait assistance [1]. We now simulate (or optimize) the gait assistance in ankle pathologies (e.g., weak dorsiflexion or plantarflexion). It is done by 1) utilizing the neuromuscular walking model, 2) parameterizing assistive torques for swing and stance legs, and 3) performing dynamic optimizations that takes into account the human-robot interactive dynamics. We evaluate the energy expenditures and walking parameters for the different gait types. Results show that each gait type should have a different assistance strategy comparing with the assistance of normal gait. Although we need further studies about the pathologies, our simulation model is feasible to design the gait assistance for the ankle muscle weaknesses.

In this paper, we propose a novel online gait task recognition algorithm for hip exoskeleton. The proposed algorithm provides an automatic and prompt recognition result in just one step based on the relations between both hip joint angles at the moment of foot contact. Gait task recognition is one of the challenges that walking assist devices must address to offer adaptable and reliable assistance to users. However gait task recognition in hip exoskeleton is challenging because the sensors are very limited and fast gait task recognition is required to prevent inadequate assistance and reduce fall risk. Although in general foot contact event can be considered as crucial information during walking, it has not received attention in hip exoskeletons with no sensors corresponding foot force or pressure. In this study, we exploit foot contact event as a critical point to perform gait task recognition in hip exoskeleton. The proposed algorithm suggests a foot contact estimation method without using any foot force or pressure sensors and a rule-based inference system to recognize a new gait task in real time. Results presented from experiments will demonstrate the validity and performance of the proposed algorithm.

This paper presents a control method for walking assist with hip-mounted exoskeleton robots. For modeling a user's current walking motion, a novel finite state machine is first constructed. We divide a walking cycle uniformly using the inevitable zero crossing events. When state transitions occur, we capture the current walking spatio-temporal sensor data as discrete form. By using the sensed hip data as boundary conditions, we also develop a gait primitives based motion reconstruction method. Gait primitives are a form of basis trajectories to represent various joint motions. From those methods we estimate the moment of heel landing with interpolated knee joint motions. Utilizing the user's previous opposite step motion, we predict the positive or negative work intervals of the current step motion. This makes it possible to achieve natural ‘one shot’ assist by driving adapted torques fast. This assist strategy is also effective to enhance gait regularity. The measures of stride time variability are improved by over 30% for the simulated experiment. Various real experimentations demonstrate the feasibility of our approach.

This paper presents a novel kinematic control scheme based on the Virtual Incision Ports (VIPs) for redundancy resolution of redundant robotic arms for single-port access (SPA) surgery. In general, manipulators have 6 DoFs except grippers to be able to reach the desired pose in 3D space. If a surgical robot for SPA surgery has only 6 DoFs, then its workspace could be restricted severely. Therefore most robots including our developed robot consist of more than 6 DoFs with an elbow to maintain triangulation. This means they have a redundancy resolution problem. One of the most popular methods for a redundancy resolution is a pseudo-inverse Jacobian method [1]. In case of robotic SPA surgery, however, this method intrinsically has a high possibility for hurting abdominal organs and muscles or conflicting with other instruments because of the unexpected elbow movements. Our control scheme can decrease the possibility of a collision with them and provide a more flexible working area for surgical tasks by reallocating the VIP. Results presented from simulation and experiment will demonstrate them.

In this paper, we introduce a remote center-of-motion (RCM) mechanism with a conical shape for laparoscopic surgeries that involve a single incision. The mechanism, which has two revolute joints and one prismatic joint, is designed to maintain a stationary point at the apex of the conical shape. By aligning the stationary point with the incision area, the mechanism allows a surgical instrument to explore the abdominal area through a small incision point. We have previously analyzed the reachable workspace of this mechanism. Here, we arrange two RCM mechanisms on a single conical structure but separated in space to avoid mutual interference, so as to enable the entire system to manipulate two surgical instruments through a single incision point without colliding. We describe the operational principle of this system, in addition to comparisons of various RCM mechanisms and the kinematics for parameter design and motion control. Finally, we describe preliminary experiments on peg transfer and suture motion by using the proposed RCM mechanism.