This study advances indoor environment modeling by focusing on the optimal placement of sensors. Our approach involves creating a detailed environment model from a 3D point cloud by identifying spatial boundaries and furniture in indoor spaces, which are then represented as a series of polygons. To validate our method, we compare its performance against ground truth data, demonstrating high accuracy in both simple and complex environments. The core of our study is a comprehensive experiment that evaluates the effectiveness of three evolutionary nature-inspired genetic and three metaheuristic iterative optimization algorithms in solving the sensor placement problem in a complex environment scenario. We perform a statistical analysis to understand the impact of the choice of optimization algorithm and the number of sensors on the achieved spatial coverage. This analysis provides insights into the comparative effectiveness of various evolutionary algorithms in enhancing sensor network design within intricate indoor spaces. In particular, the Artificial Bee Colony algorithm consistently delivered superior results.
The goal of cancer treatment is to remove or kill malignant cells while preserving surrounding healthy tissue. Among treatment methods, needle-based ultrasound thermal ablation is an option that involves the insertion of an applicator into the patient's body and the use of an ultrasound transducer to vibrate tissue, producing heat. An ablation pattern for an arbitrarily shaped tumor can be approximated by moving the applicator to deposit heat in targeted locations. However, this conformal ablation process is challenging to control because of the complex interactions between tissue and ultrasound. To address this, we built an interactive planning toolkit that allows a physician to perform the procedure multiple times in simulation and record the ablation trajectory once a desired result is achieved. To validate this method, a previously developed MRconditional robot was used to replicate the planned ablation in a phantom model. Live Magnetic Resonance Thermal Imaging was used to track temperature changes, allowing us to measure the thermal dose and identify the ablated region. In 4 ablations experiments, we achieved an average of 80.9% overlap between the targeted tumor area and the actual ablated area, with minimal damage of 9.4% affected surrounding tissues, demonstrating the effectiveness of our approach.
The actual paper presents an in-depth study and experimental development of a class of rotorcraft, named as x-tilt, that features four tilting rotors. Initially, the equations of motion modeling the aerial robot are presented based on the Euler-Lagrange formulation. The model includes the aerodynamic effects induced by the rotorcraft's relative motion and propellers. For control purposes the aforementioned model is split into a nominal model and lumped disturbance terms, the latter encompassing endogenous and exogenous uncertainties. In this vein, the actual work propose a robust navigation strategy targeting a specific performance profile whose problem is formulated through the model predictive control (MPC) framework. To this end, two schemes are proposed, (i) an integral MPC and a (ii) MP sliding-mode Control (MPSMC). Both control schemes are linked to a extended-state Linear Kalman Filter (ES-LKF) that furnishes the states and lumped disturbance estimates. Moreover, a high-fidelity simulation is presented in detail to validate the effectiveness of the proposed controller within a realistic scenario. We finally present the experimental stage to validate the tilting-rotor configuration as well as the integral MPC.
Whereas robots are and will be increasingly present in all areas of society, robotics can be limited by its narrow perspective, often neglegecting discussions about its own goals, epistemology, ethics, and socio-political impacts. In order to try to adress this issue, this article proposes a holistic analysis of robots and robotics. Taking into these blind spots, raising new challenges. In response, the second part proposes a set of guidelines : a new epistemology, a metaethical framework, an organization method and a template design in order to answer these challenges.
Chemical process design is the search for an optimal manufacturing protocol to perform chemical operations. For transient processes such as crystallization, the optimal conditions can change over time, requiring a dynamic strategy. Model-free deep reinforcement learning is an approach that can be used to identify the best sequence of states with respect to a predefined reward function. In this work, proximal policy optimization is applied in a simulated environment to identify operational strategies that are optimal with respect to the desired particle properties in unseeded batch cooling crystallization processes of paracetamol in ethanol. For this purpose, the corresponding Markov decision process is formulated, and it is shown that the method is promising for the development of novel routes that allow the tuning of particle size (623 μm) and provide high yields (96%) within a defined period of time (12 h).
Motor unit (MU) decomposition, generally, requires a time-consuming and labor-intensive manual inspection/editing process from human operators to ensure high accuracy. In this study, we propose and validate a rule-based auto-editing method that could potentially substitute the manual process. Methods: The proposed auto-editing framework (autoeditor) consists of four main rules for adding and removing spikes based on the height of the innervation pulse train (IPT) and the regularity of the firing rate of the identified motor unit. The rules were optimized and validated based on an open-source database including raw MU spike trains estimated from the convolution kernel compensation method and the manually edited MU spike trains from eight human operators. Results: Across 110 motor units, the average rate of agreement between the auto-editor and human operators reached 99.2% after the auto-editor corrected more than 10 edits for each motor unit on average from the raw spike trains. More importantly, the characteristics of the motor unit behaviors, including the MU firing rate and recruitment threshold, were consistent across human operators and the proposed auto-editor. Conclusion: With a simple but effective rulebased auto-editing framework, comparable performance in MU refinement was achieved as human operators. Significance: The proposed auto-editing framework has the potential to standardize the MU editing practice, lower the requirements for expert knowledge and specialized training for MU decomposition, and provide an expandable framework allowing contributions from the community.
A novel wheelchair called PURE (Personalized Unique Rolling Experience) that uses hands-free (HF) torso lean-to-steer control has been developed for manual wheelchair users (mWCUs). PURE addresses limitations of current wheelchairs, such as the inability to use both hands for life experiences instead of propulsion. PURE uses a ball-based robot drivetrain to offer a compact, self-balancing, omnidirectional mobile device. A custom sensor system converts rider torso motions into direction and speed commands to control PURE, which is especially useful if a rider has minimal torso range of motion. We explored whether PURE’s HF control performed as well as a traditional joystick (JS) human-robot interface and mWCUs, performed as well as able-bodied users (ABUs). 10 mWCUs and 10 ABUs were trained and tested to drive PURE through courses replicating indoor settings. Each participant adjusted ride sensitivity settings for both HF and JS control. Repeated-measures MANOVA tests suggested that the number of collisions, completion time, NASA TLX scores except physical demand, and index of performances were similar for HF and JS control and between mWCUs and ABUs for all sections. This suggests that PURE is effective for controlling this new omnidirectional wheelchair by only using torso motion thus leaving both hands to be used for other tasks during propulsion.Link for supplementary material (appendix, short descriptive video): https://uofi.box.com/s/hbrs0bbbxez4hr51potzywy3p9iupryu
This paper is a continuation on my revolutionary theory of solving the pointwise fluid flow approximation model for time-varying queues. Thus, the long-standing simulative approach has now been replaced by an exact solution by using a constant ratio 𝛽 (Ismail's ratio) , offering an exact analytical solution. The stability dynamics of the time-varying 𝑀/𝐸 𝑘 /1 queueing system are then examined numerically in relation to time, 𝛽, and the queueing parameters.
This paper details an experiment utilizing ESP8266 modules as servers to wirelessly control diverse electrical appliances in home automation. The experiment showcased the modules' capability to respond to commands via a web interface on both mobile and desktop platforms or even tablets. While most of the experiment ran smoothly, occasional freezing and connectivity disruptions were observed. The abstract encapsulates the experiment's successes, discusses encountered challenges, and outlines a forward-looking perspective, including the integration of a custom PCB for enhanced system stability.
Integrating the artificial intelligence vision system into robots has significantly enhanced the adaptability of grasping, but are vulnerable to potential backdoor threats. Currently, the majority of backdoor attacks are focused on image classification and are limited to unimodal information and single-object digital scenarios. In this work, we make the first endeavor to realize the backdoor attack on the multimodal vision-guided robot grasping within high-clutter scenarios. Specifically, we propose a novel backdoor attack method named Shortcut-enhanced Multimodal Backdoor Attack (SEMBA), which is divided into two parts. Firstly, for the attack robustness and multimodality, we introduce the Multimodal Shortcut Searching Algorithm (MSSA) to find the pixel value that deviates the most from the mean and standard deviation of the dataset and the pivotal pixel position for individual images, respectively. Next, based on MSSA, we devise the Multimodal Trigger Generation (MTG), to diversify backdoor triggers and realize attacks in the real world. After being trained on this dataset, the model will be activated to prioritize grasping the trigger-like object within the camera view. We conduct extensive experiments on the benchmark datasets and a robotic arm, showing the effectiveness of this method in both the digital and real world. Note to Practitioners-Robots are typically designed to be safe and reliable. However, the integration of artificial intelligence technology with robots can make them unpredictable in certain situations, such as when utilizing third-party data or models. Therefore, it is necessary to explore the security of artificial intelligence-driven robots. In this paper, we address a backdoor attack on robots equipped with an artificial intelligence vision system. Unlike typical backdoor attack methods focused on the digital world, we pay more attention to the attack robustness, multimodality, and adaptability in complex real-world scenarios. Along these lines, we propose a new backdoor attack method and demonstrate its capability to attack multimodality-guided visual grasping systems in high-clutter environments. Our method proposes potential avenues for future research on data-driven security, bringing a wealth of practical insights on a trustworthy visual learning-based robot grasping system.
The detection accuracy and speed of grasp detection models on benchmarks are the focal points of concern in the robotic grasping community. Especially in a collaborative robot setting, the safety of the model is an essential aspect that cannot be overlooked. In this paper, we explore how to enhance the safety of grasp detection models in autonomous vision-guided grasping. Specifically, we propose a simple yet practical Safety-optimized Strategy, which consists of two parts. The first part involves depth prioritization, optimizing the grasp sequence from top to bottom based on the order of depth values, which can mitigate the issue of grasp collisions that may arise when the depth value of the object with the highest grasp quality is significantly higher than that of other objects in high-clutter scenarios. The second part is false-positive protection, where we introduce the robust Aruco marker as the lowest grasp priority. The marker is fixed at certain positions within the camera's field of view, enabling the robot to halt its movement, thereby restraining the robot from grasping objects that should not be grasped. Once the marker disappears, the robot can resume its operations. We validate our method through real grasping experiments with a parallel-jaw gripper and an industrial robotic arm, demonstrating its effectiveness in high-clutter scenarios.
Prostheses are becoming more advanced and biomimetic with time, providing additional capabilities to their users. However, prosthetic sensation lags far behind its natural limb counterpart, limiting the use of sensory feedback in prosthetic motion planning and execution. Without actionable sensation, prostheses may never meet the functional requirements to match biological performance. We propose an approach for upper-limb prosthetic object slip prediction and notification, delivered to the wearer through direct nerve stimulation. The method is based on sensory synthesis, training a linear regression of the sensors embedded in a prosthetic hand to predict slip before it occurs. Four participants with transhumeral amputation performed block pulling tasks against increasing resistance, attempting to pull the block as far as possible without slip. These trials were performed with two different prediction notification paradigms. At lower grasp forces, spike notification stimulation reduced the incidence of object slip by 32%, and at higher grasp forces, the maximum achieved pull forces increased by 19% across participants when provided with stimulation proportional to the likelihood of a predicted slip. These results suggest that this approach may be effective in recreating a lost sense of grip stability in the missing limb and may reduce unanticipated slips.
_Goal:_ Vascular surgical procedures are challenging and require proficient suturing skills. To develop these skills, medical training simulators with objective feedback for formative assessment are gaining popularity. As hardware advancements offer more complex, unique sensors, determining effective task performance measures becomes imperative for efficient suturing training. _Methods:_ 97 subjects of varying clinical expertise completed four trials on a suturing skills measurement and feedback platform (SutureCoach). Instrument handling metrics were calculated from electromagnetic motion trackers affixed to the needle driver. _Results:_ The results of the study showed that all metrics significantly differentiated between novices (no medical experience) from both experts (attending surgeons/fellows) and intermediates (residents). Rotational motion metrics were more consistent in differentiating experts and intermediates over traditionally used tooltip motion metrics. _Conclusions:_ Our work emphasizes the importance of tool motion metrics for open suturing skills assessment and establishes groundwork to explore rotational motion for quantifying a critical facet of surgical performance. _Impact Statement_–This study aims to determine the effectiveness of metrics derived from needle driver rotational and tooltip motion tracking to determine differences in clinical expertise in open needle driving.
As human-robot interaction rapidly spreads in numerous fields, the subject of robot acceptance gains increasing importance. Visual similarity to the human body, as occurs for humanoids, is generally not enough to guarantee acceptance in physical interaction, as acceptance directly links to comfort and ergonomics, which are measured in terms of the quality of the robot movement perceived by the human. This paper discusses the connection between comfort and similarity of the robot movement to the human one. By considering the kinematic characterization of human movement, this paper focuses on the time laws of such movements, wherein the end-effector path is prescribed. Based on the sigma-lognormal velocity model, a human-likeness index is defined and used to provide with an a priori characterization of trajectories. Such an index can be used to evaluate the performance of trajectory generation algorithms in producing human-like movements, before they are actually executed. For validation purposes, through physical interaction with a robot, a sample of 38 subjects is asked to compare trajectories and judge about their comfort over three experimental campaigns. The results demonstrate a globally consistent trend between the preference in terms of perceived comfort and the distribution of the suggested human-likeness index.
This study entailed the derivation of an inverse airflow dynamics model that causes pneumatic transmission delay due to the long air tube in pneumatic artificial muscle (PAM) control. The inverse model of the derived airflow dynamics was used in designing a feedforward pressure control command for the proportional pressure control valve. We also modulated the feedback command based on the proposed pressure control for precise force tracking performance. The proposed methods were validated by varying the tube lengths in the pressure tracking task and force tracking task. The tracking tasks with the sinusoidal desired profile of 2.5 Hz were successfully achieved using the proposed method. The results indicate that the proposed method can compensate for the delay due to the airflow dynamics in the long air tube and improve the control performance.
We propose an integrated behavior and motion planning framework for the automated lane-merging problem. The behavior planner combines search-based planning with game theory to model the interaction between vehicles and select multivehicle trajectories. Inspired by human drivers, we model the lane-merging problem as a gap selection process. To overcome the challenge of multi-modal driving behavior exhibited by the surrounding vehicles, we formulate the trajectory selection as a matrix game and compute some equilibrium solutions. In practice, however, the surrounding vehicles might deviate from the computed equilibrium trajectories. Thus, we introduce a branch model predictive control (BMPC) framework to account for the uncertain behavior modes of the surrounding vehicles. A tailored numerical solver is developed to enhance computational efficiency by leveraging the tree structure inherent in BMPC. Finally, we validate our proposed integrated planner using real traffic data and demonstrate its effectiveness in handling interactions in dense traffic scenarios.
This paper proposes a method for a snake robot to fit ordered points and thus generate body shape. A non-smooth backbone curve consisting of straight line segments is constructed by sequentially connecting the ordered points, and then the bending and twist angles at the singularities are obtained iteratively to obtain the joint angles of the snake robot. Fitting the shape of a snake robot using this method does not depend on obtaining a backbone curve with known curvature and torsion, greatly expanding the applicability of the fitting method. In this paper, the validity of the fitting method is verified by simulation, which generates a shape that resembled a biological snake coiled on the ground.
This paper details the intricacies in the design of power supplies for varied voltage and current requirements in scaled-down electric vehicle platforms. It includes the development of protective circuits safeguarding electrical and electronic components from overvoltage, undervoltage, reverse voltage, inrush current, and surge current. Furthermore, the paper explores a sophisticated sensing module designed for continuous monitoring of battery and cell voltages. The circuits are implemented and tested on a one-tenth-scale electric vehicle, DEFT, operating with a 6-cell LiPo battery.