Robotic Framework Research Articles

Reinforced NEAT Algorithms for Autonomous Rover Navigation in Multi-Room Dynamic Scenario

This paper demonstrates the performance of autonomous rovers utilizing NeuroEvolution of Augmenting Topologies (NEAT) in multi-room scenarios and explores their potential applications in wildfire management and search and rescue missions. Simulations in three- and four-room scenarios were conducted over 100 to 10,000 generations, comparing standard learning with transfer learning from a pre-trained single-room model. The task required rovers to visit all rooms before returning to the starting point. Performance metrics included fitness score, successful room visits, and return rates. The results revealed significant improvements in rover performance across generations for both scenarios, with transfer learning providing substantial advantages, particularly in early generations. Transfer learning achieved 32 successful returns after 10,000 generations for the three-room scenario compared to 34 with standard learning. In the four-room scenario, transfer learning achieved 32 successful returns. Heatmap analyses highlighted efficient navigation strategies, particularly around starting points and target zones. This study highlights NEAT’s adaptability to complex navigation problems, showcasing the utility of transfer learning. Additionally, it proposes the integration of NEAT with UAV systems and collaborative robotic frameworks for fire suppression, fuel characterization, and dynamic fire boundary detection, further strengthening its role in real-world emergency management.

ROS-Driven Autonomous Navigation: A Scalable Framework for Wheeled Robots in Dynamic Environments

ROS-Driven Autonomous Navigation: A Scalable Framework for Wheeled Robots in Dynamic Environments

A novel framework for entertainment robots in personalized elderly care using adaptive emotional resonance technologies

The aging population suffers from some difficult aspects, such as loneliness, dementia, and lack of meaning, which diminish their enjoyment of life. This work presents a novel model using robotics and artificial intelligence to create entertainment robots that can adapt their interaction based on the elderly’s emotion. The model is implemented through the use of the multimodal emotion recognition technology that holds the facial expression, voice tone, physiological expressions, and body postures. Supported by the emotional resonance algorithm and personalization features, these robots can deliver emotional resonance that corresponds to the user’s mood. With time, this model may learn and provide better feedback. Ethical factors are also taken into account. The robot handles multimedia content, natural language processing, and gentle movements that enhance interaction. The trial involved 20 elderly users, which showed effective outcomes of mood and engagement. Standardized psychological assessments, such as the Geriatric Depression Scale (GDS) and the Quality of Life in Alzheimer’s Disease (QOL-AD) scale, revealed that participants exhibited a 2.1-point decrease in GDS scores, indicating a reduction in depressive symptoms, and a 3.8-point increase in QOL-AD scores. This framework, is thus, a promising tool in transforming elderly care

Implementation of a robotic framework for multi-axis supportless fused filament fabrication via volume decomposition: a practical approach

Implementation of a robotic framework for multi-axis supportless fused filament fabrication via volume decomposition: a practical approach

Shared Control Design of a Walking-Assistant Robot

Walking-assistant robots serve as a vital aid for individuals with mobility challenges, enhancing their independence and mobility. Shared control systems combine user input and robotic intelligence to ensure safe, adaptable, and intuitive navigation. This paper presents a shared control framework for a walking-assistant robot, focusing on user intention prediction and real-time obstacle avoidance. A Python-based simulation demonstrates the system's performance in a scaled-up environment with larger obstacles and a broader robot trajectory.

Chaos-based coverage path planning framework for mobile robots and its digital signal processing implementation

Abstract For special coverage path planning, chaos-based mobile robots present a promising solution. The robot can cover the area of interest rapidly with unpredictable trajectories. However, this unpredictability causes difficulties in trajectory control and the robot often makes sharp turns, affecting motion stability. To address these challenges, this paper proposes a universal chaos-based path planning framework for mobile robots. Firstly, a chaotic system that can generate multi-scroll and multi-wing attractors is designed to verify the effectiveness of the framework. Secondly, an integrated system consisting of mobile robots and chaos is proposed to generate chaotic trajectories. By the control parameters in the integrated system, the formation mechanism of continuous chaotic trajectories is shown, which makes the chaotic trajectory have the advantages of scaling, rotating, straightening, and bending, and the path planning no longer relies on the chaotic system only. Then, a regional path planning strategy is proposed to control the angular velocity and workspace of the robot, making its motion smooth and reducing trajectory redundancy. Experiments conducted under this framework have demonstrated its university, with various chaotic systems capable of generating efficient robot trajectories with high coverage rate. Compared with the state-of-the-art similar memoryless algorithms, the coverage task can be accomplished quickly and efficiently by our approach. Finally, the robot trajectory tracking control verifies the controllability of chaotic trajectories, and the implementation of chaotic trajectories with digital signal processing techniques further validates the feasibility of path planning frameworks.

GRID-FAST: A Grid-based Intersection Detection for Fast Semantic Topometric Mapping

This article introduces a novel approach to constructing a topometric map that allows for efficient navigation and decision-making in mobile robotics applications. The method generates the topometric map from a 2D grid-based map. The topometric map segments areas of the input map into different structural-semantic classes: intersections, pathways, dead ends, and pathways leading to unexplored areas. This method is grounded in a new technique for intersection detection that identifies the area and the openings of intersections in a semantically meaningful way. The framework introduces two levels of pre-filtering with minimal computational cost to eliminate small openings and objects from the map which are unimportant in the context of high-level map segmentation and decision making. The topological map generated by GRID-FAST enables fast navigation in large-scale environments, and the structural semantics can aid in mission planning, autonomous exploration, and human-to-robot cooperation. The efficacy of the proposed method is demonstrated through validation on real maps gathered from robotic experiments: 1) a structured indoor environment, 2) an unstructured cave-like subterranean environment, and 3) a large-scale outdoor environment, which comprises pathways, buildings, and scattered objects. Additionally, the proposed framework has been compared with state-of-the-art topological mapping solutions and is able to produce a topometric and topological map with up to 92% fewer nodes than the next best solution. The method proposed in this article has been implemented in the robotics framework ROS and is open-sourced. The code is available at: https://github.com/LTU-RAI/GRID-FAST.

Robot learning on the job: Human-in-the-loop autonomy and learning during deployment

With the rapid growth of computing powers and recent advances in deep learning, we have witnessed impressive demonstrations of novel robot capabilities in research settings. Nonetheless, these learning systems exhibit brittle generalization and require excessive training data for practical tasks. To harness the capabilities of state-of-the-art robot learning models while embracing their imperfections, we present Sirius, a principled framework for humans and robots to collaborate through a division of work. In this framework, partially autonomous robots are tasked with handling a major portion of decision-making where they work reliably; meanwhile, human operators monitor the process and intervene in challenging situations. Such a human–robot team ensures safe deployments in complex tasks. Further, we introduce a new learning algorithm to improve the policy’s performance on the data collected from the task executions. The core idea is re-weighing training samples with approximated human trust and optimizing the policies with weighted behavioral cloning. We evaluate Sirius in simulation and on real hardware, showing that Sirius consistently outperforms baselines over a collection of contact-rich manipulation tasks, achieving an 8% boost in simulation and 27% on real hardware than the state-of-the-art methods in policy success rate, with twice faster convergence and 85% memory size reduction. Videos and more details are available at https://ut-austin-rpl.github.io/sirius/ .

Robustness and Scalability of Incomplete Virtual Pheromone Maps for Stigmergic Collective Exploration

The Swarm Guiding and Communication System (SGCS) is a decision-making and information-sharing framework for robot swarms that only needs close-range peer-to-peer communication and no centralized control. Each robot makes decisions based on an incomplete virtual pheromone map that is updated on each interaction with another robot, imitating ant colonial behavior. Similar systems rely on continuous communication with no range limitations, environment modification, or centralized control. A computer simulation is developed to assess the effectiveness and robustness of the framework in covering an area. Consistency and the time needed for 99% coverage are compared with an unbiased random walk. The pheromone approach is shown to outperfom the unbiased one regardless of number of agents. Innate resilience to individual failures is also demonstrated.

Minimal perception: enabling autonomy in resource-constrained robots.

The rapidly increasing capabilities of autonomous mobile robots promise to make them ubiquitous in the coming decade. These robots will continue to enhance efficiency and safety in novel applications such as disaster management, environmental monitoring, bridge inspection, and agricultural inspection. To operate autonomously without constant human intervention, even in remote or hazardous areas, robots must sense, process, and interpret environmental data using only onboard sensing and computation. This capability is made possible by advancements in perception algorithms, allowing these robots to rely primarily on their perception capabilities for navigation tasks. However, tiny robot autonomy is hindered mainly by sensors, memory, and computing due to size, area, weight, and power constraints. The bottleneck in these robots lies in the real-time perception in resource-constrained robots. To enable autonomy in robots of sizes that are less than 100 mm in body length, we draw inspiration from tiny organisms such as insects and hummingbirds, known for their sophisticated perception, navigation, and survival abilities despite their minimal sensor and neural system. This work aims to provide insights into designing a compact and efficient minimal perception framework for tiny autonomous robots from higher cognitive to lower sensor levels.

Cost Assessment Framework for Construction Robots: Comparative Study of Robotic and Traditional Construction

Cost Assessment Framework for Construction Robots: Comparative Study of Robotic and Traditional Construction

Fiducial Markers and Particle Filter Based Localization and Navigation Framework for an Autonomous Mobile Robot

Fiducial Markers and Particle Filter Based Localization and Navigation Framework for an Autonomous Mobile Robot

A User- and Slope-Adaptive Control Framework for a Walking Aid Robot

A User- and Slope-Adaptive Control Framework for a Walking Aid Robot

Navigating autonomy, privacy, and ageism in robot home care with aged users: A preliminary analysis of ROB-IN.

In this article, I propose an ethical analysis of assistive domestic robots for older users. Indoing so, I illustrate my inquiry with the example of ROB-IN assistive robot. ROB-IN is a Spanish project which is devoted to developing a robot that will perform in the private home of nondependent, aged users. It is aimed to help people in their daily activities and contribute to appropriate health monitoring. One of their potentially most useful features is related to data gathering and sharing. For the inquiry on the ethical underpinnings of this case, I develop a framework for domestic assistive robots for competent older adults drawn on the ethics of care. I assess that this type of robots could be ethically appraised attending to their impact on the well-being and autonomy of users. I approach autonomy from a relational perspective, and I delve into the relationship between autonomy and well-being through the concept of paternalism. I argue that this type of assistive robots should never act paternalistically. Given ROB-IN great implications regarding privacy, I subsequently explore the ways in which the privacy of users should be respected in their interaction with assistive robots, focusing on the relation with autonomy and well-being. Lastly, I highlight the need for avoiding ageism. This investigation focuses on aged users, but it is suggested that the situation of caregivers should be also the object of further investigations.

Reduced order modeling of hybrid soft-rigid robots using global, local, and state-dependent strain parameterization

The need for fast and accurate analysis of soft robots calls for reduced order models (ROM). Among these, the relative reduction of strain-based ROMs follows the discretization of the strain to capture the configurations of the robot. Based on the geometrically exact variable strain parametrization of the Cosserat rod, we developed a ROM that necessitates a minimal number of degrees of freedom to represent the state of the robot: the Geometric Variable Strain (GVS) model. This model allows the static and dynamic analysis of open-, branched-, or closed-chain soft-rigid hybrid robots, all under the same mathematical framework. This paper presents for the first time the complete GVS modeling framework for a generic hybrid soft-rigid robot. Based on the Magnus expansion of the variable strain field, we developed an efficient recursive algorithm for computing the Lagrangian dynamics of the system. To discretize the soft link, we introduce state- and time-dependent basis, which is the most general form of strain basis. We classify the independent bases into global and local bases. We propose “FEM-like” local strain bases with nodal values as their generalized coordinates. Finally, using four real-world applications, we illustrate the potential of the model developed. We think that the soft robotics community will use the comprehensive framework presented in this work to analyze a wide range of specific robotic systems.

Robotic Framework for Requirement Management, Estimations and Project Proposals

Robotic Framework for Requirement Management, Estimations and Project Proposals

CDM-MPC: An Integrated Dynamic Planning and Control Framework for Bipedal Robots Jumping

CDM-MPC: An Integrated Dynamic Planning and Control Framework for Bipedal Robots Jumping

Stable Rapid Sagittal Walking Control for Bipedal Robot Using Passive Tendon

This paper presents the development, control, and experimental validation of a novel bipedal robot with a passive tendon. The robot, featuring foldable legs, coaxial actuation, and compact folded size, is endowed with a leg configuration with a five-bar mechanism. Based on biological observations of human walking, a passive artificial tendon made of emulsion is fabricated to work in conjunction with a tensioning device, providing adaptive heel touchdown and toe push-off in sync with single-leg movement. The tailored control framework for the bipedal robot is further established with the double-layer architecture. The regulation layer employs the linear inverted pendulum (LIP) model to generate reference trajectory of the center of mass (CoM) with a dead-beat style of parameter adjustment. An inverse-dynamics-based whole-body controller (WBC) is applied to enforce the full-order dynamics of the bipedal robot to reproduce the LIP model’s behavior. We carry out the experiments on the physical prototype to evaluate the walking performance of the developed bipedal robot. The results show that the robot achieves stable walking at the speed of 0.8 m/s (almost twice the leg length/s) and exhibits robustness to external push disturbance.

Large language model-based code generation for the control of construction assembly robots: A hierarchical generation approach

Offline programming (OLP) is a mainstream approach for controlling assembly robots at construction sites. However, existing methods are tailored to specific assembly tasks and workflows, and thus lack flexibility. Additionally, the emerging large language model (LLM)-based OLP cannot effectively handle the code logic of robot programming. Thus, this paper addresses the question: How can robot control programs be generated effectively and accurately for diverse construction assembly tasks using LLM techniques? This paper describes a closed user-on-the-loop control framework for construction assembly robots based on LLM techniques. A hierarchical strategy to generate robot control programs is proposed to logically integrate code generation at high and low levels. Additionally, customized application programming interfaces and a chain of action are combined to enhance the LLM's understanding of assembly action logic. An assembly task set was designed to evaluate the feasibility and reliability of the proposed approach. The results show that the proposed approach (1) is widely applicable to diverse assembly tasks, and (2) can improve the quality of the generated code by decreasing the number of errors. Our approach facilitates the automation of construction assembly tasks by simplifying the robot control process.

Biped Robots Control in Gusty Environments with Adaptive Exploration Based DDPG.

As technology rapidly evolves, the application of bipedal robots in various environments has widely expanded. These robots, compared to their wheeled counterparts, exhibit a greater degree of freedom and a higher complexity in control, making the challenge of maintaining balance and stability under changing wind speeds particularly intricate. Overcoming this challenge is critical as it enables bipedal robots to sustain more stable gaits during outdoor tasks, thereby increasing safety and enhancing operational efficiency in outdoor settings. To transcend the constraints of existing methodologies, this research introduces an adaptive bio-inspired exploration framework for bipedal robots facing wind disturbances, which is based on the Deep Deterministic Policy Gradient (DDPG) approach. This framework allows the robots to perceive their bodily states through wind force inputs and adaptively modify their exploration coefficients. Additionally, to address the convergence challenges posed by sparse rewards, this study incorporates Hindsight Experience Replay (HER) and a reward-reshaping strategy to provide safer and more effective training guidance for the agents. Simulation outcomes reveal that robots utilizing this advanced method can more swiftly explore behaviors that contribute to stability in complex conditions, and demonstrate improvements in training speed and walking distance over traditional DDPG algorithms.