Robotics Challenge Research Articles

Enhancing navigation performance in unknown environments using spiking neural networks and reinforcement learning with asymptotic gradient method

Achieving accurate and generalized autonomous navigation in unknown environments poses a significant challenge in robotics and artificial intelligence. Animals exhibits superlative navigation capabilities by combining the representation of internal neurals and sensory cues of self-motion and external information. This paper proposes a brain-inspired navigation method based upon the spiking neural networks (SNN) and reinforcement learning, integrated with a lidar system that serves as the local environment explorer, by which realizes high performance of obstacle avoidance and target arrival in mapless circumstances. An asymptotic gradient method is introduced to optimize the backpropagation during training, which facilitates the improvement of model robustness. The results of our experiments conducted on the Gazebo platform showcase how our approach effectively improves navigation performance in various intricate environments. Our approach yielded a higher success navigation rate ranging from 2% to 5%, depending on the SNN timesteps. Considering the inherent lower computational cost of SNN, this work contributes to advancing the fusion of SNN and reinforcement learning techniques for energy-efficient autonomous navigation tasks in real-world mapless scenarios.

The Future of Parenthood? Examining the Promise and Complexity of Pregnancy Robots in Reproductive Health.

Advancements in reproductive technology are now approaching an unprecedented frontier: the pregnancy robot, a potential artificial womb capable of carrying a fetus from fertilization to birth. This innovation, by simulating the natural uterine environment, could redefine pregnancy and parenthood, offering transformative benefits for maternal and infant health. The pregnancy robot promises safer pathways for individuals with medical risks, LGBTQ + couples, and single parents, while also reducing the risks of complications like preeclampsia and preterm birth. It builds on the foundations laid by assisted reproductive technologies (ART), aiming to make the process more accessible and inclusive. However, the introduction of such technology brings with it complex ethical and social questions, including potential impacts on maternal-child bonding, gender roles, and societal norms surrounding motherhood. Further concerns revolve around equitable access, as socioeconomic divides may restrict this technology to those with financial resources, and the risk of cultural and religious opposition. Legal, regulatory, and environmental considerations must also be addressed to responsibly integrate this technology. This article discusses the profound promise and ethical challenges of pregnancy robots, highlighting the need for thoughtful implementation and collaborative dialogue to ensure that the technology, if realized, can benefit all facets of society and pave the way for a more inclusive future in reproductive health.

Advancements and challenges in medical and healthcare robotics

Advancements and challenges in medical and healthcare robotics

Application of Improved Genetic Algorithms in Path Planning

Genetic algorithm, as a heuristic optimization technique, has achieved remarkable results in various fields, including path planning. Path planning, a fundamental challenge in automation systems and robotics, involves finding the optimal route from a starting point to a destination. This paper focuses on the application of improved genetic algorithms in path planning. Firstly, a brief overview of the fundamental principles of traditional genetic algorithms is provided, including operations such as individual encoding, selection, crossover, and mutation. Subsequently, a thorough exploration is conducted into how improved genetic algorithms can be introduced to address path planning problems. These enhancements encompass optimized population initialization strategies, novel genetic operation methods, and more effective fitness function designs. Furthermore, the discussion extends to how these improvements contribute to accelerating convergence speed, enhancing global search capabilities, and elevating the quality of solution outcomes. A substantial number of objective experiments have demonstrated the effectiveness of our approach.

Latissimus-dorsi-inspired wire mechanism for improving energy efficiency of quadruped robot locomotion

A spine, flexible structure in the trunk, improves the locomotion energy efficiency of the quadruped robot. However, improving energy efficiency remains a significant challenge for quadruped robots seeking long-duration operations. This study introduces a wire mechanism that converts active spine flexion into the forelimb propulsive motion, improving energy efficiency during locomotion through spine flexion-extension shape change. Quasi-static force simulations and static force measurements with a developed robot confirm that the proposed mechanism increases the force generated at the forelimb foot. Furthermore, robot-running experiments demonstrates that the robot with the proposed mechanism runs more energy efficiently. These results support the proposed mechanism's effectiveness in improving energy efficiency in locomotion with spine flexion-extension shape change.

SNGNN2D-v2: A GNN-Based Model for the Generation of Human-Aware Cost Maps in Dynamic Environments

AbstractNavigating dynamic, human-populated environments is a critical challenge for mobile robots, as they must balance effective pathfinding with minimizing social disruption. Cost maps can combine information from different nature and are more interpretable than final control signals. This paper addresses the generation of real-time cost maps in human-aware navigation (HAN) by introducing SNGNN2D-v2, a graph neural network designed and trained to capture social interactions and respond to dynamic elements in human-populated environments. SNGNN2D-v2 is evaluated through three types of experiments. The first involves deploying a real robot in a controlled indoor environment and assessing the disturbance caused by the robot when driven by the model. The second experiment tests the proposed model under more complex and unfavorable conditions using simulated environments. Both experiments include a comparison with other proposals using social and navigation metrics. The third experiment compares SNGNN2D-v2 with an end-to-end CNN-based method to evaluate how models generalize across changes in the appearance of the environment and its elements. The results from these experiments suggest that SNGNN2D-v2 is an effective model for human-aware cost map generation for dynamic environments. Its ability to capture dynamic information, generalize across scenarios with different appearances, and represent social interactions could contribute to the development of human-friendly robots.

LR-SLAM: Visual Inertial SLAM System with Redundant Line Feature Elimination

The present study focuses on the simultaneous localization and mapping (SLAM) system based on point and line features. Aiming to address the prevalent issue of repeated detection during line feature extraction in low-texture environments, a novel method for merging redundant line features is proposed. This method effectively mitigates the problem of increased initial pose estimation error that arises when the same line is erroneously detected as multiple lines in adjacent frames. Furthermore, recognizing the potential for the introduction of line features to prolong the marginalization process of the information matrix, optimization strategies are employed to accelerate this process. Additionally, to tackle the issue of insufficient point feature accuracy, subpixel technology is introduced to enhance the precision of point features, thereby further reducing errors. Experimental results on the European Robotics Challenge (EUROC) public dataset demonstrate that the proposed LR-SLAM system exhibits significant advantages over mainstream SLAM systems such as ORB-SLAM3, VINS-Mono, and PL-VIO in terms of accuracy, efficiency, and robustness.

Non-holonomic constraints-based adaptive trajectory tracking controller for a multi-joint snake robot

The multi-joint snake robot is poised to become a crucial asset for investigation, surveillance, and attack in national defense and military applications. This paper addresses the trajectory tracking challenge of a multi-joint snake robot characterized by high redundancy and multiple degrees of freedom in the plane. We propose an adaptive trajectory tracking controller that accounts for non-holonomic constraints. This controller estimates unknown environmental parameters, effectively mitigating the negative impacts of uncertain and time-varying conditions during robot movement, thereby ensuring stability. A suitable Lyapunov function is identified to confirm the controller’s stability. The trajectory tracking performance of the robot is thoroughly analyzed through simulations and experiments, demonstrating the effectiveness of the proposed adaptive controller.

Integrating Historical Learning and Multi-View Attention with Hierarchical Feature Fusion for Robotic Manipulation.

Humans typically make decisions based on past experiences and observations, while in the field of robotic manipulation, the robot's action prediction often relies solely on current observations, which tends to make robots overlook environmental changes or become ineffective when current observations are suboptimal. To address this pivotal challenge in robotics, inspired by human cognitive processes, we propose our method which integrates historical learning and multi-view attention to improve the performance of robotic manipulation. Based on a spatio-temporal attention mechanism, our method not only combines observations from current and past steps but also integrates historical actions to better perceive changes in robots' behaviours and their impacts on the environment. We also employ a mutual information-based multi-view attention module to automatically focus on valuable perspectives, thereby incorporating more effective information for decision-making. Furthermore, inspired by human visual system which processes both global context and local texture details, we have devised a method that merges semantic and texture features, aiding robots in understanding the task and enhancing their capability to handle fine-grained tasks. Extensive experiments in RLBench and real-world scenarios demonstrate that our method effectively handles various tasks and exhibits notable robustness and adaptability.

A Comprehensive Review of Mobile Robot Navigation Using Deep Reinforcement Learning Algorithms in Crowded Environments

Navigation is a crucial challenge for mobile robots. Currently, deep reinforcement learning has attracted considerable attention and has witnessed substantial development owing to its robust performance and learning capabilities in real-world scenarios. Scientists leverage the advantages of deep neural networks, such as long short-term memory, recurrent neural networks, and convolutional neural networks, to integrate them into mobile robot navigation based on deep reinforcement learning. This integration aims to enhance the robot's motion control performance in both static and dynamic environments. This paper illustrates a comprehensive survey of deep reinforcement learning methods applied to mobile robot navigation systems in crowded environments, exploring various navigation frameworks based on deep reinforcement learning and their benefits over traditional simultaneous localization and mapping-based frameworks. Subsequently, we comprehensively compare and analyze the relationships and differences among three types of navigation: autonomous-based navigation, navigation based on simultaneous localization and mapping, and planning-based navigation. Moreover, the crowded environment includes static, dynamic, and a combination of obstacles in different typical application scenarios. Finally, we offer insights into the evolution of navigation based on deep reinforcement learning, addressing the problems and providing potential solutions associated with this emerging field.

Robotics in Disaster Management: AI-Driven Rescue, Aerial Mapping, and Emerging IoT Applications for Effective Response

This review paper explores the growing role of robotics in disaster management, focusing on the integration of artificial intelligence (AI), Internet of Things (IoT) applications, and advanced simulation environments. Highlighting lessons from events like the Fukushima nuclear disaster and Wenchuan earthquake, it examines the deployment of aerial, ground, and underwater robots in tasks such as data collection, search and rescue, and hazard monitoring. AIoT frameworks enable these robotic systems to classify objects and make decisions on-site, supporting timely and efficient responses in hazardous conditions. Virtual platforms like the DARPA Virtual Robotics Challenge, using simulators such as Gazebo and CloudSim, facilitate realistic testing of robotic capabilities in disaster scenarios, advancing operational readiness through virtual training. This review synthesizes recent progress in AI-driven disaster robotics, identifies challenges, and discusses potential improvements for future applications.

Multi-objective path planning for mobile robots in nuclear radiation environment

Abstract To minimize the harm of nuclear radiation in hazardous environments, employing mobile robots as substitutes for human workers is of significant practical importance. To plan an optimal path in a nuclear radiation environment, we developed a multi-objective path planning (MOPP) model to facilitate the integration of algorithmic improvements and experimental validation. An improved ant colony optimization algorithm (IACO-A*-M) is proposed, considering path length, cumulative radiation dose, and energy consumption during the movement process to address the path planning challenge for mobile robots in such environments. The approach begins with optimizing the initial pheromone distribution and heuristic function. Next, an improved A* algorithm is employed to estimate the MOPP path, enhancing pheromone levels along this path. Additionally, an elite ant strategy is applied in each iteration to accelerate convergence and improve search quality. Finally, a secondary path planning step is performed in each iteration to further shorten the path and minimize the number of turns. Simulation experiments conducted in MATLAB R2023a demonstrate that the IACO-A*-M algorithm significantly reduces the accumulated radiation dose, energy consumption, and multi-objective function values compared to ACO, IACO and IACO-A*. These results highlight the effectiveness of the IACO-A*-M algorithm in planning more efficient and rational paths in radiation environments.

Efficient 3D Exploration with Distributed Multi-UAV Teams: Integrating Frontier-Based and Next-Best-View Planning

Autonomous exploration of unknown environments poses many challenges in robotics, particularly when dealing with vast and complex landscapes. This paper presents a novel framework tailored for distributed multi-robot systems, harnessing the 3D mobility capabilities of Unmanned Aerial Vehicles (UAVs) equipped with advanced LiDAR sensors for the rapid and effective exploration of uncharted territories. The proposed approach uniquely integrates the robustness of frontier-based exploration with the precision of Next-Best-View (NBV) planning, supplemented by a distance-based assignment cooperative strategy, offering a comprehensive and adaptive strategy for these systems. Through extensive experiments conducted across distinct environments using up to three UAVs, the efficacy of the exploration planner and cooperative strategy is rigorously validated. Benchmarking against existing methods further underscores the superiority of the proposed approach. The results demonstrate successful navigation through complex 3D landscapes, showcasing the framework’s capability in both single- and multi-UAV scenarios. While the benefits of employing multiple UAVs are evident, exhibiting reductions in exploration time and individual travel distance, this study also reveals findings regarding the optimal number of UAVs, particularly in smaller and wider environments.

Adaptive FPGA-Based Accelerators for Human-Robot Interaction in Indoor Environments.

This study addresses the challenges of human-robot interactions in real-time environments with adaptive field-programmable gate array (FPGA)-based accelerators. Predicting human posture in indoor environments in confined areas is a significant challenge for service robots. The proposed approach works on two levels: the estimation of human location and the robot's intention to serve based on the human's location at static and adaptive positions. This paper presents three methodologies to address these challenges: binary classification to analyze static and adaptive postures for human localization in indoor environments using the sensor fusion method, adaptive Simultaneous Localization and Mapping (SLAM) for the robot to deliver the task, and human-robot implicit communication. VLSI hardware schemes are developed for the proposed method. Initially, the control unit processes real-time sensor data through PIR sensors and multiple ultrasonic sensors to analyze the human posture. Subsequently, static and adaptive human posture data are communicated to the robot via Wi-Fi. Finally, the robot performs services for humans using an adaptive SLAM-based triangulation navigation method. The experimental validation was conducted in a hospital environment. The proposed algorithms were coded in Verilog HDL, simulated, and synthesized using VIVADO 2017.3. A Zed-board-based FPGA Xilinx board was used for experimental validation.

Towards a Unified Framework for Software-Hardware Integration in Evolutionary Robotics

The discrepancy between simulated and hardware experiments, the reality gap, is a challenge in evolutionary robotics. While strategies have been proposed to address this gap in fixed-body robots, they are not viable when dealing with populations and generations where the body is in constant change. The continual evolution of body designs necessitates the manufacturing of new robotic structures, a process that can be time-consuming if carried out manually. Moreover, the increased manufacturing time not only prolongs hardware experimental durations but also disrupts the synergy between hardware and simulated experiments. Failure to effectively manage these challenges could impede the implementation of evolutionary robotics in real-life environments. The Autonomous Robot Evolution project presents a framework to tackle these challenges through a case study. This paper describes the main three contributions of this work: Firstly, it analyses the different reality gap experienced by each different robot or the heterogenous reality gap. Secondly, it emphasizes the importance of automation in robot manufacturing. And thirdly, it highlights the necessity of a framework to orchestrate the synergy between simulated and hardware experiments. In the long term, integrating these contributions into evolutionary robotics is envisioned to enable the continuous production of robots in real-world environments.

Advancements in amphibious robot navigation through wheeled odometer uncertainty extension and distributed information fusion

The advancement and safeguarding of the water-land interface region is of paramount importance, and amphibious robots with the capacity for autonomous operation can play a pivotal role in this domain. However, the inability of the majority of reliable navigation sensors to adapt to the water-land interface environment presents a significant challenge for amphibious robots, as obtaining positional information is crucial for autonomous operation. To address this issue, we have proposed a positioning and navigation framework, designated as NAWR (Navigation Algorithm for Amphibious Wheeled Robots), with the objective of enhancing the navigation capabilities of amphibious robots. Firstly, a method for representing the odometer's confidence based on a simplified wheel-terrain interaction model has been developed. This method quantitatively assesses the reliability of each odometer by estimating the slip rate. Secondly, we have introduced an improved split covariance intersection filter (I-SCIF), which maximizes the utilization of navigation information sources to enhance the accuracy of positional estimation. Finally, we will integrate these two methods to form the NAWR framework and validate the effectiveness of the proposed methods through multiple robot field trials. The results from both field trials and ablation tests collectively demonstrate that the modules and overall approach within the NAWR framework effectively enhance the navigation capabilities of amphibious robots.

Trajectory optimization and obstacle avoidance of autonomous robot using Robust and Efficient Rapidly Exploring Random Tree.

One of the key challenges in robotics is the motion planning problem. This paper presents a local trajectory planning and obstacle avoidance strategy based on a novel sampling-based path-finding algorithm designed for autonomous vehicles navigating complex environments. Although sampling-based algorithms have been extensively employed for motion planning, they have notable limitations, such as sluggish convergence rate, significant search time volatility, a vast, dense sample space, and unsmooth search routes. To overcome the limitations, including slow convergence, high computational complexity, and unnecessary search while sampling the whole space, we have proposed the RE-RRT* (Robust and Efficient RRT*) algorithm. This algorithm adapts a new sampling-based path-finding algorithm based on sampling along the displacement from the initial point to the goal point. The sample space is constrained during each stage of the random tree's growth, reducing the number of redundant searches. The RE-RRT* algorithm can converge to a shorter path with fewer iterations. Furthermore, the Choose Parent and Rewire processes are used by RE-RRT* to improve the path in succeeding cycles continuously. Extensive experiments under diverse obstacle settings are performed to validate the effectiveness of the proposed approach. The results demonstrate that the proposed approach outperforms existing methods in terms of computational time, sampling space efficiency, speed, and stability.

Learning to Play Guitar with Robotic Hands

AbstractPlaying the guitar is a dexterous human skill that poses significant challenges in computer graphics and robotics due to the precision required in finger positioning and coordination between hands. Current methods often rely on motion capture data to replicate specific guitar playing segments, which restricts the range of performances and demands intricate post‐processing. In this paper, we introduce a novel reinforcement learning model that can play the guitar using robotic hands, without the need for motion capture datasets, from input tablatures. To achieve this, we divide the simulation task for playing guitar into three stages. (a): for an input tablature, we first generate corresponding fingerings that align with human habits. (b): based on the generated fingerings as the guidance, we train a neural network for controlling the fingers of the left hand using deep reinforcement learning, and (c): we generate plucking movements for the right hand based on inverse kinematics according to the tablature. We evaluate our method by employing precision, recall, and F1 scores as quantitative metrics to thoroughly assess its performance in playing musical notes. In addition, we conduct qualitative analysis through user studies to evaluate the visual and auditory effects of guitar performance. The results demonstrate that our model excels in playing most moderately difficult and easier musical pieces, accurately playing nearly all notes.

Addressing cybersecurity challenges in robotics: A comprehensive overview

As robotics technology becomes increasingly integrated into various sectors, ensuring the cybersecurity of robotic systems is paramount. This article provides an in-depth exploration of the cybersecurity challenges confronting robotics and offers strategies to address these concerns. With the growing connectivity and networking capabilities of robots, vulnerabilities such as unauthorized access, data breaches, and network attacks are significant threats [1]. Protecting sensitive data collected and processed by robots is crucial to preserving privacy and trust. Remote access features, while enhancing operational flexibility, also pose security risks if not adequately secured. Weak authentication mechanisms and insecure interfaces could allow malicious actors to compromise robot functionality. Furthermore, robots are susceptible to malware and cyber-attacks, including viruses, worms, and ransomware. To mitigate these risks, a comprehensive approach is necessary, incorporating secure design principles, robust authentication mechanisms, encryption techniques, and cybersecurity training. Collaboration among industry stakeholders, researchers, policymakers, and cybersecurity experts is essential to develop resilient robotic systems capable of withstanding evolving cyber threats. This article underscores the importance of addressing cybersecurity challenges in robotics to ensure the safety and security of robotic deployments across diverse domains. As robotics technology evolves and becomes integral across various sectors, prioritizing cybersecurity [2] is crucial to protect these systems from unauthorized access, data breaches, and network attacks. The interconnected nature and remote access features of robots pose significant vulnerabilities. Comprehensive measures, including secure design, encryption, and cybersecurity training, are essential. Collaboration among industry stakeholders, researchers, policymakers, and cybersecurity experts is vital for developing resilient robotic systems. This article highlights the urgent need to address cybersecurity challenges to ensure the safety and integrity of robotic deployments.

RoboMan: An Adult-Sized Humanoid Robot with Enhanced Performance, Inherent Stability, and Two-Stage Balance Control to Facilitate Research on Humanoids

Creating an adult-sized humanoid robot with stable walking capabilities is a major challenge in robotics. While many renowned research groups focus on robots for perilous work environments and precision tasks, our approach simplifies balance control, making it accessible to robotics research groups and educational institutes. This facilitates the development of complex functionalities such as vision and object manipulation for adult-sized humanoids. This research article introduces RoboMan II, an advanced version of RoboMan I, which won the most prestigious award in all humanoid robot leagues at RoboCup 2016 due to its exceptional performance in walking and playing soccer. RoboMan II features significant improvements in performance, inherent stability, recovery after falls, and balance control. To facilitate its development, RoboMan II is lighter and incorporates a modified foot and parallel structure for its leg to boost its inherent stability, along with a two-stage balance control system for Immediate Response and Gradual Adaptation, enhancing its adaptability in various environments. Our simulation results demonstrate that RoboMan II’s walking stability on flat surfaces improved significantly in the face of minor perturbations, with the number of steps within the stable region increasing from 24%, with only the immediate controller to 58% when both controllers were used. Similar improvements were observed on inclined surfaces. Additionally, the 3D CAD files for all of the robot parts are released as open source in conjunction with this paper to facilitate reproduction and further innovation. The forthcoming RoboMan III will incorporate custom servo motors for increased speed, torque, and enhanced fall recovery, preventing disengagement of the gear box after a fall. It promises to be an invaluable asset for research and practical applications in humanoid robotics.