Pub Date : 2025-01-11DOI: 10.1016/j.robot.2025.104915
Khan Raqib Mahmud , Lingxiao Wang , Sunzid Hassan , Zheng Zhang
Robotic Odor Source Localization (OSL) technology enables mobile robots to detect and navigate unknown odor sources in diverse environments. Traditional OSL methods, including bio-inspired, engineering-based, and machine learning-based approaches, face limitations of lack of adaptability to varying environments, significant computational resource requirements, and dependence on historical data. To overcome these challenges, we present a knowledge-driven framework that leverages Large Language Models (LLMs) to improve the robot’s navigation capabilities through contextual understanding and informed decision-making. A key feature of the proposed work is integrating an LLM agent with a memory module, which stores past experiences and recalls them during the decision-making process, allowing the robotic agent to make decisions based on current sensory inputs and previously acquired knowledge. Compared to traditional deep learning-based methods, such as Deep Q-Network (DQN), both simulation and real-world experiment results demonstrate that our framework significantly outperforms it in terms of accuracy, efficiency, and generalization across different environmental conditions.
{"title":"A knowledge-driven framework for Robotic Odor Source Localization using large language models","authors":"Khan Raqib Mahmud , Lingxiao Wang , Sunzid Hassan , Zheng Zhang","doi":"10.1016/j.robot.2025.104915","DOIUrl":"10.1016/j.robot.2025.104915","url":null,"abstract":"<div><div>Robotic Odor Source Localization (OSL) technology enables mobile robots to detect and navigate unknown odor sources in diverse environments. Traditional OSL methods, including bio-inspired, engineering-based, and machine learning-based approaches, face limitations of lack of adaptability to varying environments, significant computational resource requirements, and dependence on historical data. To overcome these challenges, we present a knowledge-driven framework that leverages Large Language Models (LLMs) to improve the robot’s navigation capabilities through contextual understanding and informed decision-making. A key feature of the proposed work is integrating an LLM agent with a memory module, which stores past experiences and recalls them during the decision-making process, allowing the robotic agent to make decisions based on current sensory inputs and previously acquired knowledge. Compared to traditional deep learning-based methods, such as Deep Q-Network (DQN), both simulation and real-world experiment results demonstrate that our framework significantly outperforms it in terms of accuracy, efficiency, and generalization across different environmental conditions.</div></div>","PeriodicalId":49592,"journal":{"name":"Robotics and Autonomous Systems","volume":"186 ","pages":"Article 104915"},"PeriodicalIF":4.3,"publicationDate":"2025-01-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143096376","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2025-01-06DOI: 10.1016/j.robot.2024.104910
Amit Raj , Kapil Ahuja , Yann Busnel
This paper explores the application of Artificial Intelligence (AI) techniques for generating the trajectories of massive swarm of Unmanned Aerial Vehicles (UAVs). The two main challenges addressed include accurately predicting the trajectories of UAVs and efficiently avoiding collisions between them, which we discuss in the two paragraphs below, respectively.
In previous works that did trajectory predictions, a Neural Network (NN) was used. The activation functions used were all standard like Sigmoid, Tanh, and ReLU, which resulted in low trajectory prediction accuracy. In this work, we apply application-oriented activation functions of Swish and Elliott that are known to be resilient to noisy data, which is common in UAV trajectory prediction. We also propose our new activation function, AdaptoSwelliGauss that is fusion of Swish, Elliott and a scaled and shifted Gaussian. This combination better captures the complexities of UAV trajectory prediction (noisy data as well as non-linear trajectory). The trajectory prediction accuracy obtained with our new activation function is three to four orders-of-magnitude better than that obtained from the standard activation functions.
In the UAV context, collision detection and avoidance of UAVs is of utmost importance. While there is a common standard for collision detection, collision avoidance can be done by multiple methods. The first method is by changing their trajectories and the second method is by changing their start times (called batching). The previous works on the trajectory change method were designed for small sets of UAVs. Applying these to our setup of massive UAVs leads to smooth but convoluted paths (including endless loops). On other hand, when we apply the batching method to our setup, then the number of batches is large delaying the launch of all UAVs. Therefore, in this paper we propose a novel collision avoidance strategy that combines a new trajectory change method with the batching method. This results in smooth, simple, and finite trajectory changes in the first method, and reduction-by-half in the number of batches in the second method.
{"title":"AI algorithm for predicting and optimizing trajectory of massive UAV swarm","authors":"Amit Raj , Kapil Ahuja , Yann Busnel","doi":"10.1016/j.robot.2024.104910","DOIUrl":"10.1016/j.robot.2024.104910","url":null,"abstract":"<div><div>This paper explores the application of Artificial Intelligence (AI) techniques for generating the trajectories of massive swarm of Unmanned Aerial Vehicles (UAVs). The two main challenges addressed include accurately predicting the trajectories of UAVs and efficiently avoiding collisions between them, which we discuss in the two paragraphs below, respectively.</div><div>In previous works that did trajectory predictions, a Neural Network (NN) was used. The activation functions used were all standard like Sigmoid, Tanh, and ReLU, which resulted in low trajectory prediction accuracy. In this work, we apply application-oriented activation functions of Swish and Elliott that are known to be resilient to noisy data, which is common in UAV trajectory prediction. We also propose our new activation function, AdaptoSwelliGauss that is fusion of Swish, Elliott and a scaled and shifted Gaussian. This combination better captures the complexities of UAV trajectory prediction (noisy data as well as non-linear trajectory). The trajectory prediction accuracy obtained with our new activation function is three to four orders-of-magnitude better than that obtained from the standard activation functions.</div><div>In the UAV context, collision detection and avoidance of UAVs is of utmost importance. While there is a common standard for collision detection, collision avoidance can be done by multiple methods. The first method is by changing their trajectories and the second method is by changing their start times (called batching). The previous works on the trajectory change method were designed for small sets of UAVs. Applying these to our setup of massive UAVs leads to smooth but convoluted paths (including endless loops). On other hand, when we apply the batching method to our setup, then the number of batches is large delaying the launch of all UAVs. Therefore, in this paper we propose a novel collision avoidance strategy that combines a new trajectory change method with the batching method. This results in smooth, simple, and finite trajectory changes in the first method, and reduction-by-half in the number of batches in the second method.</div></div>","PeriodicalId":49592,"journal":{"name":"Robotics and Autonomous Systems","volume":"186 ","pages":"Article 104910"},"PeriodicalIF":4.3,"publicationDate":"2025-01-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143096372","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2025-01-06DOI: 10.1016/j.robot.2024.104914
Zhao Wang , Xianbo Xiang , Xinyang Xiong , Shaolong Yang
In this paper, a position-based acoustic visual servo control scheme is proposed to achieve efficient docking for underactuated autonomous underwater vehicle (AUV). To address the issue that docking station can hardly be kept in the limited field of onboard imaging sonar using conventional controllers, deep reinforcement learning (DRL) is proposed to learn an optimal control strategy to achieve both field control and precise docking. First, a visualized underwater docking environment is developed based on robotic operation system (ROS) and Gazebo platform, and imaging sonar is modeled to simulate acoustic image. Subsequently, a deep neural network-inspired detector and a state-of-the-art feature tracker are combined as the perceptual header of docking controller which transforms the output of imaging sonar to the input of control agent rapidly and precisely. Furthermore, cost terms of DRL-based control agents are further designed by incorporating two nonlinear functions with different gradients, yet the change rates of received rewards from the state observation are not same in different situations. In this case, the control agents are enabled to learn the strategy to eliminate offset and keep depth of AUV. Moreover, feature of docking station will also be well kept in acoustic image due to the rapid increasing punishment of great bearing angle when AUV is in high maneuvering. Furthermore, evaluation results and comparative experiments are presented to verify feasibility and efficiency of the proposed servo control scheme using deep reinforcement learning.
{"title":"Position-based acoustic visual servo control for docking of autonomous underwater vehicle using deep reinforcement learning","authors":"Zhao Wang , Xianbo Xiang , Xinyang Xiong , Shaolong Yang","doi":"10.1016/j.robot.2024.104914","DOIUrl":"10.1016/j.robot.2024.104914","url":null,"abstract":"<div><div>In this paper, a position-based acoustic visual servo control scheme is proposed to achieve efficient docking for underactuated autonomous underwater vehicle (AUV). To address the issue that docking station can hardly be kept in the limited field of onboard imaging sonar using conventional controllers, deep reinforcement learning (DRL) is proposed to learn an optimal control strategy to achieve both field control and precise docking. First, a visualized underwater docking environment is developed based on robotic operation system (ROS) and Gazebo platform, and imaging sonar is modeled to simulate acoustic image. Subsequently, a deep neural network-inspired detector and a state-of-the-art feature tracker are combined as the perceptual header of docking controller which transforms the output of imaging sonar to the input of control agent rapidly and precisely. Furthermore, cost terms of DRL-based control agents are further designed by incorporating two nonlinear functions with different gradients, yet the change rates of received rewards from the state observation are not same in different situations. In this case, the control agents are enabled to learn the strategy to eliminate offset and keep depth of AUV. Moreover, feature of docking station will also be well kept in acoustic image due to the rapid increasing punishment of great bearing angle when AUV is in high maneuvering. Furthermore, evaluation results and comparative experiments are presented to verify feasibility and efficiency of the proposed servo control scheme using deep reinforcement learning.</div></div>","PeriodicalId":49592,"journal":{"name":"Robotics and Autonomous Systems","volume":"186 ","pages":"Article 104914"},"PeriodicalIF":4.3,"publicationDate":"2025-01-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143096369","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2025-01-04DOI: 10.1016/j.robot.2024.104908
Mohamed-Khalil Bouzidi , Ehsan Hashemi
An interaction-aware control and motion planning framework is proposed and experimentally verified for time-critical merging scenarios. The framework considers interaction between the automated driving system and other vehicles, including human-driven vehicles, by monitoring lateral and longitudinal response of the neighbor vehicle without communication or having access to their control and safety objective functions. This has not been accounted for safe motion planning and controls in existing merging solutions in mixed traffic. The framework includes a novel inverse differential game based on a long short-term memory network for estimation of the possible path tracking objective function of the human-driven vehicle in real-time. Then, a game-theoretic receding horizon controller is devised for the automated driving system by predicting the trajectory of the human-driven vehicle. The developed framework is validated in several merging scenarios and road surface conditions using CarSim high-fidelity simulations including human-in-the-loop case studies with different test subjects.
{"title":"Interaction-aware motion planning and control based on game theory with human-in-the-loop validation","authors":"Mohamed-Khalil Bouzidi , Ehsan Hashemi","doi":"10.1016/j.robot.2024.104908","DOIUrl":"10.1016/j.robot.2024.104908","url":null,"abstract":"<div><div>An interaction-aware control and motion planning framework is proposed and experimentally verified for time-critical merging scenarios. The framework considers interaction between the automated driving system and other vehicles, including human-driven vehicles, by monitoring lateral and longitudinal response of the neighbor vehicle without communication or having access to their control and safety objective functions. This has not been accounted for safe motion planning and controls in existing merging solutions in mixed traffic. The framework includes a novel inverse differential game based on a long short-term memory network for estimation of the possible path tracking objective function of the human-driven vehicle in real-time. Then, a game-theoretic receding horizon controller is devised for the automated driving system by predicting the trajectory of the human-driven vehicle. The developed framework is validated in several merging scenarios and road surface conditions using <em>CarSim</em> high-fidelity simulations including human-in-the-loop case studies with different test subjects.</div></div>","PeriodicalId":49592,"journal":{"name":"Robotics and Autonomous Systems","volume":"186 ","pages":"Article 104908"},"PeriodicalIF":4.3,"publicationDate":"2025-01-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143096373","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2025-01-02DOI: 10.1016/j.robot.2024.104905
Giuseppe Silano , Alvaro Caballero , Davide Liuzza , Luigi Iannelli , Stjepan Bogdan , Martin Saska
The paper addresses task assignment and trajectory generation for collaborative inspection missions using a fleet of multi-rotors, focusing on the wind turbine inspection scenario. The proposed solution enables safe and feasible trajectories while accommodating heterogeneous time-bound constraints and vehicle physical limits. An optimization problem is formulated to meet mission objectives and temporal requirements encoded as Signal Temporal Logic (STL) specifications. Additionally, an event-triggered replanner is introduced to address unforeseen events and compensate for lost time. Furthermore, a generalized robustness scoring method is employed to reflect user preferences and mitigate task conflicts. The effectiveness of the proposed approach is demonstrated through MATLAB and Gazebo simulations, as well as field multi-robot experiments in a mock-up scenario.
{"title":"A Signal Temporal Logic approach for task-based coordination of multi-aerial systems: A wind turbine inspection case study","authors":"Giuseppe Silano , Alvaro Caballero , Davide Liuzza , Luigi Iannelli , Stjepan Bogdan , Martin Saska","doi":"10.1016/j.robot.2024.104905","DOIUrl":"10.1016/j.robot.2024.104905","url":null,"abstract":"<div><div>The paper addresses task assignment and trajectory generation for collaborative inspection missions using a fleet of multi-rotors, focusing on the wind turbine inspection scenario. The proposed solution enables safe and feasible trajectories while accommodating heterogeneous time-bound constraints and vehicle physical limits. An optimization problem is formulated to meet mission objectives and temporal requirements encoded as Signal Temporal Logic (STL) specifications. Additionally, an event-triggered replanner is introduced to address unforeseen events and compensate for lost time. Furthermore, a generalized robustness scoring method is employed to reflect user preferences and mitigate task conflicts. The effectiveness of the proposed approach is demonstrated through MATLAB and Gazebo simulations, as well as field multi-robot experiments in a mock-up scenario.</div></div>","PeriodicalId":49592,"journal":{"name":"Robotics and Autonomous Systems","volume":"186 ","pages":"Article 104905"},"PeriodicalIF":4.3,"publicationDate":"2025-01-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143096374","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
In this study, a dynamic adaptation of a robot controller is investigated using large language models (LLMs). We propose our controller called the LLM-Controller, where, in response to changes in the system dynamics or reference signals, the LLM adapts the controller to the new context. Various scenarios reflecting real-world conditions, including unknown disturbances, unmodeled dynamics, and changing reference signals, were analyzed. Using the proposed LLM-Controller, one can adapt to new conditions automatically without manual tuning. Additionally, the controller's performance is investigated using different prompting techniques, such as zero-shot and few-shot chain-of-thought (COT), which facilitate step-by-step reasoning and improve adaptation to new contexts. The proposed scheme is applied to two case studies involving robot manipulators. First, it is tested on a 2-link robot manipulator, followed by a 3-link manipulator to enhance its generalizability. The algorithm's adaptability and effectiveness are further evaluated across a range of tasks and conditions, demonstrating its versatility in various scenarios. The results demonstrate that the LLM-Controller achieved a 100 % success rate in adapting the controller to new conditions for the 2-link manipulator, with a significant improvement in trial efficiency; while for the 3-link system, the controller maintained a 90 % success rate, showing greater adaptability to changes in reference signals or dynamic conditions in under 20 s. These outcomes could be further enhanced by employing a COT approach, potentially leading to higher success rates, fewer trials, and optimized costs. In contrast, the classic nonlinear adaptive controller struggled to adjust to the new conditions, while the LLM-Controller automatically adapts, guiding the system to new stable states. This research provides valuable insights into how LLMs can enhance decision-making, improving stability and performance in dynamic and uncertain environments.
{"title":"LLM-controller: Dynamic robot control adaptation using large language models","authors":"Rasoul Zahedifar , Mahdieh Soleymani Baghshah , Alireza Taheri","doi":"10.1016/j.robot.2024.104913","DOIUrl":"10.1016/j.robot.2024.104913","url":null,"abstract":"<div><div>In this study, a dynamic adaptation of a robot controller is investigated using large language models (LLMs). We propose our controller called the LLM-Controller, where, in response to changes in the system dynamics or reference signals, the LLM adapts the controller to the new context. Various scenarios reflecting real-world conditions, including unknown disturbances, unmodeled dynamics, and changing reference signals, were analyzed. Using the proposed LLM-Controller, one can adapt to new conditions automatically without manual tuning. Additionally, the controller's performance is investigated using different prompting techniques, such as zero-shot and few-shot chain-of-thought (COT), which facilitate step-by-step reasoning and improve adaptation to new contexts. The proposed scheme is applied to two case studies involving robot manipulators. First, it is tested on a 2-link robot manipulator, followed by a 3-link manipulator to enhance its generalizability. The algorithm's adaptability and effectiveness are further evaluated across a range of tasks and conditions, demonstrating its versatility in various scenarios. The results demonstrate that the LLM-Controller achieved a 100 % success rate in adapting the controller to new conditions for the 2-link manipulator, with a significant improvement in trial efficiency; while for the 3-link system, the controller maintained a 90 % success rate, showing greater adaptability to changes in reference signals or dynamic conditions in under 20 s. These outcomes could be further enhanced by employing a COT approach, potentially leading to higher success rates, fewer trials, and optimized costs. In contrast, the classic nonlinear adaptive controller struggled to adjust to the new conditions, while the LLM-Controller automatically adapts, guiding the system to new stable states. This research provides valuable insights into how LLMs can enhance decision-making, improving stability and performance in dynamic and uncertain environments.</div></div>","PeriodicalId":49592,"journal":{"name":"Robotics and Autonomous Systems","volume":"186 ","pages":"Article 104913"},"PeriodicalIF":4.3,"publicationDate":"2025-01-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143096375","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2025-01-02DOI: 10.1016/j.robot.2024.104906
Jiange Kou , Yixuan Wang , Yan Shi , Shaofeng Xu , Haoran Zhan , Qing Guo
Lower limb exoskeleton is a typical wearable robot to assist human motion with physiological power improvement. The active mode experiments based on the constant admittance parameters are carried out to acquire the original data. Then the fast fourier transform(FFT) together with linear fitting methods are used to process the original data and to obtain the optimal admittance parameters with different step frequencies. A variable admittance controller is adopted to implement the active follow-up control of exoskeleton to deal with the time-varying step frequency, which means that the operator’s motion is motivated by his/her intention. Meanwhile, the exoskeleton control tries best to improve the wearable comfortable performance of human–exoskeleton system. The effectiveness of the proposed control scheme is verified by both the comparative simulations and experimental results of the human–exoskeleton cooperative motion.
{"title":"Active control strategy of lower limb exoskeleton based on variable admittance control","authors":"Jiange Kou , Yixuan Wang , Yan Shi , Shaofeng Xu , Haoran Zhan , Qing Guo","doi":"10.1016/j.robot.2024.104906","DOIUrl":"10.1016/j.robot.2024.104906","url":null,"abstract":"<div><div>Lower limb exoskeleton is a typical wearable robot to assist human motion with physiological power improvement. The active mode experiments based on the constant admittance parameters are carried out to acquire the original data. Then the fast fourier transform(FFT) together with linear fitting methods are used to process the original data and to obtain the optimal admittance parameters with different step frequencies. A variable admittance controller is adopted to implement the active follow-up control of exoskeleton to deal with the time-varying step frequency, which means that the operator’s motion is motivated by his/her intention. Meanwhile, the exoskeleton control tries best to improve the wearable comfortable performance of human–exoskeleton system. The effectiveness of the proposed control scheme is verified by both the comparative simulations and experimental results of the human–exoskeleton cooperative motion.</div></div>","PeriodicalId":49592,"journal":{"name":"Robotics and Autonomous Systems","volume":"186 ","pages":"Article 104906"},"PeriodicalIF":4.3,"publicationDate":"2025-01-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143096371","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2025-01-01DOI: 10.1016/j.robot.2024.104907
Julian Rascon Enriquez , Bernardino Castillo-Toledo , Stefano Di Gennaro , Luis Arturo García-Delgado
This research focuses on developing a navigation method for mobile robots to effectively avoid moving obstacles while accurately tracking a desired path. The approach introduces an enhanced velocity field that incorporates hydrodynamic theory tools. Initially designed for the 2D case, the method is subsequently extended to the 3D scenario by introducing vector field extensions and rotations.
To validate the proposed scheme, experiments are conducted using a UAV model tasked with tracking a circular contour. The control system employs two PD controllers for regulating the vertical position () and yaw angle (), while the roll () and pitch () angles are controlled using a nested saturation method.
The numerical results demonstrate the successful achievement of the tracking objective, even when a moving obstacle crosses the reference path. Notably, this study considers the scenario where an obstacle approaches the vehicle from behind, which is often overlooked in similar investigations. This aspect is examined in both the 2D and 3D cases.
Subsequently, the proposed navigation method is tested on a quadrotor vehicle, yielding favorable results.
{"title":"An algorithm for dynamic obstacle avoidance applied to UAVs","authors":"Julian Rascon Enriquez , Bernardino Castillo-Toledo , Stefano Di Gennaro , Luis Arturo García-Delgado","doi":"10.1016/j.robot.2024.104907","DOIUrl":"10.1016/j.robot.2024.104907","url":null,"abstract":"<div><div>This research focuses on developing a navigation method for mobile robots to effectively avoid moving obstacles while accurately tracking a desired path. The approach introduces an enhanced velocity field that incorporates hydrodynamic theory tools. Initially designed for the 2D case, the method is subsequently extended to the 3D scenario by introducing vector field extensions and rotations.</div><div>To validate the proposed scheme, experiments are conducted using a UAV model tasked with tracking a circular contour. The control system employs two PD controllers for regulating the vertical position (<span><math><mi>z</mi></math></span>) and yaw angle (<span><math><mi>ψ</mi></math></span>), while the roll (<span><math><mi>ϕ</mi></math></span>) and pitch (<span><math><mi>θ</mi></math></span>) angles are controlled using a nested saturation method.</div><div>The numerical results demonstrate the successful achievement of the tracking objective, even when a moving obstacle crosses the reference path. Notably, this study considers the scenario where an obstacle approaches the vehicle from behind, which is often overlooked in similar investigations. This aspect is examined in both the 2D and 3D cases.</div><div>Subsequently, the proposed navigation method is tested on a quadrotor vehicle, yielding favorable results.</div></div>","PeriodicalId":49592,"journal":{"name":"Robotics and Autonomous Systems","volume":"186 ","pages":"Article 104907"},"PeriodicalIF":4.3,"publicationDate":"2025-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143096431","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2024-12-30DOI: 10.1016/j.robot.2024.104902
Zahra Samadikhoshkho, Elliot Saive, Michael G. Lipsett
The challenge of effectively interacting with the environment is a huge obstacle in robotics that needs more attention and innovation. Contact missions, in particular, provide inherent challenges such as adaptability to dynamic environments and ensuring safe and reliable operation. Incorporating compliance into robots is a viable approach to address these challenges, and various methods for doing this are explored in detail in this review. Using compliant mechanisms (CM) is a method that offer a promising solution by providing inherent flexibility and compliance, enabling robots to interact with their environment more effectively. This article provides a comprehensive review on the role of compliant mechanisms in contact robotic applications. It also analyzes several types of these mechanisms, their design approaches, as well as modeling and control strategies.
{"title":"A review of compliant mechanisms for contact robotics applications","authors":"Zahra Samadikhoshkho, Elliot Saive, Michael G. Lipsett","doi":"10.1016/j.robot.2024.104902","DOIUrl":"10.1016/j.robot.2024.104902","url":null,"abstract":"<div><div>The challenge of effectively interacting with the environment is a huge obstacle in robotics that needs more attention and innovation. Contact missions, in particular, provide inherent challenges such as adaptability to dynamic environments and ensuring safe and reliable operation. Incorporating compliance into robots is a viable approach to address these challenges, and various methods for doing this are explored in detail in this review. Using compliant mechanisms (CM) is a method that offer a promising solution by providing inherent flexibility and compliance, enabling robots to interact with their environment more effectively. This article provides a comprehensive review on the role of compliant mechanisms in contact robotic applications. It also analyzes several types of these mechanisms, their design approaches, as well as modeling and control strategies.</div></div>","PeriodicalId":49592,"journal":{"name":"Robotics and Autonomous Systems","volume":"186 ","pages":"Article 104902"},"PeriodicalIF":4.3,"publicationDate":"2024-12-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143096428","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
This paper presents a novel approach to address the challenges of energy-aware task scheduling in a collaborative swarm of robots equipped with energy-harvesting capabilities. With a primary focus on task execution timing, reliable task allocation, and efficient utilization of available energy resources, the task-scheduling process is approached with an energy-aware strategy. The developed architecture employs a centralized and autonomous approach that dynamically responds to a sequence-dependent setup time job shop scheduling demand. The proposed method incorporates energy consumption estimation, charging contingency approach, and energy harvesting prediction to minimize overall task execution time. The problem is optimized using Adaptive Particle Swarm Optimization and compared to other well-known meta-heuristic algorithms. A practical illustration of the proposed approach’s real-world utility is demonstrated through a case study scenario conducted within a heterogeneous pick-drop delivery setting inside a warehouse. The study was conducted utilizing the TurtelBot3 burger robot model within the Robotic Operating System and Gazebo simulation environment. Simulation results demonstrate the superiority of the energy-aware solution for multi-robot scheduling and task allocation problems over the energy-unaware methods by a 15 percent reduction in task completion time.
{"title":"Energy-aware multi-robot task scheduling using meta-heuristic optimization methods for ambiently-powered robot swarms","authors":"Mohmmadsadegh Mokhtari , Parham Haji Ali Mohamadi , Michiel Aernouts , Ritesh Kumar Singh , Bram Vanderborght , Maarten Weyn , Jeroen Famaey","doi":"10.1016/j.robot.2024.104898","DOIUrl":"10.1016/j.robot.2024.104898","url":null,"abstract":"<div><div>This paper presents a novel approach to address the challenges of energy-aware task scheduling in a collaborative swarm of robots equipped with energy-harvesting capabilities. With a primary focus on task execution timing, reliable task allocation, and efficient utilization of available energy resources, the task-scheduling process is approached with an energy-aware strategy. The developed architecture employs a centralized and autonomous approach that dynamically responds to a sequence-dependent setup time job shop scheduling demand. The proposed method incorporates energy consumption estimation, charging contingency approach, and energy harvesting prediction to minimize overall task execution time. The problem is optimized using Adaptive Particle Swarm Optimization and compared to other well-known meta-heuristic algorithms. A practical illustration of the proposed approach’s real-world utility is demonstrated through a case study scenario conducted within a heterogeneous pick-drop delivery setting inside a warehouse. The study was conducted utilizing the TurtelBot3 burger robot model within the Robotic Operating System and Gazebo simulation environment. Simulation results demonstrate the superiority of the energy-aware solution for multi-robot scheduling and task allocation problems over the energy-unaware methods by a 15 percent reduction in task completion time.</div></div>","PeriodicalId":49592,"journal":{"name":"Robotics and Autonomous Systems","volume":"186 ","pages":"Article 104898"},"PeriodicalIF":4.3,"publicationDate":"2024-12-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143096368","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
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