Evo-ROS

Ground-Air coordination is a very complex environment for a machine learning algorithm. We focus on the case where an Unmanned Aerial Vehicle (UAV) needs to support a group of Unmanned Ground Vehicles (UGVs). The UAV is required to broadcast an image that contains all UGVs, thus, offering a bird-eye-view on the group as a whole. The source of complexity in this task is twofold. First, coordination needs to occur without communication between the UAV and UGVs. Second, the ability of the UAV to sense the UGVs is coupled with the ability of the UAV to learn how to track laterally the UGVs and adapt its vertical position so that the images of the UGVs are appropriately spaced within the camera field of view. In this paper, we propose using the Deep Actor Network component of an Actor-Critic Deep Reinforcement Learning architecture as a supervised learner. The advantage of this approach is that it offers a step towards autonomous learning whereby the full Actor-Critic model can be utilized in the future. Human demonstrations are collected for the deep Actor network to learn from. The system is built using the Gazebo Simulator, Robot Operating System, and the OpenAI Gym. We show that the proposed setup is able to train the UAV to follow the UGVs while maintaining all UGVs within camera range in situations where UGVs are performing complex maneuvers.

Robots are an important part of urban search and rescue tasks. World wide attention has been given to developing capable physical platforms that would be beneficial for rescue teams. It is evident that use of multi-robots increases the effectiveness of these systems. The Robot Operating System (ROS) is becoming a standard platform for the robotics research community for both physical robots and simulation environments. Gazebo, with connectivity to the ROS, is a three-dimensional simulation environment that is also becoming a standard. Several simultaneous localization and mapping algorithms are implemented in the ROS; however, there is no multi-robot mapping implementation. In this work, two multi-robot mapping algorithm implementations are presented, namely multi-robot gMapping and multi-robot Hector Mapping. The multi-robot implementations are tested in the Gazebo simulation environment. Also, in order to achieve a more realistic simulation, every incremental robot movement is modeled with rotational and translational noise.

The use of camera-equipped robotic platforms for data collection and visually monitoring applications is exponentially growing. Cluttered construction sites with many objects on the ground are challenging environments for a mobile unmanned ground vehicle (UGV) to navigate. This study presents a mobile UGV equipped with a stereo camera and a robotic arm that can remove obstacles along the UGV’s path. To achieve this, the surrounding environment is captured by the stereo camera and obstacles are detected. The obstacle’s relative location to the UGV is sent to the robotic arm module through robot operating system (ROS). Then, the robotic arm picks up and removes the obstacle. The proposed method will greatly enhance the degree of automation and frequency of data collection for construction monitoring. The results successfully demonstrate the detection and removal of obstacles, serving as one of the enabling factors for developing an autonomous UGV with various construction operating applications.

This research presents a cooperation strategy for a heterogeneous group of robots that comprises two Unmanned Aerial Vehicles (UAVs) and one Unmanned Ground Vehicles (UGVs) to perform tasks in dynamic scenarios. This paper defines specific roles for the UAVs and UGV within the framework to address challenges like partially known terrains and dynamic obstacles. The UAVs are focused on aerial inspections and mapping, while UGV conducts ground-level inspections. In addition, the UAVs can return and land at the UGV base, in case of a low battery level, to perform hot swapping so as not to interrupt the inspection process. This research mainly emphasizes developing a robust Coverage Path Planning (CPP) algorithm that dynamically adapts paths to avoid collisions and ensure efficient coverage. The Wavefront algorithm was selected for the two-dimensional offline CPP. All robots must follow a predefined path generated by the offline CPP. The study also integrates advanced technologies like Neural Networks (NN) and Deep Reinforcement Learning (DRL) for adaptive path planning for both robots to enable real-time responses to dynamic obstacles. Extensive simulations using a Robot Operating System (ROS) and Gazebo platforms were conducted to validate the approach considering specific real-world situations, that is, an electrical substation, in order to demonstrate its functionality in addressing challenges in dynamic environments and advancing the field of autonomous robots.

The recent advances in precision agriculture are due to the emergence of modern robotics systems. For instance, unmanned aerial systems (UASs) give new possibilities that advance the solution of existing problems in this area in many different aspects. The reason is due to these platforms’ ability to perform activities at varying levels of complexity. Therefore, this research presents a multiple-cooperative robot solution for UAS and unmanned ground vehicle (UGV) systems for their joint inspection of olive grove inspect traps. This work evaluated the UAS and UGV vision-based navigation based on a yellow fly trap fixed in the trees to provide visual position data using the You Only Look Once (YOLO) algorithms. The experimental setup evaluated the fuzzy control algorithm applied to the UAS to make it reach the trap efficiently. Experimental tests were conducted in a realistic simulation environment using a robot operating system (ROS) and CoppeliaSim platforms to verify the methodology’s performance, and all tests considered specific real-world environmental conditions. A search and landing algorithm based on augmented reality tag (AR-Tag) visual processing was evaluated to allow for the return and landing of the UAS to the UGV base. The outcomes obtained in this work demonstrate the robustness and feasibility of the multiple-cooperative robot architecture for UGVs and UASs applied in the olive inspection scenario.

The need for efficient automation methods has prompted the rapid development in the field of Robotics. The development of intelligent robots leads to the ability of them becoming an operator highly efficient and able to adapt to a wide range of problems. Still, despite of the several robotic solutions available, the majority of current industrial robots do not use the Robotic Operative System (ROS) and have limitations in terms of autonomously correct errors during their tasks. An important aspect to consider when developing a new robot is the selection of the methodology for recognition of the objects to be handled. In this paper, it will be presented an approach with enough flexibility to be potentially applicable to different scenarios of object recognition and handling normally found in industrial environment. The main aim is for this application to be applied to pick and place routines in robotics. Moreover it will be presented one particular case study that used the proposed approach, the European Robotics Challenges (EUROC) — a challenge aiming to develop a robot for shop floor logistics and manipulation. Our proposed approach is based on the three tiers paradigm: 1-recognition/sensing tier, 2-effector tier and 3-the control tier and was built using the ROS framework. Since ROS is becoming one of the most useful tools in robotics nowadays, the possibility of using a methodology able to be expressed in ROS allows for the development of a standard approach to pick and place operations. Another advantage of our proposed pick and place approach is the ability to have a robot safely and efficiently inserted in an unknown environment. This is possible due to the insertion of an adaptive control tier in our methodology. The proposed approach can be valuable in the field of robotics and can be potentially applied in multiple tasks and it has already allowed us to advance to the next stage of the already mention challenge.

<title>ABSTRACT</title> <p>The objective of this research was to survey and evaluate simulators for use with unmanned ground vehicles and extend the functionality of the ANVEL simulator to include the robot operating system (ROS). The goals of the research were first to determine how the ANVEL simulator would compare to four other currently available simulators on four criteria: physical fidelity, functional fidelity, ease of development, and cost. The second goal was the development of an ANVEL-ROS bridge to expand the robot control functionality in ANVEL. The ANVEL-ROS bridge developed was verified using two robots, Turtlebot2 and Jaguar V4. RViz, a ROS visualization tool, was used to confirm sensor output correctness. Robot control in ANVEL was confirmed using tele-operation through ROS commands.</p>

The purpose of this paper is to offer a unique adaptive path planning framework to address a new challenge known as the Unknown environment Persistent Monitoring Problem (PMP). To identify the unknown events’ occurrence location and likelihood, an unmanned ground vehicle (UGV) equipped with a Light Detection and Ranging (LIDAR) and camera is used to record such events in agriculture land. A certain level of detecting capability must be the distinct monitoring priority in order to keep track of them to a certain distance. First, to formulate a model, we developed an event-oriented modelling strategy for unknown environment perception and the effect is enumerated by uncertainty, which takes into account the sensor’s detection capabilities, the detection interval, and monitoring weight. A mobile robot scheme utilizing LIDAR on integrative approach was created and experiments were carried out to solve the high equipment budget of Simultaneous Localization and Mapping (SLAM) for robotic systems. To map an unfamiliar location using the robotic operating system (ROS), the 3D visualization tool for Robot Operating System (RVIZ) was utilized, and GMapping software package was used for SLAM usage. The experimental results suggest that the mobile robot design pattern is viable to produce a high-precision map while lowering the cost of the mobile robot SLAM hardware. From a decision-making standpoint, we built a hybrid algorithm HSAStar (Hybrid SLAM & A Star) algorithm for path planning based on the event oriented modelling, allowing a UGV to continually monitor the perspectives of a path. The simulation results and analyses show that the proposed strategy is feasible and superior. The performance of the proposed hyb SLAM-A Star-APP method provides 34.95%, 27.38%, 33.21% and 29.68% lower execution time, 26.36%, 29.64% and 29.67% lower map duration compared with the existing methods, such as ACO-APF-APP, APFA-APP, GWO-APP and PSO-APP.

Ocean Worlds Autonomy Testbed for Exploration Research and Simulation (OceanWATERS) is an open-source simulator for developing onboard autonomy software for robotic exploration of ocean worlds, such as Europa, Enceladus, and Titan, built on the Robot Operating System (ROS) and Gazebo simulation environment. Inevitable ground communication delays increase demand for a high degree of autonomy during excavation, collection and transfer of samples to scientific instruments for in-situ analysis. This paper offers a detailed discussion of the robotic arm design and operation for such autonomous surface exploration, taking as reference the Europa Lander mission. The lander arm, which is designed primarily to acquire icy surface and subsurface samples within the arm's workspace, is a 6-degree-of-freedom manipulator with two end effectors: a sample excavation tool and a trenching end-effector. The robotic arm's modes and operations can be summarized as follows: stowed arm, intended as the lander arm default configuration characterized by zero-power consumption; un-stowed arm, target arm configuration after its first deployment; selection and deployment of the end -effector to use next; guarded move, to detect ground level at the desired trenching location; drill ice using the grinder; dig trench at a particular location using the scoop; deliver sample to the sample transfer dock; discard redundant samples. The motion planning tool used for the lander arm is MoveIt, a ROS package. MoveIt uses sampling-based planning and collision checking libraries to determine safe paths. The Rapidly Exploring Random Trees* (RRT*) has been chosen as default planning algorithm as it provides optimal plans with an exponential speed and is guaranteed to find a solution, if feasible solutions exist. Furthermore, this work quantifies and discusses the energy requirements for excavating and collecting samples. In OceanWATERS, force feedback from the terrain, which influences the arm dynamics, is modelled using a discrete element method (DEM) simulation. The DEM and Gazebo software run in parallel and communicate through a co-simulation plugin. This paper presents an analysis and comparison of three DEM open source software (YADE, ESyS-Particle, Project Chrono) for implementation in OceanWATERS and motivates the choice of YADE as most suitable candidate.

In recent years, automation and robotics have played a vital role in improving healthcare efficiency and reducing human workload. The proposed system, “Intelligent Mobile Robotics System for Healthcare and Patient Support,” focuses on developing a low-cost mobile robotic platform capable of performing autonomous navigation and medicine delivery tasks within hospital environments. The project involves designing, simulating, and controlling a medical robot capable of autonomous movement between predefined locations using the Robot Operating System (ROS) and Gazebo simulation environment. The robot is built using an ESP32 microcontroller, TS7960 high-power motor driver, 12V 300 RPM Johnson DC motors, and a 12V Li-ion rechargeable battery pack. The robot’s motion is controlled via laptop communication, while path planning and mapping are simulated virtually in Gazebo. The proposed design provides a foundation for a smart, autonomous delivery system that can safely transport medicines and medical supplies without direct human contact. In future improvements, LiDAR and Raspberry Pi will be integrated for real-time mapping, object detection, and full autonomy. This approach demonstrates an affordable, scalable solution for healthcare automation, particularly useful in hospitals, quarantine zones, and patient care environments.

This article presents the design and implementation of an autonomous navigation framework for a Quadrotor-Unmanned Aerial Vehicle (UAV) based on Simultaneous Localization and Mapping (SLAM). The proposed system leverages the Extended Kalman Filter (EKF)-SLAM algorithm, operating solely on LiDAR data and without reliance on GPS, making it highly suitable for navigation in GPS-denied or cluttered environments such as indoor settings, tunnels, and urban canyons. A dynamic model of the UAV is developed to describe its motion under 3-DoF, forming the basis for both simulation and control. The EKF-SLAM algorithm is implemented within the Robot Operating System (ROS) and tested using the Gazebo simulation environment. Experimental results confirm the algorithm’s effectiveness in real-time localization and 3D environment mapping using LiDAR data, even in the absence proprioceptive input sensor.

Decentralized algorithms are often used for multi-agent search in scenarios in which it is difficult or computationally intractable to use a centralized control strategy. "Bottom-up" multi-agent search techniques, or algorithms that are based on local interactions between agents and their environment, allow for highly scalable and fault tolerant systems. Moreover, they often demonstrate remarkable emergent properties, such as foraging behavior and emulated states of matter, which can be found in bio-inspired swarm algorithms and physics-inspired methods. However, it is often difficult to control these systems, especially when area coverage and target localization are the primary goals. In this paper we propose using stigmergic techniques combined with physicomimetic force laws to guide a multi-agent system toward efficiently exploring a pre-defined search area. We found that our method provides a reliable and effective method for decentralized area coverage by a multi-agent system. Furthermore, we implemented our approach using Robot Operating System (ROS) and the Gazebo simulation environment.

The unmanned ground vehicle is supposed to replace humans for various applications in both civilian and military area, including transport, delivery, shuttle, clean, patrol, scout, and battle. It has been widely expected that the unmanned ground vehicle will greatly change the human life and the land combat form in the near future. The key techniques of the unmanned ground vehicle are environment perception, motion planning, chassis dynamics control, and cloud control. In this paper, the state of the art of the development of the military and civilian unmanned ground vehicle is introduced, and many classic ones are reviewed in detail in different application categories. The overall design and X-by-wire dynamics control technique of the civilian and military unmanned ground vehicle are reviewed. The X-by-wire technique plays an important role in improving the maneuverability, mobility, and handling performance of the unmanned ground vehicle. Therefore, the chassis dynamics control is considered as a core technique of the X-by-wire unmanned ground vehicle. In addition, as a research trend of the unmanned ground vehicle dynamics control, the cloud-based dynamics control approach of future unmanned ground vehicle based on big data and cloud control techniques is described according to the authors’ work.

In this work, we present a Robot Operating System (ROS) service implementation to generate polynomial trajectories for an Unmanned Ground Vehicle (UGV). The algorithm has been tested on a tracked robotic platform in order to follow a set of waypoints in an open-field navigation scenario, using a state-feedback controller. The odometry estimation of the ground robot leverages on GNSS readings and motor encoders. Experimental tests are analyzed and results presented.

The problem of cooperative obstacle avoidance by a group of unmanned ground vehicles (UGVs) and unmanned air vehicles (UAVs) during a typical search mission is addressed in this paper. The group of UAVs and UGVs are performing a search operation in the designated area. All the UAVs and UGVs are equipped with a vision sensor/LIDAR to identify the possible obstacles in the search space. Due to their operation on the ground, UGVs are more likely to encounter obstacles. The obstacle avoidance for UGV under the event of sensor failure is done with environment information from the nearest UAV. The UAV plans its trajectory according to the UGV’s expected future trajectory, leading towards the base station. The UGV replans its trajectory to avoid obstacles after obtaining the information from its nearest UAV. A simulation study is performed with 10 UAVs and five UGVs performing a search mission in 1[Formula: see text]km[Formula: see text][Formula: see text][Formula: see text]1[Formula: see text]km area. The proposed obstacle avoidance method is experimentally validated in the outdoor environment with an autonomous UAV equipped with a camera and an autonomous UGV navigating based on GPS localization and environment information from the UAV.