Currently, robots play an important part in many sectors, including military application. The Frame structure is an essential element of the any robot vehicle since it supports the whole weight operating on the robot vehicle as well as other sections of the robot. As the outcomes, it has to be able to sustain shock, twist, vibration, as well as other stresses. High stress & total deformation is key performance indicator of Frame. This research is deals with optimization of the different kind of material for military operation robot vehicle on the basis of stress and maximum deformation. The ANSYS workbench is used to do static structural analysis of the Robot frame. CATIA software is used to design the robot frame. Firstly we prepare 3D model of Robot frame as per Requirement then analysis for different types of material such as Structural Steel, Aluminum Alloy & ABS Plastic and after optimization we can perform the Modal Analysis of optimized material

Projections for future air mobility envisage intensely utilised airspace that does not simply scale up from existing systems with centralised air traffic control. This paper considers the implementation and test of a software and hardware framework for decentralised control of aerial vehicles within intensely used airspace. Up to 10 rotary wing vehicles of maximum all up mass of 1 kg are flown in an outdoor volume with length scale of 100 m with GPS and WiFi connectivity. Flight control is implemented using a Pixhawk 4 flight controller running the PX4 firmware with guidance algorithms run on a separate onboard companion computer. Deconfliction is implemented using a simple elastic repulsion model with a guidance update rate of 10 Hz. Traffic structures are constructed from a path of directed waypoints and associated cross sectional geometry. Junctions are implemented when two paths converge into one or when one path diverges into two. Agents engage with structures through execution of flow, merge and swirl velocity rules. Calibration experiments showed that the worst case latency in agents sharing position information was of the order of 0 .5 s made up from delays due to finite guidance update rate, WiFi processing and centralised message processing. A choice of vehicle cruise speed of 2 m/s and conflict radius of 2 .5 m provided an acceptable compromise between experiment time efficiency (speed) and spatial efficiency (resolution) within the test volume. Results from recirculating junction experiments show that peak deconfliction activity occurs at the junction node, however biased distribution of agents within a corridor means the peak intensity is pushed ahead of the node. Use of meshed helical junction structures significantly reduces the intensity of conflict at the expense of reduced junction time efficiency.

This paper presents the design and development of a funnel-shaped Sparus Docking Station (SDS) intended for the non-holonomic torpedo-shaped Sparus II Autonomous Underwater Vehicles (AUV). The SDS is equipped with sensors and batteries, allowing for a stand-alone long-term deployment of the AUV. An inverted Ultra Short BaseLine (USBL) system is used to locate the Docking Station (DS) as well as to provide long-term drift-less AUV navigation. The SDS is able to observe the ocean currents using a Doppler Velocity Log (DVL), being motorized to allow its self-alignment with the current. Moreover, a docking algorithm accounting for the current is used to guide the robot during the docking maneuver. The paper reports experimental results of the docking maneuver in sea trials.

This paper fuses ideas from Reinforcement Learning (RL), Learning from Demonstration (LfD), and Ensemble Learning into a single paradigm. Knowledge from a mixture of control algorithms (experts) are used to constrain the action space of the agent, enabling faster RL refining of a control policy, by avoiding unnecessary explorative actions. Domain-specific knowledge of each expert is exploited. However, the resulting policy is robust against errors of individual experts, since it is refined by a RL reward function without copying any particular demonstration. Our method has the potential to supplement existing RLfD methods when multiple algorithmic approaches are available to function as experts. We illustrate our method in the context of a Visual Servoing (VS) task, in which a 7-dof robot arm is controlled to maintain a desired pose relative to a target object. We explore four methods for bounding the actions of the RL agent during training. These methods include using a hypercube and convex hull with modified loss functions, ignoring actions outside the convex hull, and projecting actions onto the convex hull. We compare the training progress of each method with and without using the expert demonstrators. Our experiments show that using the convex hull with a modified loss function significantly improves training progress. Furthermore, we demonstrate faster VS error convergence while maintaining higher manipulability of the arm, compared to classical image-based VS, position-based VS, and hybrid-decoupled VS.

A new form of waypoint navigation controller for a skid-steer vehicle is presented, which consisted of a multiple input-single output nonlinear fuzzy angular velocity controller. The mem- bership functions of the fuzzy controller employed a trape- zoidal structure with a completely symmetric rule-base. No- tably, Hierarchical Rule-Base Reduction (HRBR) was incorpo- rated into the controller to select only the rules most influen- tial on state errors. This was done by selecting inputs/outputs and generating a hierarchy of inputs using a Fuzzy Relations Control Strategy (FRCS). Similar to some traditional fuzzy con- trollers, the system provided coverage for the global operat- ing environment. However, a rule for every possible combi- nation of variables and states was no longer necessary. Con- sequently, HRBR fuzzy controllers effectively increase both the number of inputs and their associated fidelity without the rule-base dramatically increasing. To contextualize the performance of the controller, a background on vehicle dy- namic modeling methodologies and an in-depth explanation of the related simulation model are provided. An examina- tion of the proposed controller is then completed employing test courses. The test courses examine the effects of steer- ing disturbance, phase lag, and overshoot as expressed in Root Mean Square Error (RMSE), Max Error (ME), and Course Completion Time (CCT). Finally, simulation and experimental results for the controller’s performance were compared with a state-of-the-art waypoint navigation vehicle controller, ge- ometric pure pursuit. The fuzzy was found to outperform the pure pursuit experimentally by 52.1 percent in RMSE, 26.8 percent in ME, and 1.07 percent in CCT, on average, validat- ing the viability of the controller.

The fault diagnosis with one single sensor cannot comprehensively use the multi-sensor correlation information of fault signals for underwater thruster entanglement. To solve the real-time fault diagnosis problem that in the case of entanglement, a kind of fault diagnosis method based on the combination of current and rotational speed signal correlation analysis and Fuzzy C-means clustering (FCM) is proposed. Firstly, the collected current and speed signals of underwater thrusters under different states are normalized; Secondly, the correlation of normalized current and speed signals is calculated, and the correlation matrix is formed, and the sliding window of sampling series is adopted to calculate the correlation coefficient change over time between current and speed. Then a kind of Fuzzy C-means clustering method based on improved distance index is used to diagnose fault with correlation matrix elements. To verify the effectiveness of the proposed method, the sampling data in the case of propeller entanglement is used to verify the proposed fault detection method. The results show that the proposed method can fully extract the multi-sensor correlation information of underwater thrusters compared with the fault diagnosis method using only one signal using Support Vector Machine, and the feature extraction is more sufficient, effectively improving the accuracy of underwater thruster fault diagnosis.

The Complete Coverage Path Planning (CCPP) problem is a sub-field of industrial motion planning that has applications in various domains, ranging from mobile robotics to treatment applications. Especially in precision agriculture with a high level of automation, the use of CCPP techniques is essential for efficient resource utilization, reduced soil compaction, and increased yields. This paper reviews the CCPP problem in the context of machines operating in agricultural fields and proposes a methodological approach consisting of three steps: Generating the Guidance Tracks (i.e. the track system along which the path should be oriented), determining the traversing sequence through these tracks, and planning a smooth and drivable path. This paper provides an in-depth review of optimization-based approaches that deal with the first step, the generation of the guidance track system. Thereby, a comprehensive and pedagogical approach for generation of guidance tracks for arbitrarily-shaped two-dimensional regions of interest is provided, along with an overview and detailed elaboration on different exact cellular decomposition techniques found in literature. Furthermore, cost functions are outlined for the different approaches presented in this work, which are utilized to generate optimal guidance tracks.

Earthquakes, fire, and floods often cause structural collapses of buildings. The inspection of damaged buildings poses a high risk for emergency forces or is even impossible, though. We present three recent selected missions of the Robotics Task Force of the German Rescue Robotics Center, where both ground and aerial robots were used to explore destroyed buildings. We describe and reflect the missions as well as the lessons learned that have resulted from them. In order to make robots from research laboratories fit for real operations, realistic test environments were set up for outdoor and indoor use and tested in regular exercises by researchers and emergency forces. Based on this experience, the robots and their control software were significantly improved. Furthermore, top teams of researchers and first responders were formed, each with realistic assessments of the operational and practical suitability of robotic systems.

Simultaneous localization and mapping (SLAM) technology is ubiquitously employed in ground robots, unmanned aerial vehicles, and autonomous cars. This paper presents LTA-OM: an efficient, robust, and accurate LiDAR SLAM system. Employing FAST-LIO2 and Stable Triangle Descriptor as LiDAR-IMU odometry and the loop detection method, respectively, LTA-OM is implemented to be functionally complete, including loop detection and correction, false positive loop closure rejection, long-term association mapping, and multi-session localization and mapping. One novelty of this paper is the real-time long-term association (LTA) mapping, which exploits the direct scan-to-map registration of FAST-LIO2 and employs the corrected history map to constrain the mapping process globally. LTA leads to more globally consistent map construction and drift-less odometry at revisit places. We exhaustively benchmark LTA-OM and other state-of-the-art LiDAR systems with 18 data sequences. The results show that LTA-OM steadily outperforms other systems regarding trajectory accuracy, map consistency, and time consumption. The robustness of LTA-OM is validated in a challenging scene - a multi-level building having similar structures at different levels. Besides, a multi-session mode is designed to allow the user to store current session’s results, including the corrected map points, optimized odometry, and descriptor database for future sessions. The benefits of this mode are additional accuracy improvement and consistent map stitching, which is helpful for life-long mapping. Furthermore, LTA-OM has valuable features for robot control and path planning, including high-frequency and real-time odometry, drift-less odometry at revisit places, and fast loop closing convergence. Moreover, LTA-OM is versatile as it is applicable to both multi-line spinning and solid-state LiDARs, mobile robots and handheld platforms.

This paper presents a precise two-robot collaboration method for 3D self-localization relying on a single rotating camera and onboard accelerometers used to measure the tilt of the robots. This method allows for localization in GPS-denied environments and in the presence of magnetic interference or relatively (or totally) dark and unstructured unmarked locations. One robot moves forward on each step while the other remains stationary. The tilt angles of the robots obtained from the accelerometers and the rotational angle of the turret, associated with the video analysis, make it possible to continuously calculate the location of each robot. We describe a hardware setup used for experiments and provide a detailed description of the algorithm that fuses the data obtained by the accelerometers and cameras and runs in real-time on onboard micro-computers. Finally, we present 2D and 3D experimental results, which show that the system achieves 2% accuracy for the total travelled distance.

Glacial moulins (cylindrical meltwater drainage shafts) provide valuable insights into glacier dynamics, but are inaccessible and hazardous environments for humans to study. Exploring using passive sensor probes has revealed the complex geometry of moulins, however, exploration has been limited. To overcome these challenges, we propose a tethered robot capable of autonomously exploring and capturing data in glacial moulins. Our novel robot is equipped with a tether to support its motion. Combined with novel estimation and control algorithms, the tethered robot is able to safely and efficiently maneuver in confined, chimney-like structures such as moulins. Laboratory and field experiments confirm the feasibility of the proposed design, showing successful localization in environments with no access to positional measurements. Field trials on the Mer de Glace glacier demonstrate the robot’s capabilities, descending into the largest moulin to depths of 25m and using onboard sensors to reconstruct the moulin shape. Two sampling mechanisms are presented and evaluated to extract samples from the icy surface of the moulin. Our results show promising potential for future exploration of moulins, demonstrating the effectiveness of our tethered robot for safely gathering data from these hazardous environments.

The soil contact model (SCM) is widely used in practice for off-road wheeled vehicle mobility studies when simulation speed is important and highly accurate results are not a main concern. In practice, the SCM parameters are obtained via a bevameter test, which requires a complex apparatus and experimental procedure. Here, we advance the idea of running a virtual bevameter test using a high-fidelity terramechanics simulation. The latter employs the “continuous representation model” (CRM), which regards the deformable terrain as an elasto-plastic continuum that is spatially discretized using the smoothed particle hydrodynamics (SPH) method. The approach embraced is as follows: a virtual bevameter test is run in simulation using CRM terrain to generate “ground truth” data; in a Bayesian framework, this data is subsequently used to calibrate the SCM terrain. We show that (i) the resulting SCM terrain, while leading to fast terramechanics simulations, serves as a good proxy for the more complex CRM terrain; and (ii) the SCM-over-CRM simulation speedup is roughly one order of magnitude. These conclusions are reached in conjunction with two tests: a single wheel test, and a full rover simulation. The SCM and CRM simulations are run in an open-source software called Chrono. The calibration is performed using PyMC, which is a Python package that interactively communicates with Chrono to calibrate SCM. The models and scripts used in this contribution are available as open source for unfettered use and distribution in a public repository.

Radial-skeleton shape-changing robots are rough-terrain robots and exhibit many advantages in the aspect of mobility, such as excellent terrain adaptability, light weight, good portability, and stable configuration. However, existing gait generation methods are rough and yield low tracking accuracy because the leg-ground contact friction is difficult to predict and control. In addition, no closed-loop control scheme has been proposed for this type of robot. In this study, we designed a 12-legged radial-skeleton robot with a radial expansion ratio of 2.08. Based on the prototype, we proposed a high-precision gait generation algorithm that can be used to any multi-legged radial-skeleton robot and implemented a closed-loop control scheme for accurate path tracking. Combining the contact friction and multi-body dynamics model, the robot prototype exhibits the advantages of omnidirectional motion, high-precision tracking, and motion robustness. By manufacturing a prototype and conducting comparative experiments, we verified that the proposed method yields good performance in terms of trajectory tracking accuracy and robustness in the cases of unknown terrain and interference.