Learning traversability map of different robotic platforms for unstructured terrains path planning

: This report describes all SBIR Phase 2 effort to validate a mathematical model mid conduct a design study of the Air Force Dynamic Environment The Dynamic Environment Simulator, or DES, is a three degree-of-freedom centrifuge operated by Armstrong Laboratories at Wright-Patterson AFB. The effort was conducted under Contract F41624-96-C-6027 through the Armstrong Laboratory and was entitled Model Validation and Design Study of the Dynamic Environment Simulator. This Phase 2 project had four primary objectives, which were to: (1) Validate the Phase 1 software model of the DES; (2) Conduct a design study of DES improvements; (3) Perform a cost/benefit analysis of the possible DES improvements; and (4) Demonstrate the commercial potential of the model and the DES.

This paper examines the problem of simulating angular velocity fields that may for example, be experienced by a pilot during a supermaneuverable flight trajectory. The ability to simulate angular velocity fields has important applications in the study of certain physiological effects encountered by pilots during these unusual flight scenarios. The simulations are to be performed using a centrifuge simulator called the Dynamic Environment Simulator (DES) located in the Armstrong Laboratory of Wright-Patterson Air Force Base. Some of the kinematic issues relating to the motion control of the DES are examined in the paper. >

The ability to localize a virtual auditory source was evaluated under varying levels of sustained (+G2) acceleration. Participants were required to judge the locations of virtual auditory cues located along the horizontal plane (elevation 0°) during exposure to 1.0, 1.6, 2.5, 4.0, 5.6, and 7.0 +Gz. The experiment was conducted at the Air Force Research Laboratory's Dynamic Environment Simulator - a man-rated, three-axis centrifuge. No significant increases in localization error were found between 1.0 and 5.6 +Gz; however, a significant increase did occur at the 7.0 +Gz level. In addition, the percentage of reversals did not vary as a function of +Gz level. Collectively, these results indicate that one's ability to localize virtual auditory cues is well maintained at various levels of sustained acceleration.

Multi-robot systems have demonstrated strong capability in handling environmental operations. In this study, We examine how a team of robots can be utilized in covering and removing spill patches in a dynamic environment by executing three consecutive stages: deployment, coverage, and rendezvous. For the deployment problem, we aim for robot allocation based on the discreteness of the patches that need to be covered. With the deep neural network (DNN) based spill detector and remote sensing facilities such as drones with vision sensors and satellites, we are able to obtain the spill distribution in the workspace. Then, we formulate the allocation problem in a general optimization form and provide solutions using an integer linear programming (ILP) solver under several realistic constraints. After the allocation process is completed and the robot team is divided according to the number of spills, we deploy robots to their computed optimal goal positions. In the robot deployment part, control laws based on artificial potential field (APF) method are proposed and practiced on robots with a common unicycle model. For the coverage control problem, we show two strategies that are tailored for a wirelessly networked robot team. We propose strategies for coverage with and without path planning, depending on the availability of global information. Specifically, in terms of coverage with path planning, we partition the workspace from the aerial image into pieces and let each robot take care of one of the pieces. However, path-planning-based coverage relies on GPS signals or other external positioning systems, which are not applicable for indoor or GPS-denied circumstances. Therefore, we propose an asymptotic boundary shrink control that enables a collective coverage operation with the robot team. Such a strategy does not require a planned path, and because of its distributedness, it shows many advantages, including system scalability, dynamic spill adaptability, and collision avoidance. In case of a large-scale patch that poses challenges to robot connectivity maintenance during the operation, we propose a pivot-robot coverage strategy by mean of an a priori geometric tessellation (GT). In the pivot-robot-based coverage strategy, a team of robots is sent to perform complete coverage to every packing area of GT in sequence. Ultimately, the entire spill in the workspace can be covered and removed. For the rendezvous problem, we investigate the use of graph theory and propose control strategies based on network topology to motivate robots to meet at a designated or the optimal location. The rendezvous control strategies show a strong robustness to some common failures, such as mobility failure and communication failure. To expedite the rendezvous process and enable herding control in a distributed way, we propose a multi-robot multi-point rendezvous control strategy. To verify the validity of the proposed strategies, we carry out simulations in the Robotarium MATLAB platform, which is an open source swarm robotics experiment testbed, and conduct real experiments involving multiple mobile robots.

This paper studies self-organized flocking in a swarm of mobile robots. We present Kobot, a mobile robot platform developed specifically for swarm robotic studies, briefly describing its sensing and communication abilities. In particular, we describe a scalable method that allows the robots to sense the orientations of their neighbors using a digital compass and wireless communication. Then we propose a behavior for a swarm of robots that creates self-organized flocking by using heading alignment and proximal control. The flocking behavior is observed to operate in three phases: alignment, advance, and avoidance. We evaluate four variants of this behavior by setting its parameters to extreme values and analyze the performance of flocking using a number of metrics, such as order and entropy. Our results show that, the flocking behavior obtained under appropriate parameter values, is quite robust and generates successful self-organized flocking in constraint environments.

Differentiating responses of weather files and local climate change to explain variations in building thermal-energy performance simulations

Swarm robot systems perform long-term tasks such as information gathering and cooperative exploration while adapting to dynamic and unpredictable environments. These systems are increasingly applied in real-world scenarios such as lunar exploration and disaster response, where multiple robots operate independently and cooperatively across large areas. To support such missions, swarm robots often construct wireless communication networks spanning their deployment regions. Conventionally, the robustness of these systems has been evaluated mainly in terms of accidental robot failures, relying on redundant inter-robot connectivity. However, real-world environments also introduce spatially correlated failures, triggered by environmental phenomena such as flooding or structural collapse. To capture these effects, this chapter introduces a percolation theory–based framework for analyzing the robustness of communication networks formed by mobile robots with wireless relay capabilities. The proposed model incorporates two-dimensional noise fields to simulate environmentally induced failures and compares network robustness under environmental and accidental failure conditions. The results highlight distinct failure dynamics and identify redundancy strategies that can enhance overall system resilience. Furthermore, we establish how the spatial deployment and density of robots influence Quality of Service (QoS) indicators such as reliability and connectivity. Experimental results demonstrate that maintaining QoS under environmentally correlated (noise-induced) and accidental failures requires different redundancy strategies. These findings provide valuable insights for designing resilient 6G, Internet of Things (IoT), and disaster-recovery communication networks capable of operating in dynamic environments.

In this paper, the locomotion and steering control of a simulated Mini Cheetah quadruped robot was investigated in the presence of terrain characterised by low friction. Low-level locomotion and steering control were implemented via a central pattern generator approach, whereas high-level steering control manoeuvres were implemented by comparing a neural network and a linear model predictive controller in a dynamic simulation environment. A data-driven approach was adopted to identify the robot model using both a linear transfer function and a shallow artificial neural network. The results demonstrate that, whereas the linear approach showed good performance in high-friction terrain, in the presence of slippery conditions, the application of a neural network predictive controller improved trajectory accuracy and preserved robot safety with different steering manoeuvres. A comparative analysis was carried out using several performance indices.

The key to the successful "factory of the future" will be the effective coordination of managerial functions and production operations. The research presented in this dissertation addresses this "coordination" issue, especially in terms of understanding the impact and possibilities which exist in an information-rich environment. Such an environment is often referred to in the literature as being a computer-integrated manufacturing (CIM) environment. An interactive simulation modeling program, MOSES (Manufacturing Organization Simulation and Evaluation System), was developed for the purpose of describing, analyzing, and understanding such manufacturing. MOSES is a copyrighted computer program, a menu-driven, interactive, simulation modeling tool. MOSES users are able to define the manufacturing environment to be simulated by keying in data values (e.g., products, bills of material, and production rates) which describe the organization. MOSES then uses these data values to "build the manufacturing simulation model." That is, once the manufacturing parameters have been defined, MOSES can simulate the manufacturing organization (from orders through distribution) without the user having to write any computer code or having to understand the principles of simulation modeling. MOSES users are then able to interact with the manufacturing organization as "managers" in the dynamic simulation environment. This interactive capability allows MOSES users to test various manufacturing scenarios, watch the model's progress from the perspectives of different organizational functions (e.g., from marketing or from production), override model-generated decisions (e.g., by changing the demand forecast), and view the results. The MOSES approach to simulation modeling differs from traditional approaches. In short, MOSES uses both mathematic and logical modeling principles, employs traditional simulation modeling techniques but in combination with database technology, and includes the user as an integral component in the simulation process.

• Developed dynamic models to simulate LFR-based nuclear propulsion systems. • Introduced the Ship-to-Shore concept, enabling clean energy transfer to ports. • Achieved significant CO 2 emission reduction: 24.5 kt/y during navigation. • Increased energy production by 697% through the Ship-to-Shore concept. • Provided a pathway for sustainable maritime propulsion aligned with IMO targets. This paper investigates the integration of IV generation small modular reactors into maritime vessels as a viable alternative to conventional fossil fuel-powered systems. To support maritime decarbonisation and evaluate the energy and economic performance of their onboard implementation, suitable mathematical models of energy systems powered by small modular reactors are developed. Among various nuclear technologies, a lead-cooled fast reactor (LFR) using mixed oxide fuel is modelled and implemented within a dynamic simulation environment. The model simulates the energy performance of a Helium Brayton cycle with a regenerator and reheating, powered by an LFR. This model can assess the temperature distribution of the lead coolant, the helium temperatures throughout the Brayton cycle, the generated mechanical and electrical power, thermodynamic efficiency, and waste heat. Following the development of the LFR-based model, it is integrated into a broader computational framework that enables the evaluation of the ship’s overall energy performance, including the energy demands of onboard utilities under different operating modes and climatic conditions. To demonstrate the potential of these systems, a LFR is simulated as a replacement for conventional fossil fuel engines onboard a containership. A representative route, reconstructed from real-time online data, is used to assess reactor performance under dynamic maritime conditions. Furthermore, a novel concept – ship-to-shore connection – is introduced, whereby surplus electricity produced by the onboard LFR is supplied to port grids, contributing to the decarbonisation of port facilities and surrounding areas. Simulation results indicate that small modular reactor systems could effectively nullify onboard fossil fuel consumption. For instance, avoided CO 2 emissions are estimated at 24.5 kt/y during navigation and 15.3 kt/y during port stays, including also emissions offset through the replacement of national thermoelectric power generation in ship-to-shore application. The implementation of the ship-to-shore connection concept results in an increase in energy production during port stays ranging from 572% to 697%, yielding substantial environmental and economic benefits.

Steady-state Optimality Analysis for Investigating the Energy Optimal Operation of Representative Natural Gas Liquefaction Cycles

Disaggregate Analysis of the Inter-Building Effect in a Dense Urban Environment

This paper entails application of the energy shaping methodology to control a flexible, elastic Cosserat rod model. Recent interest in such continuum models stems from applications in soft robotics, and from the growing recognition of the role of mechanics and embodiment in biological control strategies: octopuses are often regarded as iconic examples of this interplay. Here, the dynamics of the Cosserat rod, modeling a single octopus arm, are treated as a Hamiltonian system and the internal muscle actuators are modeled as distributed forces and couples. The proposed energy shaping control design procedure involves two steps: (1) a potential energy is designed such that its minimizer is the desired equilibrium configuration; (2) an energy shaping control law is implemented to reach the desired equilibrium. By interpreting the controlled Hamiltonian as a Lyapunov function, asymptotic stability of the equilibrium configuration is deduced. The energy shaping control law is shown to require only the deformations of the equilibrium configuration. A forward-backward algorithm is proposed to compute these deformations in an online iterative manner. The overall control design methodology is implemented and demonstrated in a dynamic simulation environment. Results of several bio-inspired numerical experiments involving the control of octopus arms are reported.

When moving objects that are too bulky or heavy to be grasped or lifted, robotic manipulation can benefit from the object's interaction with the support surface and its natural dynamics under gravity. In this work, we show that such dynamic, underactuated manipulation capability can be acquired through reinforcement learning and deployed on real robot systems. First, we present a framework to learn a control policy for object transport in a dynamic simulation environment, featuring the object and the support surface. We then demonstrate successful object locomotion with the learned policy through a set of simulated and real-world experiments, performed with a robot arm and an aerial robot interacting with the object in a non-prehensile manner. While the object, which is in contact with the support surface, oscillates sideways passively under gravity, the robot uses the learned policy to move the object forward with a steady gait by regulating the mechanical energy and the posture of the object. Our experiment results show that the learned policy can transport the object through unmodeled effects of terrain and perturbation.

<title>ABSTRACT</title> <p>This paper presents a novel approach for modeling LAV-terrain systems in a dynamic simulation environment, which is based on results from the research and development of advanced technologies by the Computer Modeling and Simulation team of General Dynamics Land Systems-Canada (GDLS-C). The presented soil-tire model has been developed based upon the application of terra-mechanics and is being uniquely integrated with a full 8x8 LAV model in ADAMS/View, with incorporation of large tire deflections and multi-passing effect. It is shown that the highly efficient soil-tire model is capable of dynamically predicting soil sinkage, tire deflection, wheel slip, rolling resistance, drawbar pull and actual torque created at each soil-tire interface, as required by the mobility analysis of LAV systems over soft terrains.</p>