Path Planning for Reconfigurable hTetro Robot Combining Heat Conduction-Based and Discrete Optimization

Modular robots are composed of many elementary mechatronic modules that can be connected to form a robot body of various shapes. This feature allows such a robot to adapt for a given task and particular environment. A motion of the modular robot is based on control of individual angles between the modules, and the robot locomotion can be realized using Central Pattern Generators (CPG). A robot motion in the environment with obstacles can be achieved using several locomotion controllers that are switched by a strategy based on motion planning techniques. Preparation of CPG-based gaits leads to a high-dimensional optimization that requires to design proper cost functions. Existing approaches optimize the gaits separately according to human-designed cost functions. In this paper, we investigate how to automatically derive a set of gaits suitable for modular robots without specifying low-level details about the gaits. We propose to optimize multiple gaits simultaneously using a single cost function. This cost function is based on the ability of motion planning to solve the task using the gaits being optimized. The proposed system is verified on several modular robots with unusual shapes including robots with failed modules.

We address the problem of detecting potential instability in the planned motion of modular self-reconfigurable robots. Previous research primarily focused on determining the system’s unique physical state but overlooked the mutual compensation effects of connection constraints. We introduce a linear-time quasi-static stability detection method for modular self-reconfigurable robots. The internal connections, non-connected contacts, and environmental contacts are considered, and the problem is modeled as a second-order cone program problem, whose solving time linearly increases with the number of modules. We aim to determine the critical stable state of the system and that is achieved by finding the required minimum characteristic connection strength. By analyzing the critical stable state, we can assess the system’s stability and identify potential broken connection points. Furthermore, the internal stability margin is defined to evaluate the configuration’s stability level. The suspension and object manipulation configurations are first demonstrated in simulation to analyze the effectiveness of the proposed algorithm. Subsequently, a series of physical experiments based on FreeSN were carried out. The calculated stable motion ranges of manipulator configurations are highly consistent with the actual sampling boundaries. Moreover, the proposed algorithm successfully predicts stability and identifies broken connections in diverse configurations, encompassing quadruped and closed-chain configurations on both even and uneven terrains. The load experiment further demonstrates that the impacts from unmodeled factors and input errors under normal conditions can be on a small scale. By combining the proposed detection method and stability margin, we open the door to realizing real-time motion planning on modular self-reconfigurable robots.

Ultrasonic scanning is the most commonly used method for detecting breast cancer. Traditional breast ultrasound mainly relies on manual examination, which requires doctors with rich clinical experience. The scanning path planning of the robot for breast ultrasound examination based on binocular vision and non-uniform rational B-Splines (NURBS) is proposed in this paper to solve the limitations of traditional examination methods. First, breast images are acquired by the binocular camera. The depth information of the images is obtained through stereo matching, and the 3D point cloud reconstruction of the breast model is completed. Second, the spatial index algorithm based on the K-dimension tree (Kd-tree) and uniform sampling method are selected to denoise and down-sample the point cloud model to complete the preliminary optimization of the model. The point cloud information for the scanning path is extracted according to the method of point cloud extraction and the principle of normal vector calculation. Then, the interpolation optimization of the scanning path based on the NURBS curve is completed. The optimized scanning path is transformed into the robot motion path. Finally, the robot motion is simulated and analyzed, and the experimental results show that the rationality of robot operation and the smoothness of robot motion meet the requirements of path planning defined in this paper. The error analysis experiment shows that the path planned by this method can ensure the efficiency and accuracy of ultrasound examination.

This paper proposes a novel self-reconfigurable modular robot for search and rescue. The modular robot consists of two main structures, a planetary wheel driven robot body and a double unlocking system as the docking structure. By means of the planetary wheel structure, the robot has a strong flexibility to cross obstacles in a disaster environment. In virtue of the docking structure with double unlocking function, the self-reconfigurable ability has improved robot reliability. Further, the modular robot can be connected or disconnected according to the actual task requirements. Finally, to verify the stability and reliability, the finite element analysis is carried out.

Highly redundant modular robots may undergo large shape changes which significantly affect the geometry and dynamics of the robot. In these motions, the shape change may induce a tipping or rolling behavior of the robot. The paper describes the dynamic modeling, locomotion planning, control and simulation of such rolling motions for the Tetrobot modular robots. The motion is described by the path profiles of controlled nodes, the tipping criteria and dynamic tipping motion and an impact-reaction model of contact with the ground. These phases of motion are described using Newton-Euler dynamic equations and the principle of conservation of angular momentum. In the paper, a two-phase planning and switching control sequence is introduced to achieve stable and reliable motion of a Tetrobot modular robot. Simulation results illustrate the tipping behavior of a tetrahedron, the dynamic contact and rolling of an icosahedral Tetrobot and dynamic control of the rolling Tetrobot. The resulting models are useful to analyze and control both intentional rolling as a new mode of mobility as well as the avoidance of unintentional tipping and rolling during task execution.

Multi-robot systems have been widely used in various actual fields to perform specific tasks. In the robot system, path planning is a burning problem. To solve this challenging issue, we mainly study the problem of path planning between robots and robots, robots and robots systems. Three factors are taken into account in the paper: the robot autonomous model, the real-time detection of speed and the obstacle risk. And we find that the robot's motion path ultimately depends on a technique based on hierarchical fuzzy control. From the simulation results, we get the conclusion: in the multi-robot system, obstacle avoidance and path decision made by the hierarchical fuzzy control system are both effective and practical.

This paper studies the effect of the presence of a thermal interface material between a heat source and a heat sink. A heat sink is used as a passive cooling device for a thick film heating element used as a heat source. The heating element is fixed using 2 screws. The temperatures are measured in 2 scenarios: first, where the heating element is placed directly on the heat sink, and second, where a thermal pad is placed between the heat source and heat sink. Using a similar CAD model as the one tested and the same boundary conditions, finite element analysis were solved. The goal is to see if the finite element method delivers similar results as the measured ones and if the method is reliable in predicting the thermal behaviour of the electronic components. Temperature measurements show an improvement in heat source cooling when the thermal interface material is placed between the heat source and heating element. Also, the temperatures from the finite element analysis are similar to the measured ones, making the method reliable for similar applications.

We have proposed a self-reconfigurable robotic module, which has a very simple structure. The system is capable of not only building a static structure, but also generating a dynamic robotic motion. We have also developed a simulator for the motion planning. In this paper, we present details of the mechanical and electrical designs of the developed module and its control system architecture. Experiments using ten modules demonstrate the robotic configuration change, crawling locomotion and three types of quadruped locomotion.

It has recently been demonstrated that 50 mm thick industrial grade thermal insulation may serve as passive fire protection of jet fire exposed thick walled steel distillation columns. The present study investigates the performance of thermal insulation in conjunction to 3 mm, 6 mm, 12 mm and 16 mm steel walls, i.e., where the wall represents less heat sink, when exposed to 350 kW/m2 heat load. Regardless of the tested steel plate thicknesses, about 10 min passed before a nearly linear steel temperature increase versus time was observed. Thereafter, the thinnest plates systematically showed a faster temperature increase than the thickest plates confirming the wall heat sink effect. To study thermal insulation sintering, 50 mm thermal insulation cubes were heat treated (30 min holding time) at temperatures up to 1100 °C. No clear sign of melting was observed, but sintering resulted in 25% shrinkage at 1100 °C. Thermogravimetric analysis to 1300 °C revealed mass loss peaks due to anti-dusting material at 250 °C and Bakelite binder at 460 °C. No significant mass change occurred above 1000 °C. Differential scanning calorimetry to 1300 °C revealed endothermic processes related to the anti-dusting material and Bakelite mass losses, as well as a conspicuous endothermic peak at 1220 °C. This peak is most likely due to melting. The endothermic processes involved when heating the thermal insulation may to a large part explain the 10 min delay in steel plate temperature increase during fire testing. Overall, the tested thermal insulation performed surprisingly well also for protecting the thin steel plates.

Enhancing power density and reliability of power electronics is extremely important in power electronics applications. One of the key challenges in the design process is to design the optimum heat sink. In this paper, an algorithm is proposed to design air-cooled heat sinks using genetic algorithm (GA) and finite element analysis (FEA) simulations. While the GA generates a population of candidate heat sinks in each iteration, FEA simulations are used to evaluate the fitness function of each. The fitness function considered in this paper is the maximum junction temperature of the semiconductor devices. With an approach that prefers “survival of the fittest”, a heat sink providing better performance than the conventional heat sinks is obtained. The simulation and experimental evaluations of the optimized air-cooled heat sink are also included in the paper.

High thermal conductivity ceramics

Some high performance computing systems interconnect the microprocessor and the printed circuit board (PCB) using an integrated circuit (IC) socket. In these systems the socket provides a low resistance electrical connection when its contacts are compressed between the IC and the PCB pads. Obtaining and maintaining an acceptable contact resistance is essential for the seamless operation of these interconnects. The electrical resistance of an IC socket contact is a function of applied load, contact compression, and contact surface quality. To apply load to a socket (and compress the contact) a mechanical system is utilized which may consist of springs, screws, bolster plate, heat sink, and other components. The analysis of contact compression and contact resistance in an IC socket assembly is not a simple task. It requires extensive material characterization and testing. Due to rapid design, vendor selection cycles, sample size requirements, and cost, the physical testing to estimate the variability of contact resistance in socket assemblies is not feasible. Probabilistic methods such as finite element analysis (FEA) are used to overcome this obstacle. FEA provides the means to model the response of an assembly to compressive loads and variation in the mechanical load system components. An issue with this approach is that each finite element analysis run takes hours to execute, making it an impractical approach for evaluations that consider numerous parameters and parameter values. This paper describes how the response surface methodology (RSM) can be utilized to reduce the number of FEA runs that are required for the analysis of contact compression and contact resistance. Statistical distributions of the parameters of interest are constructed and used with RSM and Monte Carlo simulations to generate a transfer function. This function is evaluated thousands of times by means of Monte Carlo simulations to quantify the probability that the contact compression (and contact resistance) requirements may not be met, while considering manufacturing variability. The approach allows the virtual qualification of socket assemblies without resorting to expensive testing and prohibitive FEA computation cycles

Collaborative robots play an important role in production, assembly, operating systems, and can be used in many automation tasks. Due to the repetitive nature of robot actions, the overall efficiency of the production system is mainly determined by the precision of the robot's movement. This study proposes the optimization of robotic path planning relied on its digital twin combined with the A* algorithm. Unity is used to create the digital twin of the UR3 robot, where its path planning, based on an improved A* algorithm, is trained in a virtual environment before being implemented in the physical world. Some limitations associated with the traditional A* algorithm include redundant points, jagged paths and proximity to obstacles, increasing collision risks. Therefore, we developed the BRS-improved A* algorithm that incorporates buffer distance, redundant point elimination, and smoothing optimization using Bezier curves. Realistic movements for the robot are planned by simulated training in a virtual environment. In addition, the virtual robot's available path will be flexibly adjusted based on the physical robot's motion data, allowing for the re-adjustment of the physical robot's motion trajectory. This work uses a robot path planning algorithm to optimize the robot's path by reducing errors in the physical robotic path through interaction between virtual and real data. Additionally, optimizing the robot's path reduces the distance it needs to travel, thereby increasing energy efficiency for both the robot and the entire system.

Designing mobile robots that navigate indoor environments autonomously is known to be a difficult problem. A critical issue is in the formulation of robust motion control algorithms capable of reliably sensing partial or incomplete information from the environment and using this information to choose appropriate actions to achieve its designed goals. As an example, suppose we wish to deploy a mobile robot that autonomously patrols defined locations at a hazardous high-security facility. The robot must maintain accurate knowledge of its location, while using sensory data to recognize objects and obstacles in its immediate vicinity. Its task is to inspect the desired locations within a defined time period and provide real-time data in the event of an incident. The problem is thus to choose appropriate actions that result in accomplishing the patrol in a minimal amount of time in the partially structured environment. To solve this problem we adopt the Partially Observable Markov Decision Processes (POMDP) formalism to find near-optimal and efficient policies that provides a description the robot's motion in environments with incomplete state information. POMDP is a generalization of Markov Decision Processes (MDPs). It models a system as a coupling of an agent/decision maker (robots in our case) and an environment. We also present a methodology called Goal-Specific Representation (GSR) to reduce the size of the state-space for computational efficiency and propose an extension to the methodology.

Mobile robots are the robots that can move through the environment and be used in many applications, including the industrial environment, planet exploration, warehousing, and daily household chores. They can be controlled by an operator, set to do some specific jobs, or work autonomously. Robot path planning is the task of an autonomous robot to move safely from one position to another. In this paper, three new objective functions are introduced in the structure of improved grey wolf optimizer (IGWO) and improved particle swarm optimization (IPSO) for the robot path planning problems. As another part of our proposed method, a reduction of laser range finder (LRF) data is performed, and the avoidance collision approach is also introduced. Robots determine the next position by using LRF data and IGWO (IPSO) algorithms in a local approach. The initial and the goal positions are predefined for each robot. Moreover, the location of static obstacles and other robots are unknown for each robot. Finally, the experimental results of the robot path planning using IGWO are compared to different algorithms. The results indicate that the proposed method performs better in determining an optimal, short, safe, and smooth path. Also, it has less power and time consumption than other methods. All the algorithms are implemented in the V-REP robot simulator.