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Aiming to address challenges in the motion coordination of dual-arm robot engineering applications, a comprehensive set of planning methods is devised. This paper takes a dual-arm system composed of two six-degrees-of-freedom industrial robots as the research object. Initially, a transformation model is established for the characteristic trajectories between the workpiece coordinate system and various other coordinate systems. Subsequently, the position and orientation curves of the working trajectory are discretized to facilitate the controller's execution. Furthermore, an analysis is conducted of the closed-chain kinematics relationship between two arms of the robot and a pose-calibration method based on a reference coordinate system is introduced. Finally, constraints to the collaborative motion of the dual-arm robot are analyzed, leading to the establishment of a motion collaboration planning methodology. Simulations and experiments demonstrate that the proposed approach enables effective and collaborative task planning for dual-arm robots. Moreover, joint angle and angular velocity curves corresponding to the motion trajectory exhibit smoothness, reducing joint impacts. [ABSTRACT FROM AUTHOR]

This paper presents a distributed approach to the motion control problem for a platoon of unicycle robots moving through an unknown environment filled with static obstacles under multiple hard and soft operational constraints. Each robot has an onboard camera to determine its relative position in relation to its predecessor and proximity sensors to detect and avoid nearby obstascles. Moreover, no robot apart from the leader can independently localize itself within the given workspace. To overcome this limitation, we propose a novel distributed control protocol for each robot of the fleet, utilizing the Adaptive Performance Control (APC) methodology. By utilizing the APC approach to address input constraints via the on-line modification of the error specifications, we ensure that each follower effectively tracks its predecessor without encountering collisions with obstacles, while simultaneously maintaining visual contact with its preceding robot, thus ensuring the inter-robot visual connectivity. Finally, extensive simulation results are presented to demonstrate the effectiveness of the presented control system along with a real-time experiment conducted on an actual robotic system to validate the feasibility of the proposed approach in real-world scenarios. [ABSTRACT FROM AUTHOR]

With the development of technology, the humanoid robot is no longer a concept, but a practical partner with the potential to assist people in industry, healthcare and other daily scenarios. The basis for the success of humanoid robots is not only their appearance, but more importantly their anthropomorphic behaviors, which is crucial for the human-robot interaction. Conventionally, robots are designed to follow meticulously calculated and planned trajectories, which typically rely on predefined algorithms and models, resulting in the inadaptability to unknown environments. Especially when faced with the increasing demand for personalized and customized services, predefined motion planning cannot be adapted in time to adapt to personal behavior. To solve this problem, anthropomorphic motion planning has become the focus of recent research with advances in biomechanics, neurophysiology, and exercise physiology which deepened the understanding of the body for generating and controlling movement. However, there is still no consensus on the criteria by which anthropomorphic motion is accurately generated and how to generate anthropomorphic motion. Although there are articles that provide an overview of anthropomorphic motion planning such as sampling-based, optimization-based, mimicry-based, and other methods, these methods differ only in the nature of the planning algorithms and have not yet been systematically discussed in terms of the basis for extracting upper limb motion characteristics. To better address the problem of anthropomorphic motion planning, the key milestones and most recent literature have been collated and summarized, and three crucial topics are proposed to achieve anthropomorphic motion, which are motion redundancy, motion variation, and motion coordination. The three characteristics are interrelated and interdependent, posing the challenge for anthropomorphic motion planning system. To provide some insights for the research on anthropomorphic motion planning, and improve the anthropomorphic motion ability, this article proposes a new taxonomy based on physiology, and a more complete system of anthropomorphic motion planning by providing a detailed overview of the existing methods and their contributions.

Aiming to address challenges in the motion coordination of dual-arm robot engineering applications, a comprehensive set of planning methods is devised. This paper takes a dual-arm system composed of two six-degrees-of-freedom industrial robots as the research object. Initially, a transformation model is established for the characteristic trajectories between the workpiece coordinate system and various other coordinate systems. Subsequently, the position and orientation curves of the working trajectory are discretized to facilitate the controller’s execution. Furthermore, an analysis is conducted of the closed-chain kinematics relationship between two arms of the robot and a pose-calibration method based on a reference coordinate system is introduced. Finally, constraints to the collaborative motion of the dual-arm robot are analyzed, leading to the establishment of a motion collaboration planning methodology. Simulations and experiments demonstrate that the proposed approach enables effective and collaborative task planning for dual-arm robots. Moreover, joint angle and angular velocity curves corresponding to the motion trajectory exhibit smoothness, reducing joint impacts.

This paper focuses on the optimal geometry and motion coordination problem of mobile bearings-only sensors for improving target tracking performance. A general optimal sensor–target geometry is derived with uniform sensor–target distance using D-optimality for arbitrary n ( n ≥ 2 ) bearings-only sensors. The optimal geometry is characterized by the partition cases dividing n into the sum of integers no less than two. Then, a motion coordination method is developed to steer the sensors to reach the circular radius orbit (CRO) around the target with a minimum sensor–target distance and move with a circular formation. The sensors are first driven to approach the target directly when outside the CRO. When the sensor reaches the CRO, they are then allocated to different subsets according to the partition cases through matching the optimal geometry. The sensor motion is optimized under constraints to achieve the matched optimal geometry by minimizing the sum of the distance traveled by the sensors. Finally, two illustrative examples are used to demonstrate the effectiveness of the proposed approach. [ABSTRACT FROM AUTHOR]

A bionic robotic hand can perform many movements similar to a human hand. However, there is still a significant gap in manipulation between robot and human hands. It is necessary to understand the finger kinematics and motion patterns of human hands to improve the performance of robotic hands. This study aimed to comprehensively investigate normal hand motion patterns by evaluating the kinematics of hand grip and release in healthy individuals. The data corresponding to rapid grip and release were collected from the dominant hands of 22 healthy people by sensory glove. The kinematics of 14 finger joints were analyzed, including the dynamic range of motion (ROM), peak velocity, joint sequence and finger sequence. The results show that the proximal interphalangeal (PIP) joint had a larger dynamic ROM than metacarpophalangeal (MCP) and distal interphalangeal (DIP) joints. Additionally, the PIP joint had the highest peak velocity, both in flexion and extension. For joint sequence, the PIP joint moved prior to the DIP or MCP joints during flexion, while extension started in DIP or MCP joints, followed by the PIP joint. Regarding the finger sequence, the thumb started to move before the four fingers, and stopped moving after the fingers during both grip and release. This study explored the normal motion patterns in hand grip and release, which provided a kinematic reference for the design of robotic hands and thus contributes to its development. [ABSTRACT FROM AUTHOR]

This paper addresses the redundancy resolution problem for underwater vehicle-manipulator systems (UVMSs). More specifically, a new redundancy resolution approach with motion coordination and dexterous manipulation is presented to guarantee the tracking accuracy of the end-effector while avoiding singular configurations of the manipulator. The task-priority redundancy resolution framework is adopted to achieve an effective coordinated motion of the vehicle and manipulator while exploiting the redundant degrees of freedom available. A dexterous manipulation task with low priority is proposed to reconfigure the UVMS into a more dexterous configuration by keeping the end-effector within the designed heuristic subset. An inequality-based singularity avoidance task is inserted into the redundancy resolution framework as a high-priority task to keep the measure of manipulability above a specified minimum value. Experimental results verify that the proposed approach significantly improves the dexterity and coordination of the UVMS. • A redundancy resolution approach is proposed to achieve singularity avoidance. • A heuristic subset is presented to specify the position boundary of the end-effector. • A dexterous manipulation task is designed to increase the manipulator's dexterity. • A singularity avoidance task is utilized to move away from singular configurations. [ABSTRACT FROM AUTHOR]

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We address a decentralized control problem for a multi-agent network whose goal is to track a moving multi-target system over a cluttered environment. Our problem is comprised of two interconnected problems. In the first problem, we aim to find a reference density path for the multi-target system that the multi-agent network must track as a whole. In our proposed solution approach, the probability density of the multi-target system is represented as a Gaussian mixture density which is estimated by an adaptive Gaussian sum filter. The second problem seeks for the individual inputs that will make the agents follow the moving targets while avoiding collisions. To address the latter problem, we propose a control algorithm which is based on a variation of Lloyd’s algorithm that utilizes the Gaussian mixture density computed as part of the solution to the first problem and a modified Voronoi tessellation which is composed of the so-called Obstacle-Aware Voronoi Cells. Because the two sub-problems are interconnected, an iterative approach is proposed which combines their solutions to solve the multi-target tracking problem in a comprehensive and safe way. Finally, we provide simulation results to showcase the effectiveness of our proposed approach. [ABSTRACT FROM AUTHOR]

This paper focuses on the optimal geometry and motion coordination problem of mobile bearings-only sensors for improving target tracking performance. A general optimal sensor–target geometry is derived with uniform sensor–target distance using D-optimality for arbitrary n (n≥2) bearings-only sensors. The optimal geometry is characterized by the partition cases dividing n into the sum of integers no less than two. Then, a motion coordination method is developed to steer the sensors to reach the circular radius orbit (CRO) around the target with a minimum sensor–target distance and move with a circular formation. The sensors are first driven to approach the target directly when outside the CRO. When the sensor reaches the CRO, they are then allocated to different subsets according to the partition cases through matching the optimal geometry. The sensor motion is optimized under constraints to achieve the matched optimal geometry by minimizing the sum of the distance traveled by the sensors. Finally, two illustrative examples are used to demonstrate the effectiveness of the proposed approach.

Joint relation modeling is a curial component in human motion prediction. Most existing methods rely on skeletal-based graphs to build the joint relations, where local interactive relations between joint pairs are well learned. However, the motion coordination, a global joint relation reflecting the simultaneous cooperation of all joints, is usually weakened because it is learned from part to whole progressively and asynchronously. Thus, the final predicted motions usually appear unrealistic. To tackle this issue, we learn a medium, called coordination attractor (CA), from the spatiotemporal features of motion to characterize the global motion features, which is subsequently used to build new relative joint relations. Through the CA, all joints are related simultaneously, and thus the motion coordination of all joints can be better learned. Based on this, we further propose a novel joint relation modeling module, Comprehensive Joint Relation Extractor (CJRE), to combine this motion coordination with the local interactions between joint pairs in a unified manner. Additionally, we also present a Multi-timescale Dynamics Extractor (MTDE) to extract enriched dynamics from the raw position information for effective prediction. Extensive experiments show that the proposed framework outperforms state-of-the-art methods in both short- and long-term predictions on H3.6M, CMU-Mocap, and 3DPW. [ABSTRACT FROM AUTHOR]

This article describes a novel approach to achieve motion coordination in a multirobot system based on the concept of deformation. Our main novel contribution is to link these two elements (namely, coordination and deformation). In particular, the core idea of our approach is that the robots’ motions minimize a global measure of the deformation of their positions relative to a prescribed shape. Based on this idea, we propose a linear shape controller that also incorporates a term modeling an affine deformation. We show that the affine term is particularly useful when the deformation to be controlled is large. We also propose controls for the other variables (centroid, rotation, and size) that define the geometric configuration of the team. Importantly, these additional controls are completely decoupled from the shape control. The overall approach is simple and robust, and it creates closely coordinated robot motions. Being based on deformation, it is useful in several scenarios involving manipulation tasks, e.g., handling of a highly deformable object, control of an object’s shape, or regulation of the shape formed by the fingertips of a robotic hand. We present simulation and experimental results to validate the proposed approach. [ABSTRACT FROM AUTHOR]

A bionic robotic hand can perform many movements similar to a human hand. However, there is still a significant gap in manipulation between robot and human hands. It is necessary to understand the finger kinematics and motion patterns of human hands to improve the performance of robotic hands. This study aimed to comprehensively investigate normal hand motion patterns by evaluating the kinematics of hand grip and release in healthy individuals. The data corresponding to rapid grip and release were collected from the dominant hands of 22 healthy people by sensory glove. The kinematics of 14 finger joints were analyzed, including the dynamic range of motion (ROM), peak velocity, joint sequence and finger sequence. The results show that the proximal interphalangeal (PIP) joint had a larger dynamic ROM than metacarpophalangeal (MCP) and distal interphalangeal (DIP) joints. Additionally, the PIP joint had the highest peak velocity, both in flexion and extension. For joint sequence, the PIP joint moved prior to the DIP or MCP joints during flexion, while extension started in DIP or MCP joints, followed by the PIP joint. Regarding the finger sequence, the thumb started to move before the four fingers, and stopped moving after the fingers during both grip and release. This study explored the normal motion patterns in hand grip and release, which provided a kinematic reference for the design of robotic hands and thus contributes to its development.

Motion capture systems are crucial in developing multi-quadrotor systems due to their ability to provide fast and accurate ground truth measurements for tracking and control. This paper presents the implementation details and experimental validation of a relatively low-cost motion-capture system for multi-quadrotor motion planning using an event camera. The real-time, multi-quadrotor detection and tracking tasks are performed using a deep learning network You-Only-Look-Once (YOLOv5) and a k-dimensional (k-d) tree, respectively. An optimization-based decentralized motion planning algorithm is implemented to demonstrate the effectiveness of this motion capture system. Extensive experimental evaluations were performed to (1) compare the performance of four deep-learning algorithms for high-speed multi-quadrotor detection on event-based data, (2) study precision, recall, and F1 scores as functions of lighting conditions and camera motion, and (3) investigate the scalability of this system as a function of the number of quadrotors flying in the arena. Comparative analysis of the deep learning algorithms on a consumer-grade GPU demonstrates a 4.8× to 12× sampling/inference rate advantage that YOLOv5 provides over representative one- and two-stage detectors and a 1.14× advantage over YOLOv4. In terms of precision and recall, YOLOv5 performed 15% to 18% and 27% to 41% better than representative state-of-the-art deep learning networks. Graceful detection and tracking performance degradation was observed in the face of progressively darker ambient light conditions. Despite severe camera motion, YOLOv5 precision and recall values of 94% and 98% were achieved, respectively. Finally, experiments involving up to six indoor quadrotors demonstrated the scalability of this approach. This paper also presents the first open-source event camera dataset in the literature, featuring over 10,000 fully annotated images of multiple quadrotors operating in indoor and outdoor environments. [ABSTRACT FROM AUTHOR]

This article investigates the online motion coordination problem for a group of mobile robots moving in a shared workspace, each of which is assigned a linear temporal logic specification. Based on the realistic assumptions that each robot is subject to both state and input constraints and can have only local view and local information, a fully distributed multirobot motion coordination strategy is proposed. For each robot, the motion coordination strategy consists of three layers. An offline layer precomputes the braking area for each region in the workspace, the controlled transition system, and a so-called potential function. An initialization layer outputs an initially safely satisfying trajectory. An online coordination layer resolves conflicts when one occurs. The online coordination layer is further decomposed into three steps. First, a conflict detection algorithm is implemented, which detects conflicts with neighboring robots. Whenever conflicts are detected, a rule is designed to assign dynamically a planning order to each pair of neighboring robots. Finally, a sampling-based algorithm is designed to generate local collision-free trajectories for the robot, which at the same time guarantees the feasibility of the specification. Safety is proven to be guaranteed for all robots at any time. The effectiveness and the computational tractability of the resulting solution is verified numerically by two case studies. [ABSTRACT FROM AUTHOR]

The problem of leader-follower formation of a platoon of differential-drive wheeled mobile robots without using attitude measurements is addressed in this paper. Contrary to the position-distance approaches existing in the literature, the formation and collision avoidance is achieved by introducing a state-dependent delay in the desired trajectory. The delay is obtained as the output of a dynamical system and its magnitude will decrease/increase depending on the distance between the robots. To guarantee trajectory tracking and to overcome the lack of orientation measurements, an output feedback control and attitude observer are proposed based on the kinematic model of the robots. The attitude observer is designed directly on the special orthogonal group S O (2) and it can be used in open-loop schemes. The proposed control-observer scheme ensures asymptotic convergence of the tracking and observer errors. Finally, experimental results are presented to show the performance of the proposed approach. [ABSTRACT FROM AUTHOR]

With the development of technology, the humanoid robot is no longer a concept, but a practical partner with the potential to assist people in industry, healthcare and other daily scenarios. The basis for the success of humanoid robots is not only their appearance, but more importantly their anthropomorphic behaviors, which is crucial for the human-robot interaction. Conventionally, robots are designed to follow meticulously calculated and planned trajectories, which typically rely on predefined algorithms and models, resulting in the inadaptability to unknown environments. Especially when faced with the increasing demand for personalized and customized services, predefined motion planning cannot be adapted in time to adapt to personal behavior. To solve this problem, anthropomorphic motion planning has become the focus of recent research with advances in biomechanics, neurophysiology, and exercise physiology which deepened the understanding of the body for generating and controlling movement. However, there is still no consensus on the criteria by which anthropomorphic motion is accurately generated and how to generate anthropomorphic motion. Although there are articles that provide an overview of anthropomorphic motion planning such as sampling-based, optimization-based, mimicry-based, and other methods, these methods differ only in the nature of the planning algorithms and have not yet been systematically discussed in terms of the basis for extracting upper limb motion characteristics. To better address the problem of anthropomorphic motion planning, the key milestones and most recent literature have been collated and summarized, and three crucial topics are proposed to achieve anthropomorphic motion, which are motion redundancy, motion variation, and motion coordination. The three characteristics are interrelated and interdependent, posing the challenge for anthropomorphic motion planning system. To provide some insights for the research on anthropomorphic motion planning, and improve the anthropomorphic motion ability, this article proposes a new taxonomy based on physiology, and a more complete system of anthropomorphic motion planning by providing a detailed overview of the existing methods and their contributions., Competing Interests: The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest., (Copyright © 2024 Zheng, Han and Liang.)

An adaptive relative pose controller for docking ports of two uncertain spacecraft in autonomous rendezvous and docking is developed. A novel relative translational and rotational model represented in the chaser body-fixed frame is derived first based on the classical Newton–Euler equations. Based on the proposed model, a six-degrees-of-freedom adaptive control law is presented based on norm-wise estimations for the unknown parameters of two spacecraft to decrease the online computational burden. Meanwhile, an adaptive robust control input is designed by introducing an exponential function of states to improve the response performance with respect to the traditional adaptive robust control. Moreover, a linear antiwindup compensator is employed to ensure the bounded performance of the control inputs. The explicit tuning rules for designing parameters are derived based on the stability analysis of the closed-loop system. It is proved in Lyapunov framework that all closed-loop signals are always bounded and the pose tracking error ultimately converges to a small neighborhood of zero. Simulation results validate the performance of the proposed robust adaptive control strategy. [ABSTRACT FROM AUTHOR]

This paper focuses on improving an optimization process for swarm robots using Particle Swarm Optimization (PSO) by altering the acceleration coefficient from static to dynamic. In swarm robotic, motion coordination addresses the issue of avoiding a group of robots interfere with each other in a limited workspace, while achieving the global motion objective. PSO is commonly suggested in the literature to optimize path trajectory in robotic field. However, the typical PSO tends to be trapped in local optima. Therefore, a dynamic acceleration coefficient is proposed to optimize the cognitive and social coefficients of PSO in order to improve its exploration ability in seeking the global optimum solution. With this novel feature, PSO becomes less dependent on the chain of its past experience that it had explored in a certain region within the solution space. The effectiveness of the proposed method is tested on a simulated swarm robotic platform. Results show the proposed PSO with Dynamic Coefficient (DCPSO) is 1.09 seconds and 3.58 seconds faster than the typical PSO under dynamic and extreme conditions respectively. [ABSTRACT FROM AUTHOR]

The motion coordination formation control problem for a class of non-linear system is considered in this paper, where networked induced time-delays exist in the feedback channel of each agent and in communication channels between agents. As a foundation work, a coordination formation controller in discrete-time domain that without time-delay is provided firstly. Based on the above results, a motion coordination predictive formation control strategy as well as its detail implementation processes are proposed to actively compensate the time-delays. Stability analysis and simulation results are provided to demonstrate the feasibility and effectiveness of the proposed predictive strategy. [ABSTRACT FROM AUTHOR]

To realize high-performance contouring tracking of dual-linear-motor-driven (DLMD) gantry, both motion coordination of multiaxes and synchronization of redundant actuators are indispensable factors. However, earlier contouring methods of multiaxes systems only focus on motion coordination, leading to certain inherent limitations and uncertainties when applied to DLMD gantry. Especially, the usually ignored synchronization issue may directly affect the normal operation of system. To this end, this paper proposes a novel contouring control method by integrating both motion coordination between axes and synchronization of redundant actuators. With the knowledge of the complete system dynamics, corresponding modification and extension have been done within the framework of global task coordinate frame (GTCF). Subsequently, both associated issues are effectively dealt with in the task space to simultaneously obtain efficient motion coordination ability and high-level synchronization, along with more accurate contouring error calculation. The proposed method is compared with the earlier GTCF-based contouring control method without consideration of a synchronization issue. The experimental results fully indicate the effectiveness and superiority of the proposed method to indeed guarantee and further improve the contouring performance. [ABSTRACT FROM AUTHOR]

The problem of leader-follower formation of a platoon of differential-drive wheeled mobile robots without using attitude measurements is addressed in this paper. Contrary to the position-distance approaches existing in the literature, the formation and collision avoidance is achieved by introducing a state-dependent delay in the desired trajectory. The delay is obtained as the output of a dynamical system and its magnitude will decrease/increase depending on the distance between the robots. To guarantee trajectory tracking and to overcome the lack of orientation measurements, an output feedback control and attitude observer are proposed based on the kinematic model of the robots. The attitude observer is designed directly on the special orthogonal group SO(2) and it can be used in open-loop schemes. The proposed control-observer scheme ensures asymptotic convergence of the tracking and observer errors. Finally, experimental results are presented to show the performance of the proposed approach.

A decentralized control approach is presented for a network of robotic sensors to achieve the global coordination: group aggregation or consensus flocking. Each robotic sensor obeys three local interaction rules: group cohesion, collision avoidance and velocity alignment. Accordingly, the resultant control law for each robotic sensor is composed of three associated control components. The cohesion control component is based on geometry and additionally enables maintaining initial interconnection of network. The alignment control component ensures that all members in an interconnected network eventually reach to a common velocity. The control component for collision avoidance among members, as a passive role, is embedded in cohesion control. Simulation results demonstrate the capability of the proposed control approach in achieving the coordination behaviour with an initially interconnected network. However, we observed that an initially non-interconnected network may also achieve the coordination behaviour depending on the initial distribution and velocities. Initial interconnection of a network is only a sufficient but not essential condition for the network to achieve the coordination behaviour for our control approach. [ABSTRACT FROM AUTHOR]

This paper addresses the coordinated control problem of under-actuated thrust-propelled vehicles with intermittent communication in the presence of varying communication delays. We propose a distributed control algorithm that achieves a prescribed formation for a team of vehicles that interact according to a directed graph containing a spanning tree. Each vehicle in the team is allowed to have access to its neighbors’ information only at an irregular sequence of time instants in the presence of possibly unbounded irregular communication delays and packet dropout. We show that our objective is reached under some easily verified conditions provided that the communication blackout intervals between each pair of neighbors are bounded. Simulations are provided to illustrate the effectiveness of the proposed control algorithm. [ABSTRACT FROM AUTHOR]

This article is concerned with the generic structure of the motion coordination system resulting from the application of the method of virtual holonomic constraints (VHCs) to the problem of the generation and robust execution of a dynamic humanlike motion by a humanoid robot. The motion coordination developed using VHCs is based on a motion generator equation, which is a scalar nonlinear differential equation of second order. It can be considered equivalent in function to a central pattern generator in living organisms. The relative time evolution of the degrees of freedom of a humanoid robot during a typical motion are specified by a set of coordination functions that uniquely define the overall pattern of the motion. This is comparable to a hypothesis on the existence of motion patterns in biomechanics. A robust control is derived based on a transverse linearization along the configuration manifold defined by the coordination functions. It is shown that the derived coordination and control architecture possesses excellent robustness properties. The analysis is performed on an example of a real human motion recorded in test experiments. [ABSTRACT FROM AUTHOR]

In this study, the authors propose a novel range‐only distributed motion coordination to achieve square‐grid blanket coverage for self‐deployments of networked multi‐robot systems. They specifically address the research question of maximising coverage and minimising local uniformity index (i.e. to achieve uniform coverage) for each sensor node while simultaneously maintaining the connectivity of the whole network in the absence of central coordinated controller. Under the umbrella of intelligent controls, the motion coordination scheme employed in this study is because of the nearest neighbour techniques which are employed for the purpose of clustering and autonomous coordination among sensor nodes in local area (vicinity), that is, to achieve distributed coverage. The author's research indicates that simple motion coordination schemes have led to intelligent ensemble behaviours at group levels. Compared to the existing algorithms, the superiorities of their decentralised protocol are because of its reliability and robustness against inevitable node failures, scalability and flexibility in the sense of the growth of the networks as well as its uniformity across most of steady‐state positions of sensor nodes leading to better network management. They present extensive computer simulations to investigate the effectiveness of their proposed algorithm for self‐deployment in challenging deployment regions. [ABSTRACT FROM AUTHOR]

The problem of cooperative path-finding is addressed in this work. A set of agents moving in a certain environment is given. Each agent needs to reach a given goal location. The task is to find spatial temporal paths for agents such that they eventually reach their goals by following these paths without colliding with each other. An abstraction where the environment is modeled as an undirected graph is adopted-vertices represent locations and edges represent passable regions. Agents are modeled as elements placed in the vertices while at most one agent can be located in a vertex at a time. At least one vertex remains unoccupied to allow agents to move. An agent can move into unoccupied neighboring vertex or into a vertex being currently vacated if a certain additional condition is satisfied. Two novel scalable algorithms for solving cooperative path-finding in bi-connected graphs are presented. Both algorithms target environments that are densely populated by agents. A theoretical and experimental evaluation shows that the suggested algorithms represent a viable alternative to search based techniques as well as to techniques employing permutation groups on the studied class of the problem. [ABSTRACT FROM AUTHOR]

ABSTRACT Monofunctional heme-catalases have been studied for many decades but there is still an incomplete understanding of why such a large tetrameric protein with deeply buried active sites is required to accomplish such a simple reaction as H2O2 dismutation. Catalase accomplishes this reaction at a high rate although water at 55 M is expected to compete with H2O2 for the enzyme's active site. Using molecular dynamics simulations we addressed the question as to how catalase selects H2O2 in water. Selection is accomplished through different mechanisms: higher residence time of H2O2 in the vicinity of certain prevalent amino acid residues at the protein surface and substrate channel, coordinated motion of the main passage amino acids that is increased in the presence of H2O2, a gate valve mechanism consisting of the motion of two contiguous phenylalanine residues that drive water molecules out of the final section of the substrate channel, a hydrophobic barrier before the active site that was crossed more easily by H2O2 which kept most of its hydrogen bonds while passing, and finally an increased residence time for H2O2 at the active site. These mechanisms, based on the physicochemical differences between H2O2 and water, provide an explanation as to why such a large tetrameric protein with deeply buried active sites is required to accomplish efficient H2O2 dismutation. Proteins 2014; 82:45-56. © 2013 Wiley Periodicals, Inc. [ABSTRACT FROM AUTHOR]