Search

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]

Copyright of Frontiers of Information Technology & Electronic Engineering is the property of Springer Nature and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

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]

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]

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.

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]

When multiple robots operate in the same environment, it is desirable for scalability purposes to coordinate their motion in a distributed fashion while providing guarantees about their safety. If the robots have to respect second-order dynamics, this becomes a challenging problem, even for static environments. This work presents a replanning framework where each robot computes partial plans during each cycle, while executing a previously computed trajectory. Each robot communicates with its neighbors to select a trajectory that is safe over an infinite time horizon. The proposed approach does not require synchronization and allows the robots to dynamically change their cycles over time. This paper proves that the proposed motion coordination algorithm guarantees safety under this general setting. This framework is not specific to the underlying robot dynamics, as it can be used with a variety of dynamical systems, nor to the planning or control algorithm used to generate the robot trajectories. The performance of the approach is evaluated using a distributed multi-robot simulator on a computing cluster, where the simulated robots are forced to communicate by exchanging messages. The simulation results confirm the safety of the algorithm in various environments with up to 32 robots governed by second-order dynamics. [ABSTRACT FROM AUTHOR]

A distributed telerobotic system is proposed based on a master-arm station that is interconnected by a computer network to a slave-arm station. The distributed telerobotic system is evaluated using a set of teleoperated experiments: 1) peg-inhole insertion; 2) assembly of a small water pump; 3) operating drawers; 4) pouring of water; and 5) wire wrapping. Direct teleoperation is evaluated using the following schemes: 1) stereo vision; 2) vision and force feedback (VFF); and 3) vision with active compliance (VAC). Space indexing and scaling tools are also used. Operator hand is logically mapped to a remote tool both in position and force. The operator feels the forces that were exerted on the tool as they were exerted on the hand. Extensive experimental analysis showed that mapping of operator hand motion and force feedback (FF) to a convenient tool point reduces the operator mental load and task time due to highly coordinated motion. Stereo vision may solely be used at the cost of large peak forces and extended task time. VFF has nearly equal task time compared to VAC but with a noticeable increase in contact forces. For a large majority of cases, the contact-based tasks that were done using VAC resulted in the least task times and the least contact forces. VAC is superior to VFF, which is better than V. In other words, there is an enormous gain in stability if one removes the bilateral FF channel in teleoperation and relies on a slave-arm active compliance. [ABSTRACT FROM AUTHOR]

A general framework for the feedback control of mobile manipulators is proposed. Its main originality lies in its capacity to address in a unified manner both cases of omnidirectional and nonholonomic mobile platforms. it also allows for the execution of the desired manipulation task without the manipulator colliding into its joint limits, whenever these two objectives are compatible. This is obtained without having to rely upon trajectory planning. In the nonholonomic case this feature, which is not demonstrated for previous feedback control schemes proposed in the literature, is instrumental for performing reflex maneuvers automatically. It relies upon the concept, used in the control design, ofa companion omnidirectional frame associated with the physical platform. The generality of the approach can be appreciated from its applicability to various nonholonomic platforms, for example unicycle-type and car-like. [ABSTRACT FROM AUTHOR]

Safe and efficient operations of cranes in complex construction environments require thorough planning of lift paths. As the scales of construction projects expand, path planning becomes excessively sophisticated and laborious using conventional practices where lift paths are verified manually. This study proposes a novel practicality and safety-aware path planning method for single crane lifting scenarios, which defines and incorporates key practicality considerations in a sampling-based path planning algorithm. An RRT*-based algorithm is proposed and validated via a case study of real-world lift jobs. Results indicate that the proposed method significantly improved the path quality in terms of practicality considerations without compromising computational efficiency. The proposed method is promising in generating operable and realistic paths that can be practically executed by crane operators. It is expected to advance the knowledge and practice in automated crane lift planning, allowing for more informed prediction and management of on-site activities involving the use of cranes. • Major practicality considerations in crane path planning are mathematically modeled. • A practicality and safety-aware path planning method is introduced. • The proposed PSRRT* algorithm is validated in real lift tasks. • The impact of lift task complexity on planning outcome is evaluated. • Improvement on lift visibility and reduction of motion coordination are observed. [ABSTRACT FROM AUTHOR]

The long-range objective is to help the development of computational tools for all human movement on the ground and in the air. Movements that can be neurally and physiologically observed and measured contribute to unlocking the mysteries of the central nervous system (CNS). Medicine, science and engineering have successfully adopted minimal and reduced systems to understand the complexities of the human system and to offer effective solutions. The coordinate systems and the state variables the CNS utilizes are not well known. Movement in the air poses problems of dynamics, control, stability, coordination, maneuverability, landing and signal processing. Awareness of the states of the implement and how the CNS estimates these states from the available contact with it in the air is one challenge. It is assumed the head and the torso are held as one rigid body such that the angular velocities and accelerations, measured by the vestibular system, are the same as those of the torso. The vision system is not considered. [ABSTRACT FROM AUTHOR]