Search

Reduced traction limits the ability of mobile robotic systems to resist or apply large external loads, such as tugging a massive payload. One simple and versatile solution is to wrap a tether around naturally occurring objects to leverage the capstan effect and create exponentially-amplified holding forces. Experiments show that an idealized capstan model explains force amplification experienced on common irregular outdoor objects - trees, rocks, posts. Robust to variable environmental conditions, this exponential amplification method can harness single or multiple capstan objects, either in series or in parallel with a team of robots. This adaptability allows for a range of potential configurations especially useful for when objects cannot be fully encircled or gripped. This versatility is demonstrated with teleoperated mobile platforms to (1) control the lowering and arrest of a payload, (2) to achieve planar control of a payload, and (3) to act as an anchor point for a more massive platform to winch towards. We show the simple addition of a tether, wrapped around shallow stones in sand, amplifies holding force of a low-traction platform by up to 774x.

With the increasing diversity and complexity of tasks assigned to unmanned aerial vehicles (UAVs), the demands on task assignment and sequencing technologies have grown significantly, particularly for large UAV tasks such as multi-target reconnaissance area surveillance. While the current exhaustive methods offer thorough solutions, they encounter substantial challenges in addressing large-scale task assignments due to their extensive computational demands. Conversely, while heuristic algorithms are capable of delivering satisfactory solutions with limited computational resources, they frequently struggle with converging on locally optimal solutions and are characterized by low iteration rates. In response to these limitations, this paper presents a novel approach: the probabilistic chain-enhanced parallel genetic algorithm (PC-EPGA). The PC-EPGA combines probabilistic chains with genetic algorithms to significantly enhance the quality of solutions. In our approach, each UAV flight is considered a Dubins vehicle, incorporating kinematic constraints. In addition, it integrates parallel genetic algorithms to improve hardware performance and processing speed. In our study, we represent task points as chromosome nodes and construct probabilistic connection chains between these nodes. This structure is specifically designed to influence the genetic algorithm’s crossover and mutation processes by taking into account both the quantity of tasks assigned to UAVs and the associated costs of inter-task flights. In addition, we propose a fitness-based adaptive crossover operator to circumvent local optima more effectively. To optimize the parameters of the PC-EPGA, Bayesian networks are utilized, which improves the efficiency of the whole parameter search process. The experimental results show that compared to the traditional heuristic algorithms, the probabilistic chain algorithm significantly improves the quality of solutions and computational efficiency.

Ensuring safety in human–robot collaboration is one of the main challenges in mobile robotics today. Semantic maps are a potential solution because they provide semantic knowledge in addition to the geometric representation of the environment. They allow robots to perform their basic tasks using geometric representation, mainly localization, path planning and navigation, and additionally allow them to maintain a cognitive interpretation of the environment in order to reason and make decisions based on the context. The goal of this paper is to briefly review semantic mapping for a single mobile robot in indoor environments, and then focus on collaborative mobile semantic mapping. In both contexts, the semantic mapping process is divided into modules/tasks, and recent solutions for each module are discussed. Possible system architectures are also discussed for collaborative semantic mapping. Finally, future directions are highlighted. [ABSTRACT FROM AUTHOR]

Multi-robot collaboration has become a needed component in unknown environment exploration due to its ability to accomplish various challenging situations. Potential-field-based methods are widely used for autonomous exploration because of their high efficiency and low travel cost. However, exploration speed and collaboration ability are still challenging topics. Therefore, we propose a D istributed M ulti-Robot P otential- F ield-Based Exploration (DMPF-Explore). In particular, we first present a D istributed S ubmap-Based M ulti-Robot C ollaborative Mapping Method (DSMC-Map), which can efficiently estimate the robot trajectories and construct the global map by merging the local maps from each robot. Second, we introduce a Potential-Field-Based Exploration Strategy Augmented with M odified W ave- F ront Distance and C olored N oises (MWF-CN), in which the accessible frontier neighborhood is extended, and the colored noise provokes the enhancement of exploration performance. The proposed exploration method is deployed for simulation and real-world scenarios. The results show that our approach outperforms the existing ones regarding exploration speed and collaboration ability. • Our exploration method, DMPF-Explore, comprises the DSMC-Map and the MWF-CN. • The DSMC-Map provides reliable and consistent maps of unknown environments. • The MWF-CN boosts frontier detection and robots' adaptability. • By experiments, robots with DMPF-Explore explore more efficiently than the baselines. [ABSTRACT FROM AUTHOR]

In this article, we consider a network of agents that has to self-assign a set of tasks while respecting resource constraints. One possible formulation is the generalized assignment problem, where the goal is to find a maximum payoff while satisfying capability constraints. We propose a purely distributed branch-and-price algorithm to solve this problem in a cooperative fashion. Inspired by classical (centralized) branch-and-price schemes, in the proposed algorithm, each agent locally solves small linear programs, generates columns by solving simple knapsack problems, and communicates to its neighbors a fixed number of basic columns. We prove finite-time convergence of the algorithm to an optimal solution of the problem. Then, we apply the proposed scheme to a generalized assignment scenario, in which a team of robots has to serve a set of tasks. We implement the proposed algorithm in a Robot Operating System testbed and provide experiments for a team of heterogeneous robots solving the assignment problem. [ABSTRACT FROM AUTHOR]

Suppose in a given planar region, there are smart mobile evaders and we want to detect them using sweeping agents. We assume that the agents have line sensors of equal length. We propose procedures for designing cooperative sweeping processes that ensure successful completion of the task, thereby deriving conditions on the sweeping velocity of the agents and their paths. Successful completion of the task means that evaders with a known limit on their velocity cannot escape detection by the sweeping agents. A simpler task for the sweeping swarm is the confinement of the evaders to their initial domain. The feasibility of completing these tasks depends on geometric and dynamic constraints that impose a lower bound on the velocity the sweeping agent must have. This critical velocity is derived to ensure the achievement of the confinement task. Increasing the velocity above the lower bound enables the agents to complete the search task as well. We present results on the total search time for two types of novel pincer-movement search processes, circular and spiral, for any even number of sweeping agents. The proposed spiral process allows detection of all evaders while sweeping at velocities that approach the theoretical lower bound. [ABSTRACT FROM AUTHOR]

Management and control of transportation systems benefit from simulation modeling. The design of the corresponding models is difficult because of their complexity. Multi-agent systems cope with this problem by a divide-and-conquer approach. However, agent model design is still quite a challenge. In this paper, we propose a layered architecture for agents where each component is a kind of a stack-based state machine of our own. This model complements extended finite-state machines with some basic state stack operations that enable not only dealing with hierarchy but also with planning, which is a key element for belief-desire-intention (BDI) agents. Special care was taken to make the representation of these extended finite-state stack machines (EFS2M) simple so that their programming is straightforward. Through an educational example we show how such class of models are, and the potentiality of the solution. The taxi fleet simulation model is a metaphor for transportation systems in structured environments like factories or warehouses but can also be used as a vehicle traffic simulator. As for the latter case, we illustrate how it can be used to determine the efficiency and the quality of service of a taxi fleet in an urban area. [ABSTRACT FROM AUTHOR]

Ensuring safety in human–robot collaboration is one of the main challenges in mobile robotics today. Semantic maps are a potential solution because they provide semantic knowledge in addition to the geometric representation of the environment. They allow robots to perform their basic tasks using geometric representation, mainly localization, path planning and navigation, and additionally allow them to maintain a cognitive interpretation of the environment in order to reason and make decisions based on the context. The goal of this paper is to briefly review semantic mapping for a single mobile robot in indoor environments, and then focus on collaborative mobile semantic mapping. In both contexts, the semantic mapping process is divided into modules/tasks, and recent solutions for each module are discussed. Possible system architectures are also discussed for collaborative semantic mapping. Finally, future directions are highlighted.

Multi-robot patrolling represents a fundamental problem for many monitoring and surveillance applications and has gained significant interest in recent years. In patrolling, mobile robots repeatedly travel through an environment, capture sensor data at certain sensing locations and deliver this data to the base station in a way that maximizes the changes of detection. Robots move on tours, exchange data when they meet with robots on neighboring tours and so eventually deliver data to the base station. In this paper we jointly consider two important optimization criteria of multi-robot patrolling: (i) idleness, i.e. the time between consecutive visits of sensing locations, and (ii) delay, i.e. the time between capturing data at the sensing location and its arrival at the base station. We systematically investigate the effect of the robots' moving directions along their tours and the selection of meeting points for data exchange. We prove that the problem of determining the movement directions and meeting points such that the data delay is minimized is NP-hard. For this purpose, we define a structure called tour graph which models the neighborhood of the tours defined by potential meeting points. We propose two heuristics that are based on a shortest-path-search in the tour graph. We provide a simulation study which shows that the cooperative approach can outperform an uncooperative approach where every robot delivers the captured data individually to the base station. Additionally, the experiments show that the heuristic which is computational more expensive performs slightly better on average than the less expensive heuristic in the considered scenarios. [ABSTRACT FROM AUTHOR]

Localization based on scalar field map matching (e.g., using gravity anomaly, magnetic anomaly, topographics, or olfaction maps) is a potential solution for navigating in Global Navigation Satellite System (GNSS)-denied environments. In this paper, a scalable framework is presented for cooperatively localizing a group of agents based on map matching given a prior map modeling the scalar field. In order to satisfy the communication constraints, each agent in the group is assigned to different subgroups. A locally centralized cooperative localization method is performed in each subgroup to estimate the poses and covariances of all agents inside the subgroup. Each agent in the group, at the same time, could belong to multiple subgroups, which means multiple pose and covariance estimates from different subgroups exist for each agent. The improved pose estimate for each agent at each time step is then solved through an information fusion algorithm. The proposed algorithm is evaluated with two different types of scalar field based simulations. The simulation results show that the proposed algorithm is able to deal with large group sizes (e.g., 128 agents), achieve 10-m level localization performance with 180 km traveling distance, while under restrictive communication constraints.

Due to the epidemic threat, more and more companies decide to automate their production lines. Given the lack of adequate security or space, in most cases, such companies cannot use classic production robots. The solution to this problem is the use of collaborative robots (cobots). However, the required equipment (force sensors) or alternative methods of detecting a threat to humans are usually quite expensive. The article presents the practical aspect of collision detection with the use of a simple neural architecture. A virtual force and torque sensor, implemented as a neural network, may be useful in a team of collaborative robots. Four different approaches are compared in this article: auto-regressive (AR), recurrent neural network (RNN), convolutional long short-term memory (CNN-LSTM) and mixed convolutional LSTM network (MC-LSTM). These architectures are analyzed at different levels of input regression (motor current, position, speed, control velocity). This sensor was tested on the original CURA6 robot prototype (Cooperative Universal Robotic Assistant 6) by Intema. The test results indicate that the MC-LSTM architecture is the most effective with the regression level set at 12 samples (at 24 Hz). The mean absolute prediction error obtained by the MC-LSTM architecture was approximately 22 Nm. The conducted external test (72 different signals with collisions) shows that the presented architecture can be used as a collision detector. The MC-LSTM collision detection f1 score with the optimal threshold was 0.85. A well-developed virtual sensor based on such a network can be used to detect various types of collisions of cobot or other mobile or stationary systems operating on the basis of human-machine interaction.

This paper deals with the problem of estimating in a collaborative way the gradient and the Hessian matrix of an unknown signal via noisy measurements collected by a group of robots. We propose symmetric formations with a reduced number of robots for both the two-dimensional (2-D) and the three-dimensional (3-D) cases, such that the gradient and Hessian of the signal are estimated at the center of the formation via simple computation on local quantities independently of the orientation of the formation. If only gradient information is required, the proposed formations are suitable for mobile robots that need to move in circular motion. We also provide explicit bounds for the approximation error and for the noise perturbation that can be used to optimally scale the formation radius. Numerical simulations illustrate the performance of the proposed strategy for source seeking against alternative solutions available in the literature and show how Hessian estimation can provide faster convergence even in the presence of noisy measurements. [ABSTRACT FROM AUTHOR]

The focus of this research is to introduce the concept of combined intelligent control (CIC) as an effective architecture for decision making and control of intelligent agents and multi robot sets. Basically, the CIC is a combination of various architectures and methods from fields such as artificial intelligence, Distributed Artificial Intelligence (DAI), control and biological computing. Although any intelligent architecture may be very effective for some specific applications, it could be less for others. Therefore, CIC combines and arranges them in a way that the strengths of any approach cover the weaknesses of others. In this paper first, we introduce some intelligent architectures from a new aspect. Afterward, we offer the CIC by combining them. CIC has been executed in a multi agent set. In this set, robots must cooperate to perform some various tasks in a complex and nondeterministic environment with a low sensory feedback and relationship. In order to investigate, improve, and correct the combined intelligent control method, simulation software has been designed which will be presented and considered. To show the ability of the CIC algorithm as a distributed architecture, a central algorithm is designed and compared with the CIC.

External metrology systems are increasingly being used in modern manufacturing to improve the accuracy of industrial robots. In this paper, the problem of achieving absolute accuracy in the positioning and movement of cooperating robots is addressed using the indoor GPS (iGPS) technology as an external position measurement system for real-time feedback and control. This metrology system is presented as an introduction to the iGPS-based 3D Pose Detector and a new concept using generalised measurement systems inspired by iGPS. Attached to the robot end-effectors, the receivers allow coordinate frame measurements to provide spatial information on the robot poses in six degrees of freedom. Experimental results show a strong correspondence between iGPS measurements of cooperating robot end-effector positioning and the control measurements obtained from a double ballbar. Ballbar measurements are further used to determine the relative accuracy between state-of-the-art cooperating manipulators. The iGPS system is validated as an external measurement system using a ballbar device, and its use in the external control of basic robotic tasks is demonstrated. The predicted accuracy achievable for the robots when being controlled or compensated is determined to be at least within 0.3 mm, subject to improvements with continuing research and refinements. [ABSTRACT FROM AUTHOR]

The focus of this research is to introduce the concept of combined intelligent control (CIC) as an effective architecture for decision making and control of intelligent agents and multi robot sets. Basically, the CIC is a combination of various architectures and methods from fields such as artificial intelligence, Distributed Artificial Intelligence (DAI), control and biological computing. Although any intelligent architecture may be very effective for some specific applications, it could be less for others. Therefore, CIC combines and arranges them in away that the strengths of any approach cover the weaknesses of others. In this paper first, we introduce some intelligent architectures from a new aspect. Afterward, we offer the CIC by combining them. CIC has been executed in a multi-agent set. In this set, robots must cooperate to perform some various tasks in a complex and nondeterministic environment with alow sensory feed back and relationship. In order to investigate, improve, and correct the combined intelligent control method, simulation software has been designed which will be presented and considered. To show the ability of the CIC algorithm as a distributed architecture, a central algorith misdesigned and compared with the CIC. [ABSTRACT FROM AUTHOR]

This paper presents the formulation and application of a strategy for the determination of an optimal trajectory for a multiple robotic configuration. Genetic Algorithm (GA) and Simulated Annealing (SA) have been used as the optimization techniques and results obtained from them compared. First, the motivation for multiple robot control and the current state-of-art in the field of cooperating robots are briefly given. This is followed by a discussion of energy minimization techniques in the context of robotics, and finally, the principles of using genetic algorithms and simulated annealing as an optimization tool are included. The initial and final positions of the end effector are specified. Two cases, one of a single manipulator, and the other of two cooperating manipulators carrying a common payload illustrate the proposed approach. The GA and SA techniques identify the optimal trajectory based on minimum joint torque requirements. The simulations performed for both the cases show that although both the methods converge to the global minimum, the SA converges to solution faster than the GA. [Copyright &y& Elsevier]

Localization based on scalar field map matching (e.g., using gravity anomaly, magnetic anomaly, topographics, or olfaction maps) is a potential solution for navigating in Global Navigation Satellite System (GNSS)-denied environments. In this paper, a scalable framework is presented for cooperatively localizing a group of agents based on map matching given a prior map modeling the scalar field. In order to satisfy the communication constraints, each agent in the group is assigned to different subgroups. A locally centralized cooperative localization method is performed in each subgroup to estimate the poses and covariances of all agents inside the subgroup. Each agent in the group, at the same time, could belong to multiple subgroups, which means multiple pose and covariance estimates from different subgroups exist for each agent. The improved pose estimate for each agent at each time step is then solved through an information fusion algorithm. The proposed algorithm is evaluated with two different types of scalar field based simulations. The simulation results show that the proposed algorithm is able to deal with large group sizes (e.g., 128 agents), achieve 10-m level localization performance with 180 km traveling distance, while under restrictive communication constraints. [ABSTRACT FROM AUTHOR]

Fielding robots in complex applications can stress the human operators responsible for supervising them, particularly because the operators might understand the applications but not the details of the robots. Our answer to this problem has been to insert agent technology between the operator and the robotic platforms. In this paper, we motivate the challenges in defining, developing, and deploying the agent technology that provides the glue in the application of tasking unmanned ground vehicles in a military setting. We describe how a particular suite of architectural components serves equally well to support the interactions between the operator, planning agents, and robotic agents, and how our approach allows autonomy during planning and execution of a mission to be allocated among the human and artificial agents. Our implementation and demonstrations (in real robots and simulations) of our multi-agent system shows significant promise for the integration of unmanned vehicles in military applications. [ABSTRACT FROM AUTHOR]