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We consider the problem of optimal unsignalized intersection management, wherein we seek to obtain safe and optimal trajectories, for a set of robots that arrive randomly and continually. This problem involves repeatedly solving a mixed integer program (with robot acceleration trajectories as decision variables) with different parameters, for which the computation time using a naive optimization algorithm scales exponentially with the number of robots and lanes. Hence, such an approach is not suitable for real-time implementation. In this paper, we propose a solution framework that combines learning and sequential optimization. In particular, we propose an algorithm for learning a shared policy that given the traffic state information, determines the crossing order of the robots. Then, we optimize the trajectories of the robots sequentially according to that crossing order. This approach inherently guarantees safety at all times. We validate the performance of this approach using extensive simulations and compare our approach against 5 different heuristics from the literature in 9 different simulation settings. Our approach, on average, significantly outperforms the heuristics from the literature in various metrics like objective function, weighted average of crossing times and computation time. For example, in some scenarios, we have observed that our approach offers up to 150% improvement in objective value over the first come first serve heuristic. Even on untrained scenarios, our approach shows a consistent improvement (in objective value) of more than 30% over all heuristics under consideration. We also show through simulations that the computation time for our approach scales linearly with the number of robots (assuming all other factors are constant). We further implement the learnt policies on physical robots with a few modifications to the solution framework to address real-world challenges and establish its real-time implementability.

A humanoid robot called BarelangFC was designed to take part in the Kontes Robot Indonesia (KRI) competition, in the robot coordination division. In this division, each robot is expected to recognize its opponents and to pass the ball towards a team member to establish coordination between the robots. In order to achieve this team coordination, a fast and accurate system is needed to detect and estimate the other robot’s position in real time. Moreover, each robot has to estimate its team members’ locations based on its camera reading, so that the ball can be passed without error. This research proposes a Tiny-YOLO deep learning method to detect the location of a team member robot and presents a real-time coordination system using a ZED camera. To establish the coordinate system, the distance between the robots was estimated using a trigonometric equation to ensure that the robot was able to pass the ball towards another robot. To verify our method, real-time experiments was carried out using an NVDIA Jetson NX Xavier, and the results showed that the robot could estimate the distance correctly before passing the ball toward another robot. [ABSTRACT FROM AUTHOR]

As cyber–physical networks become increasingly equipped with embedded capabilities, they are made vulnerable to malicious attacks with the increased number of access points available to attackers. A particularly pernicious attack is spoofing, in which a malicious agent spawns multiple identities and can compromise otherwise attack–resilient algorithms that rely on assumed network robustness structures. We generalize a class of resilient consensus strategies, known as weighted mean-subsequence-reduced (W-MSR) consensus, to further provide spoof resilience by incorporating a physical layer authentication. By comparing the physical fingerprints of received signals, legitimate agents can identify and isolate malicious agents that attempt spoofing attacks. A key technical contribution is to quantify worst case misclassification probability using distributionally robust Chebyshev bounds computed via semidefinite programming when the physical fingerprints of received signals are stochastic. Numerical simulations and experimental results illustrate the effectiveness of the proposed methods. Our framework is applicable to a variety of problems involving multirobot systems coordinating via wireless communication. [ABSTRACT FROM AUTHOR]

Abstract Self-assembling robots have the potential to undergo autonomous morphological adaptation. However, due to the simplicity in their hardware makeup and their limited perspective of the environment, self-assembling robots are often not able to reach their potential and adapt their morphologies to tasks or environments without external cues or prior information. In this paper, we present supervised morphogenesis — a control methodology that makes self-assembling robots truly flexible by enabling aerial robots to exploit their elevated position and better view of the environment to initiate and control (hence supervise) morphology formation on the ground. We present results of two case studies in which we assess the feasibility of the presented methodology using real robotic hardware. In the case studies, we implemented supervised morphogenesis using two different aerial platforms and up to six self-assembling autonomous robots. We furthermore quantify the benefits attainable for self-assembling robots through cooperation with aerial robots using simulation-based studies. The research presented in this paper is a significant step towards realizing the true potential of self-assembling robots by enabling autonomous morphological adaptation to a priori unknown tasks and environments. [ABSTRACT FROM AUTHOR]

In a recent French robotic contest, the objective was to develop a multi-robot system able to autonomously map and explore an unknown area while also detecting and localizing objects. As a participant in this challenge, we proposed a new decentralized Markov decision process (Dec-MDP) resolution based on distributed value functions (DVF) to compute multi-robot exploration strategies. The idea is to take advantage of sparse interactions by allowing each robot to calculate locally a strategy that maximizes the explored space while minimizing robots interactions. In this paper, we propose an adaptation of this method to improve also object recognition by integrating into the DVF the interest in covering explored areas with photos. The robots will then act to maximize the explored space and the photo coverage, ensuring better perception and object recognition.

The cops-and-robber (CR) game has been used in mobile robotics as a discretized model (played on a graph G) of pursuit/evasion problems. The “classic” CR version is a perfect information game: the cops’ (pursuer’s) location is always known to the robber (evader) and vice versa. Many variants of the classic game can be defined: the robber can be invisible and also the robber can be either adversarial (tries to avoid capture) or drunk (performs a random walk). Furthermore, the cops and robber can reside in either nodes or edges of G. Several of these variants are relevant as models or robotic pursuit/evasion. In this paper, we first define carefully several of the variants mentioned above and related quantities such as the cop number and the capture time. Then we introduce and study the cost of visibility (COV), a quantitative measure of the increase in difficulty (from the cops’ point of view) when the robber is invisible. In addition to our theoretical results, we present algorithms which can be used to compute capture times and COV of graphs which are analytically intractable. Finally, we present the results of applying these algorithms to the numerical computation of COV.

Self-replicating robots represent a new area for prospective advancement in robotics. A self-replicating robot can identify when additional robots are needed to solve a problem or meet user needs, and create them in response to this identified need. This allows robotic systems to respond to changing (or non-predicted) mission needs. Being able to modify the physical system component provides an additional tool for optimizing robotic system performance. This paper begins the process of developing a command and coordination system that makes decisions with the consideration of replication, repair, and retooling capabilities. A high-level algorithm is proposed and qualitatively assessed. [ABSTRACT FROM AUTHOR]

Self-replicating robots represent a new area for prospective advancement in robotics. A self-replicating robot can identify when additional robots are needed to solve a problem or meet user needs, and create them in response to this identified need. This allows robotic systems to respond to changing (or non-predicted) mission needs. Being able to modify the physical system component provides an additional tool for optimizing robotic system performance. This paper begins the process of developing a command and coordination system that makes decisions with the consideration of replication, repair, and retooling capabilities. A high-level algorithm is proposed and qualitatively assessed.

Underwater mobile sensor networks such as Autonomous Underwater Vehicles (AUVs) or robots are envisioned to enable applications for oceanographic data collection, environmental and pollution monitoring, offshore exploration, and distributed tactical surveillance. These applications require running compute- and data-intensive algorithms that go beyond the capabilities of the individual AUVs that are involved in a mission. To execute these task-parallel algorithms in resource- and time-constrained environments, dynamic and reliable collaboration between local networked robots (e.g., AUVs) and remote public Clouds is needed. To this end, the heterogeneous sensing, computing, communication, and storage capabilities of local and remote resources are exploited to form a “loosely coupled” mobile Cloud, and a novel resource provisioning engine that dynamically takes decisions on “what” and “where” the tasks should be executed in the mobile Cloud is introduced. Comparison of benefits of collaboration between local and Cloud resources with purely local and centralized approaches are presented through exhaustive computer simulations. [ABSTRACT FROM PUBLISHER]

Abstract: This paper introduces and studies the application of Constrained Sampling Evolutionary Algorithms in the framework of an UAV based search and rescue scenario. These algorithms have been developed as a way to harness the power of Evolutionary Algorithms (EA) when operating in complex, noisy, multimodal optimization problems and transfer the advantages of their approach to real time real world problems that can be transformed into search and optimization challenges. These types of problems are denoted as Constrained Sampling problems and are characterized by the fact that the physical limitations of reality do not allow for an instantaneous determination of the fitness of the points present in the population that must be evolved. A general approach to address these problems is presented and a particular implementation using Differential Evolution as an example of CS-EA is created and evaluated using teams of UAVs in search and rescue missions. The results are compared to those of a Swarm Intelligence based strategy in the same type of problem as this approach has been widely used within the UAV path planning field in different variants by many authors. [Copyright &y& Elsevier]

Abstract: Nature teaches us how the collaboration between the members of a herd is an important aspect to ensure their survival as a group. Collaboration requires communication, but it may happen that it could not be established for some reason. In this context, members of the herd have to make decisions on their own, trying to do the best for the group. Inspired by this principle, we have designed a method to overcome the communication difficulties of the environment. The team members compete using their own decisions, and this competition affects the best possible way for the team to benefit. [Copyright &y& Elsevier]

Abstract: In this paper, we propose a compliant motion control strategy for handling a single object by two similar industrial robots. The dynamics of the object carried by the two robots is assimilated to the dynamics of a mass-spring-damper system described by a piecewise linear model (PWA). The coordination of the two robots is accomplished using a master slave synchronization approach dedicated for PWA systems, based on the Lyapunov theory, and solved via Linear Matrix Inequalities (LMIs). The performances of the proposed approach are proved by simulation results and compared to a related approach with larger stability criteria. [Copyright &y& Elsevier]

Abstract: This paper presents a Probabilistic Road Map (PRM) motion planning algorithm to be queried within Dynamic Robot Networks—a multi-robot coordination platform for robots operating with limited sensing and inter-robot communication. First, the Dynamic Robot Networks (DRN) coordination platform is introduced that facilitates centralized robot coordination across ad hoc networks, allowing safe navigation in dynamic, unknown environments. As robots move about their environment, they dynamically form communication networks. Within these networks, robots can share local sensing information and coordinate the actions of all robots in the network. Second, a fast single-query Probabilistic Road Map (PRM) to be called within the DRN platform is presented that has been augmented with new sampling strategies. Traditional PRM strategies have shown success in searching large configuration spaces. Considered here is their application to on-line, centralized, multiple mobile robot planning problems. New sampling strategies that exploit the kinematics of non-holonomic mobile robots have been developed and implemented. First, an appropriate method of selecting milestones in a PRM is identified to enable fast coverage of the configuration space. Second, a new method of generating PRM milestones is described that decreases the planning time over traditional methods. Finally, a new endgame region for multi-robot PRMs is presented that increases the likelihood of finding solutions given difficult goal configurations. Combining the DRN platform with these new sampling strategies, on-line centralized multi-robot planning is enabled. This allows robots to navigate safely in environments that are both dynamic and unknown. Simulations and real robot experiments are presented that demonstrate: (1) speed improvements accomplished by the sampling strategies, (2) centralized robot coordination across Dynamic Robot Networks, (3) on-the-fly motion planning to avoid moving and previously unknown obstacles and (4) autonomous robot navigation towards individual goal locations. [Copyright &y& Elsevier]