Search Results for « Multi-Swarm Herding »

This paper studies a defense approach against one or more swarms of adversarial agents. In our earlier work, we employ a closed formation (`StringNet') of defending agents (defenders) around a swarm of adversarial agents (attackers) to confine their motion within given bounds, and guide them to a safe area. The control design relies on the assumption that the adversarial agents remain close enough to each other, i.e., within a prescribed connectivity region. To handle situations when the attackers no longer stay within such a connectivity region, but rather split into smaller swarms (clusters) to maximize the chance or impact of attack, this paper proposes an approach to learn the attacking sub-swarms and reassign defenders towards the attackers. We use a `Density-based Spatial Clustering of Application with Noise (DBSCAN)' algorithm to identify the spatially distributed swarms of the attackers. Then, the defenders are assigned to each identified swarm of attackers by solving a constrained generalized assignment problem. Simulations are provided to demonstrate the effectiveness of the approach.

A leader-follower framework is proposed for multi-robot navigation of large scale teams where the leader agents corral the follower agents. A group of leaders is modeled as a 2D deformable object where discrete masses (i.e., leader robots) are interconnected by springs and dampers. A time-varying domain is defined by the positions of leaders while the external forces induce deformations of the domain from its nominal configuration. The team of followers is performing coverage over the time-varying domain by employing a perspective transformation that maps between the nominal and deformed configurations. A decentralized control strategy is proposed where a leader only takes local sensing information and information about its neighbors (connected by virtual springs and dampers), and a follower only needs partial information about leaders and information about its Delaunay neighbors.

This paper introduces the PMST-CHIO, a novel variant of the Coronavirus Herd Immunity Optimizer (CHIO) algorithm, exclusively tailored for individual unmanned aerial vehicle (UAV) path planning in complex 3D environments. While acknowledging and building upon the foundational principles derived from UAV swarm path planning research, the PMST-CHIO distinctively focuses on optimizing the trajectory of single UAVs. It innovatively integrates a parallel multi-swarm treatment mechanism, enhancing the standard CHIO’s exploration and exploitation capabilities significantly. This mechanism diverges from the swarm-based approaches by deploying multiple instances of the CHIO optimizer, each functioning autonomously within its sub-swarm, thereby facilitating independent path planning for individual UAVs. These multiple CHIO instances or CHIO candidates, operate in concert to determine the optimal and collision-free routes, taking into account the unique characteristics of individual UAVs and the intricacies of the service area. The algorithm incorporates two key mechanisms: 1) global exploitation, employing the best solution identified by the highest performing CHIO candidate across the swarms; and 2) strategic shift from parallel multi-swarm exploration to focused exploration by the top-performing CHIO candidate after a specific iteration threshold is reached. This adaptation significantly improves the algorithm’s global search efficiency, convergence behavior, and navigational accuracy under challenging environments. Extensive simulations and comparative studies validate that the PMST-CHIO can effectively overcome the limitations of the standard CHIO algorithm, yielding safer, shorter, and more compliant flight paths for individual UAVs in intricate 3D landscapes.

Behavioural diversity has been demonstrated as beneficial in biological social systems, such as insect colonies and human societies, as well as artificial systems such as large-scale software and swarm-robotics systems. Evolutionary swarm robotics is a popular experimental platform for demonstrating the emergence of various social phenomena and collective behaviour, including behavioural diversity and specialization. However, from an automated design perspective, the evolutionary conditions necessary to synthesize optimal collective behaviours (swarm-robotic controllers) that function across increasingly complex environments (difficult tasks), remains unclear. Thus, we introduce a comparative study of behavioural-diversity maintenance methods (swarm-controller extension of the MAP-Elites algorithm) versus those without behavioural diversity mechanisms (Steady-State Genetic Algorithm), as a means to evolve suitable degrees of behavioural diversity over increasingly difficult collective behaviour (sheep-dog herding) tasks. In support of previous work, experiment results demonstrate that behavioural diversity can be generated without specific speciation mechanisms or geographical isolation in the task environment, although the direct evolution of a functionally (behaviorally) diverse swarm does not yield high task performance.

Abstract The present paper aims to design and simulate an adversarial strategy where a swarm of quadrotor UAVs is herding anti-aircraft land vehicles (AALV) that actively oppose the swarm’s objective by potentially taking them down. The main strategy is to block the AALVs’ line of sight to their goal zone (AALVs’ objective), shifting its trajectory so it reaches a kill zone instead (UAVs’ objective). The counter-swarm strategy performed by the AALVs consists of taking down the closest aerial units to the goal zone. As a result, a consensus algorithm is executed by the UAVs in order to assess the communication network and re-group. Consensus is based on the propagation of local observations that converge into a global agreement on a communication graph. Re-grouping is done via positioning around the kill zone vector or preferring an anti-clockwise formation to better close gaps. The adversarial strategy was tested in an empty arena and urban setting, the latter making use of a path-planning procedure that re-routes the AALV trajectory based on its current destination. Simulation results show a maximum UAV mission success rate converging to roughly 80% in the empty arena. When targeted elimination procedures are executed, UAV mission performance drops 5%, making no distinction between re-grouping strategies in the empty arena. The urban setting shows lower performance due to navigation complexity but favors the decision to re-group based on a formation that close gaps rather than positioning around the kill zone vector.

Multi-robot systems can accomplish tasks that are difficult for a single robot by organizing into task-specific formations. Different from existing studies on multi-robot shape formation, we here study the task-oriented formation decision problem, with a focus on the herding task. This task is challenging due to the attackers' superior maneuverability and their unknown strategies. To address these challenges, we propose the following novel results. First, we encode the formation shape using a low-dimensional parameter vector. This parametric representation reformulates the formation decision as a parameter optimization problem, thereby resolving the limited flexibility of predefined shapes. By optimizing these formation parameters, the defenders' maneuverability disadvantage is mitigated through a formation shape that continuously adapts to task requirements. Second, we develop a reinforcement learning-based policy to regulate the formation parameters. Trained offline in simulations covering diverse attacking strategies, the learned policy can effectively handle adversarial unpredictability during online deployment. Comparative simulations against three baselines demonstrate that our method can successfully accomplish challenging herding tasks. Additional scalability simulations further verify its applicability to simulated scenarios involving dozens of robots. We also validate the practical feasibility of our method on a physical robotic platform with 3 attackers and 7 defenders.

This paper studies a defense approach against a swarm of adversarial agents. We employ a closed formation (`StringNet') of defending agents around the adversarial agents to restrict their motion and guide them to a safe area while navigating in an obstacle-populated environment. Control laws for forming the StringNet and guiding it to a safe area are developed, and the stability of the closed-loop system is analyzed formally. The adversarial swarm is assumed to move as a flock in the presence of rectangular obstacles. Simulation results are provided to demonstrate the efficacy of the approach.

This article presents a multimode solution to the problem of defending a circular protected area (target) from a wide range of attacks by swarms ofrisk-takingand/orrisk-averseattacking agents (attackers). The proposed multimode solution combines two defense strategies, namely: 1) an interception strategy for a team of defenders to intercept multiplerisk-takingattackers while ensuring that the defenders do not collide with each other; 2) a herding strategy to herd a swarm ofrisk-averseattackers to a safe area. In particular, we develop mixed integer programs (MIPs) and geometry-inspired heuristics to distribute and assign and/or reassign the defenders to interception and herding tasks under different spatiotemporal behaviors by the attackers such as splitting into smaller swarms to evade defenders easily or high-speed maneuvers by some risk-taking attackers to maximize damage to the protected area. We provide theoretical as well as numerical comparison of the computational costs of these MIPs and the heuristics, and demonstrate the overall approach in simulations.

This paper studies a defense approach against one or more swarms of adversarial agents. In our earlier work, we employed a closed formation ("StringNet") of defending agents (defenders) around a swarm of adversarial agents (attackers) to confine their motion within given bounds, and guide them to a safe area. The adversarial agents were assumed to remain close enough to each other, i.e., within a prescribed connectivity region. To handle situations when the attackers no longer stay within such a connectivity region, but rather split into smaller swarms (clusters) to maximize the chance or impact of attack, this paper proposes an approach to learn the attacking sub-swarms and reassign defenders toward the attackers. We use a "Density-based Spatial Clustering of Application with Noise (DBSCAN)" algorithm to identify the spatially distributed swarms of the attackers. Then, the defenders are assigned to each identified swarm of attackers by solving a constrained generalized assignment problem. We also provide conditions under which defenders can successfully herd all the attackers. The efficacy of the approach is demonstrated via computer simulations, as well as hardware experiments with a fleet of quadrotors.

This paper presents a defense approach to safeguard a protected area against an attack by a swarm of adversarial agents in three-dimensional (3D) space. We extend our 2D `stringNet Herding' approach, in which a closed formation of string-barriers is established around the adversarial swarm to confine their motion and herd them to a safe area, to 3D spaces by introducing 3D-StringNet. 3D-StringNet is a closed 3D formation of triangular netlike barriers. We provide a systematic approach to generate three types of 3D formations that are used in the 3D herding process and modifications to the finite-time convergent control laws developed in our earlier work. Furthermore, for given initial positions of the defenders, we provide conditions on the initial positions of the attackers for which the defenders are guaranteed to gather as a specified formation at a position on the shortest path of the attackers to the protected area before attackers reach there. The approach is investigated in simulations.