Consensus-based Bundle Algorithm Research Articles

Heterogeneous Multi-UAV Mission Reallocation Based on Improved Consensus-Based Bundle Algorithm

Heterogeneous Multi-UAV Mission Reallocation Based on Improved Consensus-Based Bundle Algorithm

An Efficient Distributed Task Allocation Method for Maximizing Task Allocations of Multirobot Systems

This paper addresses the distributed task allocation problem for maximizing the total number of successfully executed tasks of multirobot systems. Due to the deadline time of tasks and fuel limits of robotic vehicles, not all tasks can be successfully executed sometimes. Based on the performance impact (PI) algorithm, an effective and efficient performance impact (EEPI) algorithm is proposed, its novelty lies in its cost function and task release procedure. The fundamental ideas of the proposed cost function are as follows. First, the traveling time from the initial position of each vehicle to the positions of its tasks is minimized, so that more time can be left for the vehicle to execute more tasks due to the limited fuel. Second, the start time of each task should be close enough to its deadline, so that tasks with earlier deadlines can be assigned earlier than those with later deadlines. To avoid invalid removal performance impacts (RPIs) and inclusion performance impacts (IPIs), the tasks assigned to a vehicle are all released if the number of tasks removed by the vehicle during the task removal phase is the most, which further increases the total number of successfully executed tasks. Both simulations and hardware-in-the-loop experiments suggest that compared with the state-of-the-art distributed task allocation algorithms, the proposed EEPI is not only effective in maximizing the number of successfully executed tasks but efficient in saving the number of iterations and time to converge. <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">Note to Practitioners</i> —This work was motivated by the limitations of the existing distributed task allocation algorithms for maximizing the total number of successfully executed tasks. The consensus-based bundle algorithm (CBBA) has been proven to guarantee convergence and 50% optimality under the diminishing marginal gain (DMG) assumption in previously published works. Based on CBBA, a performance impact (PI) algorithm was proposed, and simulations show that it can assign more tasks than CBBA when applied to time-critical scenarios with low task-to-vehicle ratios. Starting from the results of PI, a rescheduling method named PI for maximizing assignments (PI-maxAss) was proposed, which has been demonstrated to assign more tasks than PI with high task-to-vehicle ratios. However, much more iterations and time are required by PI-maxAss to converge to globally consistent assignments because of the rescheduling. Due to the above considerations, an effective and efficient performance impact (EEPI) algorithm is proposed in this paper to maximize the number of successfully executed tasks without any rescheduling. Both simulations and hardware-in-the-loop experiments suggest that compared with the algorithms mentioned above, the proposed EEPI is effective in maximizing the number of successfully executed tasks and efficient in saving the number of iterations and time to converge. In future work, the distributed task allocation problem in which several vehicles execute a task at the same time cooperatively or a vehicle executes several tasks simultaneously will be further addressed.

Auction-Based Distributed Task Allocation Algorithm for Drone Swarms

Research on drone swarms has gained traction in recent times due to their ability to outperform single drones in various tasks. In order for autonomous drone swarms to carry out tasks effectively, it is crucial that the tasks are distributed among the drones. This poses a considerable challenge that must be addressed in the advancement of drone swarm technology. The assignment problem is basically defined as the process of assigning M tasks to N drones in a way that minimizes the total cost. This paper presents an auction-based distributed task allocation algorithm called Harmony DTA (Drone Task Allocation) for heterogeneous drone swarms. Prior research primarily focuses on minimizing overall costs associated with assignments. In contrast, Harmony DTA not only minimizes total costs through an enhanced cost calculation function, but also ensures equitable distribution of workload among drones. Additionally, the proposed two-stage auction process reduces the total message size utilized during communication. Matlab simulations and field tests were conducted to assess the effectiveness of the proposed algorithm. Moreover, the algorithm's performance was evaluated in comparison to the CBBA (Consensus Based Bundle Algorithm). The results obtained indicated that the proposed algorithm is capable of assigning tasks with high efficiency, without encountering conflicts.

Multi-AUV underwater static target search method based on consensus-based bundle algorithm and improved Glasius bio-inspired neural network

This research introduces a hierarchical strategy for static target searches with multi-autonomous underwater vehicles (AUVs) to optimize cumulative search rewards. The approach comprises two primary elements: task allocation and path planning. A Voronoi diagram segments regions based on peak detection via a maximum filter in the task allocation stage. Then, a consensus-based bundling algorithm ensures the load-balanced distribution of peak sub-regions across AUVs, while a dynamic cooperation mechanism allows for dynamic adjustment of task allocation, thereby increasing the system's operational flexibility. Path planning employs an improved Glasius bio-inspired neural network, leveraging analogies to convolution processes and incorporating mean pooling, multiple convolutions, and resampling. This method enhances global information propagation and optimizes path point selection through a discounted reward function evaluating adjacent nodes, thus boosting the search efficiency of individual AUVs. Simulation experiments validate the method's effectiveness and robustness in multi-AUV static target searches, demonstrating its potential to improve search efficiency.

Multi-Domain Collaborative Task Allocation and Conflict Resolution in Unmanned Systems under Complex Constraints

Under the background that multi-domain collaborative tasks with complex constraints executed by heterogeneous unmanned systems, based on a consensus-based bundle algorithm, a consensus-based synergy algorithm is proposed to solve the distributed multi-domain collaborative task planning problem. First, a multi-domain collaborative task allocation model and score evaluation system are established by considering task resource requirement constraints, task timing constraints, and path threat constraints. Second, the time sequence constraints of collaborative tasks are transformed into elastic time window constraints. The bidding method based on collaborative constraints is used for task allocation, and the improved consistency conflict resolution principle is adopted to realize distributed multi-domain collaborative task allocation conflict resolution. Finally, the path planning is coupled to the task allocation process by using the Bezier curve path, and the results of multi-domain collaborative task allocation and path planning are obtained synchronously. Simulation results show that the proposed algorithm can effectively solve the problem of multi-domain collaborative task planning for heterogeneous unmanned systems.

BDBM: A distributed network simplification method for solving task allocation problems

Existing task allocation algorithms often fail to consider communication load and do not meet the scalability requirements of large-scale systems in practical applications. Furthermore, many general network simplification algorithms are centralized and not designed for task allocation scenarios, thus losing their superiority when applied to solve task allocation problems in distributed systems. Inspired by recent network simplification algorithms, this paper introduces a novel network simplification algorithm called bid-based distributed broken-motifs (BDBM), which simplifies the communication network by reducing the number of closed-loop triangles. The BDBM algorithm keeps those communication edges that facilitate the deconfliction based on the initial bids, thus avoiding the surge in the number of convergence iterations caused by the simplification. In addition, CBBA is distributed and requires only two iterations to complete the communication network simplification. Theoretical analysis confirms that the simplified network of BDBM remains connected and can be used in combination with improved task allocation algorithms based on consensus-based bundle algorithm (CBBA). In terms of experiments, we conducted comprehensive experiments in different settings and found that the proposed algorithm does not lead to worse allocation solutions. The statistical results also show that BDBM outperforms state-of-the-art network simplification algorithms in terms of solving efficiency and scalability in assisting CBBA solve large-scale complex task allocation problems.

Integrate Assignment of Multiple Heterogeneous Unmanned Aerial Vehicles Performing Dynamic Disaster Inspection and Validation Task With Dubins Path

The actual performance of a task assignment method of unmanned aerial vehicles (UAVs) shows a considerable dependence on the path planning process coupled with it; however, this topic has been rarely studied in the existing literature. This paper considers the problem of maximizing the task-assignment reward of a fleet of heterogeneous UAVs for a dynamic reconnaissance and confirmation task under constraints of critical time and multi-UAV tasks where the coupled path optimization objectives also need to be considered. The existing consensus-based bundle algorithm is extended with an effective method for managing the multi-task and multi-agent constraints. The proposed method can optimize the inherent coupling path using the Dubins path to reduce the differences between the estimated path and the actual path, shortening the operation time by adopting the distributed genetic algorithm. The proposed method is verified by the sample run tests of a disaster area reconnaissance and confirmation task and Monte Carlo comparison simulations with the two existing algorithms. The results verify both the practicality and advantages of the proposed method.

Decentralized UAV Swarm Scheduling with Constrained Task Exploration Balance

Scheduling is one of the key technologies used in unmanned aerial vehicle (UAV) swarms. Scheduling determines whether a task can be completed and when the task is complete. The distributed method is a fast way to realize swarm scheduling. It has no central node and UAVs can freely join or leave it, thus making it more robust and flexible. However, the two most representative methods, the Consensus-Based Bundle Algorithm (CBBA) and the Performance Impact (PI) algorithm, pursue the minimum cost impact of tasks, which have optimization limitations and are easily cause task conflicts. In this paper, a new concept called “task consideration” is proposed to quantify the impact of tasks on scheduling and the regression of the task itself, balancing the exploration of the UAV for the minimum-impact task and the regression of neighboring tasks to improve the optimization and convergence of scheduling. In addition, the conflict resolution rules are modified to fit the proposed method, and the exploration of tasks is increased by a new removal method to further improve the optimization. Finally, through extensive Monte Carlo experiments, compared with CBBA and PI, the proposed method is shown to perform better in terms of task allocation and total travel time, and with the increase in the number of average UAV tasks, the number of iterations is less and the convergence is faster.

An Air-to-Ground Relay Communication Planning Method for UAVs Swarm Applications

For civil applications of Unmanned Aerial Vehicles (UAVs) for search, and emergency, data transmission between UAVs and Ground Control Stations (GCS) is always suffered from long distance, undulating terrains or other interferences. Thus, relaying is a critical enhancing technology in wireless communications for better service quality. In this paper, a planning method for fixed-wing relay UAV (RU) serving to sequential tasks of UAV swarm is proposed. Firstly, considering characteristics of signal propagation, antenna, environment and UAV, the UAV-to-GCS and UAV-to-UAV communication models are designed. And the limits of communication are investigated, which are crucial issues to provide a guarantee for swarm mission. Meanwhile, based on the analysis of relay UAVs requirements during the spatial distribution of tasks, a mixed integer nonlinear programming problem (MINLP) is constructed to solve the approximate number of relay nodes and access locations. Then aiming for the minimum number of relay UAVs, a continuous time-sharing relay UAVs reward model is constructed, and the consensus-based bundle algorithm (CBBA) is adopted to obtain the maximum rewards, including the number of relay nodes, mission sequences and route planning. Finally, the numerical simulation for a typical scenario is conducted to show the performance of the proposed approach.

Hierarchical optimization method of space patrol task assignment based on deep neural network and consensus-based bundle algorithm

This research looks towards improving the accuracy and efficiency of orbit transfer speed increment estimation and task assignment for space patrol missions involving multiple platforms. First, we set up a quick prediction model for the increment in orbit transfer velocity. Then, a two-layered hybrid optimization framework is developed, with a deep neural network sub-model as the inner layer to find the optimal speed increase and the consensus-based bundle algorithm as the outer layer to solve the multi-task patrol sequence problem. Finally, the simulation and analysis of the patrol task assignment problem are conducted under two types of scenarios. The results show that the hierarchical optimization method proposed in this paper is three orders of magnitude faster than the traditional optimization method. In addition, the suggested hierarchical optimization method has significant promise in practical applications, where it can produce solutions more quickly to adapt to more complex patrol scenarios.

A Distributed Double-Loop Optimization Method with Fast Response for UAV Swarm Scheduling

An unmanned aerial vehicle (UAV) swarm has broad application prospects, in which scheduling is one of the key technologies determining the completion of tasks. A market-based approach is an effective way to schedule UAVs distributively and quickly, meeting the real-time requirements of swarm scheduling without a centre. In this paper, a double-loop framework is designed to enhance the performance of scheduling, where a new task removal method in the outer loop and a local redundant auction method in the inner loop are proposed to improve the optimization of scheduling and reduce iterations. Furthermore, a deadlock detection mechanism is introduced to avoid endless loops and the scheduling with the lowest local cost will be adopted to exit the cycle. Extensive Monte Carlo experiments show that the iterations required by the proposed method are less than the two representative algorithms consensus-based bundle algorithm (CBBA) and performance impact (PI) algorithm, and the number of allocated tasks is increased. In addition, through the deadlock avoidance mechanism, PI can completely converge as the method in this paper.

A bilevel task allocation method for heterogeneous multi-UUV recovery system

This paper presents a novel task allocation method for multiple unmanned underwater vehicles (multi-UUV) recovery systems. Specifically, a bilevel model that enjoys the advantages of centralized and distributed methods is constructed for mothership and cooperative constraints. In the centralized upper-level, an improved Gaussian mixture mode (IGMM) is presented, and an improved discrete crow search algorithm (IDCSA) is proposed. Thus, the leaders are clustered into subgroups, and the matching relationship between motherships and subgroups is established. In the distributed lower-level, the proposed consensus-based bundle algorithm with group constraint (CBBA-GC) addresses the allocation problem between subgroups and heterogeneous follower UUVs. It is found that the proposed bilevel method can achieve conflict-free and load-balanced multi-UUV recovery task allocation when the communication capability of each UUV and motherships recovery ability are heterogeneous.

On-Board Decentralized Observation Planning for LEO Satellite Constellations

The multi-satellite on-board observation planning (MSOOP) is a variant of the multi-agent task allocation problem (MATAP). MSOOP is used to complete the observation task allocation in a fully cooperative mode to maximize the profits of the whole system. In this paper, MSOOP for LEO satellite constellations is investigated, and the decentralized algorithm is exploited for solving it. The problem description of MSOOP for LEO satellite constellations is detailed. The coupled constraints make MSOOP more complex than other task allocation problems. The improved Consensus-Based Bundle Algorithm (ICBBA), which includes a bundle construction phase and consensus check phase, is proposed. A constraint check and a mask recovery are introduced into bundle construction and consensus check to handle the coupled constraints. The fitness function is adjusted to adapt to the characteristics of different scenes. Experimental results on series instances demonstrate the effectiveness of the proposed algorithm.

Intention Prediction of a Hypersonic Glide Vehicle Using a Satellite Constellation Based on Deep Learning

Tracking of hypersonic glide vehicles (HGVs) by a constellation tracking and observation system is an important part of the space-based early warning system. The uncertainty in the maneuver intentions of HGVs has a non-negligible impact on the tracking and observation process. The cooperative scheduling of multiple satellites in an environment of uncertainty in the maneuver intentions of HGVs is the main problem researched in this paper. For this problem, a satellite constellation tracking decision method that considers the HGVs’ maneuver intentions is proposed. This method is based on building an HGV maneuver intention model, developing a maneuver intention recognition and prediction algorithm, and designing a sensor-switching strategy to improve the local consensus-based bundle algorithm (LCBBA). Firstly, a recognizable maneuver intention model that can describe the maneuver types and directions of the HGVs in both the longitudinal and lateral directions was designed. Secondly, a maneuver intention recognition and prediction algorithm based on parallel, stacked long short-term memory neural networks (PSLSTM) was developed to obtain maneuver directions of the HGV. On the basis of that, a satellite constellation tracking decision method (referred to as SS-LCBBA in the following) considering the HGVs’ maneuver intentions was designed. Finally, the maneuver intention prediction capability of the PSLSTM network and two currently popular network structures: the multilayer LSTM (M-LSTM) and the dual-channel and bidirectional neural network (DCBNN) were tested for comparison. The simulation results show that the PSLSTM can recognize and predict the maneuver directions of HGVs with high accuracy. In the simulation of a satellite constellation tracking HGVs, the SS-LCBBA improved the cumulative tracking score compared to the LCBBA, the blackboard algorithm (BM), and the variable-center contract network algorithm (ICNP). Thus, it is concluded that SS-LCBBA has better adaptability to environments with uncertain intentions in solving multi-satellite collaborative scheduling problems.

Consensus-Based Decentralized Task Allocation for Multi-Agent Systems and Simultaneous Multi-Agent Tasks

This letter proposes a novel consensus-based timetable algorithm (CBTA) to solve the decentralized simultaneous multi-agent task allocation problem. Due to the limited capability of each agent, multiple agents may be required to perform a task simultaneously. A key challenge is how to meet the requirements and minimize the average start time of all tasks. The proposed CBTA aims to minimize the start time of each task to minimize the average start time of all tasks indirectly, it iterates between a timetable construction phase and a consensus phase. New tasks are included in the timetable of each agent by comparing the estimated start time of tasks placed by its own and other agents during the timetable construction phase. Then in the consensus phase, agents share their timetables with a communication network, and conflicts among their timetables are eliminated according to a consensus rule. Extensive simulation results show that the average start time of tasks of the proposed CBTA is nearly the same as the consensus-based bundle algorithm (CBBA) when performing single-agent tasks, and it is much less than the consensus-based grouping algorithm (CBGA) when performing multi-agent tasks with various communication network topologies.

A Decentralized Multirobot Spatiotemporal Multitask Assignment Approach for Perimeter Defense

This article provides a new decentralized approach to solve a perimeter defense problem (PDP). In a typical PDP, many intruders try to enter a territory, and a group of defenders operating both inside and on the perimeter protects the territory by capturing the intruders on the perimeter. The objective of the defenders is to detect and capture the intruders before they enter the territory. Defenders sense the intruders independently and compute their trajectories to capture all the intruders in a cooperative way. Each intruder is estimated to reach a specific location on the perimeter at a specific time, and this is considered as a spatiotemporal task to be handled by a defender. At any given time, the PDP is converted to a decentralized multirobot spatiotemporal multitask assignment (DMRST-MTA) problem. The cost of executing a task for a defender is defined by a composite cost function that includes both the spatial and temporal cost components. In this article, a modified decentralized consensus-based bundle algorithm is presented to solve the above spatiotemporal multitask assignment problem. The performance evaluation of the proposed approach is presented based on Monte Carlo studies, and the results show the effectiveness of the proposed approach under different scenarios. The robustness studies also show that the proposed approach is robust against uncertainties in the heading angles of the intruders. The performance comparison with the decentralized adaptive partitioning approach for the various arrival distributions of intruders clearly shows that DMRST-MTA is efficient.

Cooperative Search Method for Multiple UAVs Based on Deep Reinforcement Learning.

In this paper, a cooperative search method for multiple UAVs is proposed to solve the problem of low efficiency of multi-UAV task execution by using a cooperative game with incomplete information. To improve search efficiency, CBBA (Consensus-Based Bundle Algorithm) is applied to designate the tasks area for each UAV. Then, Independent Deep Reinforcement Learning (IDRL) is used to solve Nash equilibrium to improve UAVs’ collaborations. The proposed reward function is smartly developed to guide UAVs to fly along the path with higher reward value while avoiding the collisions between UAVs during flights. Finally, extensive experiments are carried out to compare our proposed method with other algorithms. Simulation results show that the proposed method can obtain more rewards in the same period of time as other algorithms.

Multi-UAV Unbalanced Targets Coordinated Dynamic Task Allocation in Phases

Unmanned aerial vehicles (UAVs) can be used in swarms to achieve multiple tasks cooperatively. Multi-UAV and multi-target cooperative task assignments are difficult. To solve the problem of unbalanced, phased, cooperative assignment between UAVs and tasks, we establish an unbalanced, phased task assignment model that considers the constraints of task execution, time, and target task execution demand. Based on an improved consensus-based bundle algorithm (CBBA), we propose a two-tier task bidding mechanism. According to dynamic changes in new tasks, we study a dynamic assignment strategy and propose a mechanism based on task continuity adjustment and time windows. Finally, a simulation experiment is used to verify the feasibility and effectiveness of the proposed allocation method in multi-UAV target assignment scenarios. The results show that the dynamic task assignment strategy can efficiently assign random new tasks as they arise.

Cooperative Cellular UAV-to-Everything (C-U2X) communication based on 5G sidelink for UAV swarms

A swarm of cellular-connected Unmanned Aerial Vehicles (UAVs) enables new possibilities for emerging services and applications as the UAVs can autonomously coordinate their activities and cooperate to accomplish a given task. Because of spatio-temporal dynamics of swarm topology, robust and reliable network formation with seamless connectivity among UAVs is highly critical for any successful mission. This work focuses on the problem of cooperative and communication-aware UAV channel scheduling of data transmission from a set of target points of interests (PoIs) towards a cellular base station (BS). A novel, cooperative multi-hop communication model based on the C-V2X (Cellular Vehicle-to-Everything) Mode 4 cellular sidelink (PC5) radio interface is presented for efficient UAV data flow scheduling within the swarm. The model design aims at optimizing the cellular communications on UAV-to-UAV (U2U) and UAV-to-Infrastructure (U2I) links via a novel interference-aware scheduling, hence envisioning a new Cellular UAV-to-Everything (C-U2X) communication paradigm. An optimization model is used to solve the centralized version of the problem, while a distributed Dynamic Consensus-Based Bundle Algorithm (D-CBBA) is proposed to generate the best subchannel scheduling for maximizing data transmission in a distributed setting. Extensive simulation results demonstrate that the proposed distributed algorithm is able to extend the cellular infrastructure coverage while improving the multi-hop communications by autonomously adapting to the network conditions.

Receding task allocation method for modular robots during on-orbit assembly

On-orbit assembly of large infrastructures plays a pivotal role in future space missions. Taking the process of extraction of one assembling object, its transportation and placement by one or several modular robots as a single-robot (SR) or multi-robot (MR) assembling tasks, this paper investigates the problem of designing a task allocation method, so as to realize the adaptive coordination among modular robots. A receding task allocation method is developed to solve this problem in a dynamic and decentralized manner. By proposing the receding-task strategy, the allocation over all tasks is formulated as the allocations at each triggering time over receding tasks to reduce the online computational complexity, where the receding optimization and triggering mechanism based on real-time information can improve the adaptability to a dynamic environment. In order to satisfy the precedence constraints from assembly sequence and the special task requirements about MR tasks, a decentralized algorithm which is an extension of the consensus based bundle algorithm is designed for the allocation of receding tasks, which can reduce the online computational burden of individuals. Numerical simulations are carried out to validate the performance of the proposed method.