We present an online underwater localization method that enables passive vehicles to localize themselves by overhearing two-way ranging (TWR) communication between an active vehicle and multiple acoustic anchors. Swarm simulations confirm the scalability of the system and show faster position updates, as well as improved accuracy compared to the active benchmark. A field experiment in a shallow water canal using the low-cost BlueROV2 underwater vehicle and ahoi modems has demonstrated the real-world applicability of the system, achieving an RMSE of approximately 0.82m.
An efficient transportation system leads to increased safety, better financial security, as well as a cleaner environment. We propose an approach for significantly improving efficiency in heterogeneous unregulated traffic environments. Existing intelligent transportation solutions for improving efficiency, however, apply only to traffic intersections with sparse traffic or traffic where drivers obey the regulations, or both. Our approach combines an auction mechanism with an optimization-based trajectory planner. The auction mechanism generates a fair priority entrance sequence for heterogeneous agents while the trajectory planner computes velocity controls in real-time, taking less than 10 milliseconds even in high-density traffic with over 10, 000 vehicles per hour. Compared to fully optimization-based methods, it operates 100x faster while ensuring fairness, safety, and efficiency. Tested on the SUMO simulator, our algorithm improves throughput by at least 25%, reduces the time to reach the goal by at least 70%, and decreases fuel consumption by 50% compared to approaches using traffic lights and stop signs. Our approach is also unaffected by unbalanced traffic inflows, whereas some of the other baselines encountered a decrease in performance in unbalanced traffic inflow environments.
This paper presents an Auction-Consensus Algorithm with a Loss Mechanism (ACALM), a decentralized task allocation method for multi-robot systems that enhances the existing Consensus-Based Auction Algorithm (CBAA) by incorporating a novel loss propagation mechanism. In contrast to purely greedy bidding strategies, it enables agents to dynamically update task priorities based on the accumulated loss from previously unsuccessful bids. This extended work reduces globally inefficient allocations caused by early suboptimal decisions. The proposed approach is evaluated through large-scale simulations in thousands of randomized scenarios and swarm sizes ranging from 5 to 120 robots. Compared to existing CBAA and GCAA algorithms, ACALM yields task assignments with higher global efficiency on average. The results also show that ACALM maintains effectiveness with larger swarms, suggesting strong robustness in large decentralized contexts. However, this improvement comes with an increase in communication overhead due to additional consensus rounds. Potential extensions include methods to reduce communication costs, support for multiple assignments per robot, and verification of functionality under asynchronous communication.
Multi-Agent Teamwise Cooperative Path Finding (TC-MAPF) seeks collision-free paths for the agents from their start to goal locations. In addition, agents are grouped into multiple teams, and each team has its own objective function to optimize. TC-MAPF arises in scenarios such as the coordination of multiple autonomous vehicles at a signal-free traffic intersection, where, for example, the vehicles from each direction naturally forms a team. TC-MAPF was recently studied and optimal planners such as TC-CBS has been developed. While being able to find all Pareto-optimal solutions for TC-MAPF, these optimal planners usually suffer from limited scalability as the number of agents grows. This paper develops a bounded sub-optimal planner TC-CBS-TF for TC-MAPF, trading off solution quality for scalability, by leveraging and extending several bounded sub-optimal search techniques for MAPF to handle multiple teams as in TC-MAPF. We test TC-CBS-TF and baselines on various maps with up to 50 agents, and TC-CBS-TF achieves up to 68 % higher success rates.
We consider the problem of combining potential field and ergodic search on multi-robot systems. Traditional ergodic search algorithms use metrics for ergodicity that account for the desired distribution at different scales. Recently, a heat equation-driven ergodic approach was proposed, which adds flexibility to the smoothing of the ergodic metric. However, such an approach, as it is an isotropic diffusion, propagates the error uniformly in all directions, regardless of changes in the desired distribution. We introduce a general class of anisotropic diffusion formulation of the ergodicity problem, which generates a potential field for the ergodic search. We demonstrate that this approach generalizes previous results, which consider radial basis functions and the solution of the heat equation to represent the difference between the goal density distribution and the covered trajectories. In our solution, the agent movement is directed using the gradient of the solution of the Perona-Malik diffusion, and our formulation includes the heat equation as a special case. We demonstrate the methodology with a series of simulations in different scenarios.
