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1. dSLAP: Distributed Safe Learning and Planning for Multi-robot Systems

   https://doi.org/10.1109/CDC51059.2022.9992938  

   Yuan, Zhenyuan; Zhu, Minghui (December 2022, IEEE 61st Conference on Decision and Control)

   This paper considers the problem where a group of mobile robots subject to unknown external disturbances aim to safely reach goal regions. We develop a distributed safe learning and planning algorithm that allows the robots to learn about the external unknown disturbances and safely navigate through the environment via their single trajectories. We use Gaussian process regression for online learning where variance is adopted to quantify the learning uncertainty. By leveraging set-valued analysis, the developed algorithm enables fast adaptation to newly learned models while avoiding collision against the learning uncertainty. Active learning is then applied to return a control policy such that the robots are able to actively explore the unknown disturbances and reach their goal regions in time. Sufficient conditions are established to guarantee the safety of the robots. A set of simulations are conducted for evaluation. 

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2. Decentralized Multi-Robot Information Gathering From Unknown Spatial Fields

   https://doi.org/10.1109/LRA.2023.3264720  

   Newaz, Abdullah Al; Alsayegh, Murtadha; Alam, Tauhidul; Bobadilla, Leonardo (May 2023, IEEE Robotics and Automation Letters)

   We present an incremental scalable motion planning algorithm for finding maximally informative trajectories for decentralized mobile robots. These robots are deployed to observe an unknown spatial field, where the informativeness of observations is specified as a density function. Existing works that are typically restricted to discrete domains and synchronous planning often scale poorly depending on the size of the problem. Our goal is to design a distributed control law in continuous domains and an asynchronous communication strategy to guide a team of cooperative robots to visit the most informative locations within a limited mission duration. Our proposed Asynchronous Information Gathering with Bayesian Optimization (AsyncIGBO) algorithm extends ideas from asynchronous Bayesian Optimization (BO) to efficiently sample from a density function. It then combines them with decentralized reactive motion planning techniques to achieve efficient multi-robot information gathering activities. We provide a theoretical justification for our algorithm by deriving an asymptotic no-regret analysis with respect to a known spatial field. Our proposed algorithm is extensively validated through simulation and real-world experiment results with multiple robots. 

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3. SEAR: A Polynomial-Time Multi-Robot Path Planning Algorithm with Expected Constant-Factor Optimality Guarantee

   Han, Shuai D.; Rodriguez, Edgar J.; Yu, Jingjin (January 2018, 2018 IEEE/RSJ International Conference on Intelligent Robots and Systems)

   We study the labeled multi-robot path planning problem in continuous 2D and 3D domains in the absence of obstacles where robots must not collide with each other. For an arbitrary number of robots in arbitrary initial and goal arrangements, we derive a polynomial time, complete algorithm that produces solutions with constant-factor optimality guarantees on both makespan and distance optimality, in expectation, under the assumption that the robot labels are uniformly randomly distributed. Our algorithm only requires a small constant-factor expansion of the initial and goal configuration footprints for solving the problem, i.e., the problem can be solved in a fairly small bounded region. Beside theoretical guarantees, we present a thorough computational evaluation of the proposed solution. In addition to the baseline implementation, adapting an effective (but non-polynomial time) routing subroutine, we also provide a highly efficient implementation that quickly computes near-optimal solutions. Hardware experiments on the microMVP platform composed of non-holonomic robots confirms the practical applicability of our algorithmic pipeline. 

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4. A Visibility-Based Escort Problem

   https://doi.org/10.1109/IROS55552.2023.10341574  

   Fletcher, Lance G; Perali, Priyankari; Beathard, Andrew; O'Kane, Jason M (October 2023, IEEE)

   This paper introduces and solves a visibility-based escort planning problem. This novel problem, which is closely related to the well-researched family of visibility-based pursuit-evasion problems in robotics, entails an escort agent tasked with escorting a vulnerable agent, called the VIP, in a 2-dimensional environment. The escort protects the VIP from adversaries that pose line-of-sight threats. We describe a correct and complete planning algorithm whose inputs are a simply-connected polygonal map of the environment, starting locations for the escort and the VIP, along with a goal location to which the VIP agent should be safely moved. The algorithm computes trajectories for the escort and VIP which allow the VIP to reach its goal without coming into the line-of-sight of the adversary at any time. During the execution of these trajectories, the adversary is allowed to move along any continuous path that does not enter into the line-of-sight of the escort. The algorithm proceeds by dividing the environment into a collection of conservative regions and planning the escort's movements as a sequence of these regions via breadth-first search over an information graph. The trajectory of the VIP can then be constructed by tracing the 'safe zones' swept out by the escort's trajectory. We describe an implementation of this algorithm and present computed examples of escort agent strategies in diverse environments. 

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5. Adaptive Multiple Distributed Bidirectional Spiral Path Planning for Foraging Robot Swarms

   https://doi.org/10.1109/CRV60082.2023.00036  

   Lu, Qi; Luna, Ryan (June 2023, IEEE)

   The Distributed Deterministic Spiral Algorithm (DDSA) has shown great foraging efficiency in robot swarms. However, when the number of robots in the swarm increases, scalability becomes a significant bottleneck due to increased collisions among robots, making it challenging to deploy them in the search space (e.g., 20 robots). To address this issue, we propose an adaptive Multiple-Distributed Bidirectional Spiral Algorithm (MDBSA) that enhances scalability. Our proposed algorithm partitions the squared search arena into multiple identical squared regions and assigns robots to regions dynamically based on the number of regions. In each region, a bidirectional spiral search path is planned, and when a robot completes its search, it is assigned to either an unassigned region or a region with one robot. The two robots will then travel along the path from the starting and ending points of the spiral path. We evaluated the performance of robot swarms using the MDBSA algorithm in the ARGoS robot simulator. Our experimental results show that the proposed MDBSA algorithm outperforms DDSA. When robots deliver collected resources to regions instead of the center, it reduces collisions and significantly improves the scalability of the robot swarm. Our findings suggest that a multiple-distributed search strategy is an efficient solution for foraging robot swarms. 

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