Woosuk Kang

In addition to the various requirements that a multi-robot framework should meet, swarm robotics applications also demand robustness, flexibility, and scalability. While several frameworks have been developed for multi-robot operation, they mostly fall short of adequately supporting some of these essential requirements. In this work, we introduce a novel multi-robot programming framework called HiSARM (High-level Specification, Automatic code generation, and Retargetable deployment for Multi-robot systems), designed to assist both mission planners and robot software developers. HiSARM employs a high-level language to enable mission planners to specify collaborative tasks among multiple robots intuitively. From these scripts written in a high-level language, executable robot code is automatically generated. Mission planners can easily customize the executable robot code according to their specific needs within HiSARM. Additionally, HiSARM provides multiple verification environments by introducing a formal intermediate representation and enabling retargetable deployment of the binary file across various domains, including simulation and real robots. For robot software developers, HiSARM eases the development process for robot software developers by categorizing software components and auto-generating swarm-related functions. We tested HiSARM with multiple scenarios in simulation and real robot environments, demonstrating that it effectively supports all the necessary features for multi-robot applications, including swarm operations.

While task allocation of swarm robots has been extensively researched, resource constraints of robots are rarely considered. In this work, we propose two novel task allocation methods robust to robot failures while considering the resource constraint, limited communication range, and deadline constraint of tasks. The first method, STA (static task allocation) method, finds an optimal task allocation solution at compile-time in terms of the minimum expected finish time, using answer set programming. On the other hand, the DTA (dynamic task allocation) method determines the task candidates for each robot at compile-time considering the resource constraint. It lets each robot select a task autonomously at run-time iteratively by exchanging the task allocation information with its neighbor robots. We assess the efficacy of our methods across three distinct environments: a numerical simulation, a swarm robotics simulation, and real robots. Experimental results show that the proposed methods can effectively tolerate robot failures, and the DTA method is superior to the STA method as the probability of robot failure increases. However, the STA method also exhibits consistent performance and superiority when faced with limitations in inter-robot communication. Additionally, we validate the feasibility of our method in a real-world context by conducting experiments with actual robots. Note to Practitioners —The motivation of this work is to explore how to allocate tasks efficiently to swarm robots to ensure timely completion despite occasional robot failures. In search-and-rescue scenarios, such as in the aftermath of a disaster, the effective use of swarm robots is vital, and the time taken to search is crucial to rescuing individuals within a critical time. Various approaches have been proposed to tackle this problem, taking into account time constraints. However, few studies have considered the impact of hardware constraints on robots. To address this issue, this paper proposes two new strategies to find an optimal allocation: the Static Task Allocation (STA) method and the Dynamic Task Allocation (DTA) method. Our methods are evaluated both on real robots and in simulation environments, demonstrating their suitability for practical application.

Programming for swarm robots is challenging due to platform diversity and the gap between individual and swarm behaviors. To tackle this challenge, we propose a component-based software synthesis method from a high-level specification. To support heterogeneous robots and maximize code reuse, we adopt a component-based approach that classifies software components into three categories: robot, algorithm, and consensus. We generate a task graph model for an individual robot from a high-level specification and use a software synthesizer to generate the target code from the task graph model. Through a proof-of-concept implementation with a group searching application, the viability of the proposed technique is demonstrated.

Software designed to run on memory-limited embedded platforms must not violate peak memory requirements. This article proposes a memory usage estimation technique in a model-based software development methodology to meet these goals for code that is synthesized from a dataflow-model-based specification. —Sudeep Pasricha, Colorado State University, USA