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To accomplish complex swarm robotic missions in the real world, one needs to plan and execute a combination of single robot behaviors, group primitives such as task allocation, path planning, and formation control, and mission-specific…
This paper describes a technique for the autonomous mission planning of robotic swarms in high risk environments where agent disablement is likely. Given a swarm operating in a known area, a central command system generates measurements…
In this work, we investigate swarm self-clustering, where robots autonomously organize into spatially coherent groups using only local sensing and decision-making, without external commands, global positioning, or inter-robot communication.…
We introduce a framework for cooperative manipulation, applied on an underactuated manipulation problem. Two stationary robotic manipulators are required to cooperate in order to reposition an object within their shared work space. Control…
Decentralized multi-robot motion planning requires each robot to generate collision-free trajectories from local observations, without global sensing or reliable communication. However, most existing planners, whether classical or…
This study highlights the potential of image-based reinforcement learning methods for addressing swarm-related tasks. In multi-agent reinforcement learning, effective policy learning depends on how agents sense, interpret, and process…
Modern manufacturing systems require adaptive computing infrastructures that can respond to highly dynamic workloads and increasingly customized production demands. The compute continuum emerges as a promising solution, enabling flexible…
Modern cities are growing ecosystems that face new challenges due to the increasing population demands. One of the many problems they face nowadays is waste management, which has become a pressing issue requiring new solutions. Swarm…
Shared autonomy systems require principled methods for inferring user intent and determining appropriate assistance levels. This is a central challenge in human-robot interaction, where systems must be successful while being mindful of user…
Collective movement inspired by animal groups promises inherited benefits for robot swarms, such as enhanced sensing and efficiency. However, while animals move in groups using only their local senses, robots often obey central control or…
We describe a probabilistic framework for synthesizing control policies for general multi-robot systems, given environment and sensor models and a cost function. Decentralized, partially observable Markov decision processes (Dec-POMDPs) are…
Swarm robotics is a creative method of organizing multi-robot structures, consisting of many basic robots influenced by communal insects. The greatest astonishing attribute of swarm robots is their capacity to function together to…
This paper presents a consensus-based payload algorithm (CBPA) to deal with the condition of robots' capability decrease for multi-robot task allocation. During the execution of complex tasks, robots' capabilities could decrease with the…
Representation and control of the dynamics of stigmergic substances used by bio-inspired approaches is a challenge when applied to robotics. In order to overcome this challenge, this work proposes a model to coordinate swarms of robots…
Rapid performance recovery from unforeseen environmental perturbations remains a grand challenge in swarm robotics. To solve this challenge, we investigate a behaviour adaptation approach, where one searches an archive of controllers for…
We present GLAS: Global-to-Local Autonomy Synthesis, a provably-safe, automated distributed policy generation for multi-robot motion planning. Our approach combines the advantage of centralized planning of avoiding local minima with the…
Swarm systems constitute a challenging problem for reinforcement learning (RL) as the algorithm needs to learn decentralized control policies that can cope with limited local sensing and communication abilities of the agents. While it is…
Robot swarms navigating through unknown obstacle environments are an emerging research area that faces challenges. Performing tasks in such environments requires swarms to achieve autonomous localization, perception, decision-making,…
In this paper, we extended previous studies of cooperating autonomous robots to include situations when environmental changes and changes in the number of robots in the swarm can affect the efficiency to execute tasks assigned to the swarm…
Trajectory planning for multiple robots in shared environments is a challenging problem especially when there is limited communication available or no central entity. In this article, we present Real-time planning using Linear Spatial…