Probabilistic Gathering of Agents with Simple Sensors

Abstract

We present a novel probabilistic gathering algorithms for agents that can only detect the presence of other agents in front or behind them. The agents act in the plane and are identical and indistinguishable, oblivious and lack any means of direct communication. They do not have a common frame of reference in the plane and choose their orientation (direction of possible motion) at random. The analysis of the gathering process assumes that the agents act synchronously in selecting random orientations that remain fixed during each unit time-interval. Two algorithms are discussed. The first one assumes discrete jumps based on the sensing results given the randomly selected motion direction and in this case extensive experimental results exhibit probabilistic clustering into a circular region with radius equal to the step-size in time proportional to the number of agents. The second algorithm assumes agents with continuous sensing and motion, and in this case we can prove gathering into a very small circular region in finite expected time.

On the role and opportunities in teamwork design for advanced multi-robot search systems

“…Shared autonomy refers to the autonomous control of the majority of degrees of freedom in a system, while designing a control interface for human operators to control a reduced number of parameters defining the global behavior of the system. Furthermore the paper discusses the important role of communication by using robotic mobile ad hoc networks for the purpose of coordination and sharing of information between team members, highlighting that maintaining communication connectivity between the robots is essential in search and rescue missions, a topic that has been extensively studied in decentralized multi-agent control applications such as Barel et al. (2019) and Segall and Bruckstein (2022) .…”

AntAlate—A Multi-Agent Autonomy Framework

“…Our work during these years led us to develop several types of local interaction-based motion rules for autonomous mobile agents in swarms deployed in various types of environments that achieve global tasks such as patrolling an area, gathering into a cohesive but flexible "cloud" of agents, coverage of regions for intruder detection, equitable distribution of workload, and path planning. See for example, the works of Wagner and Bruckstein (1997), Yanovski et al (2003), Felner et al (2006, Gordon et al (2008), Osherovich et al (2008), Elor and Bruckstein (2014), Elazar and Bruckstein (2016), Bellaiche and Bruckstein (2017), Dovrat and Bruckstein (2017), Altshuler et al (2018), Manor and Bruckstein (2018), Amir and Bruckstein (2019), Barel et al (2021), andFrancos and. We also addressed the issue of achieving guidance and steering of cohesive mobile agent swarms using some global "broadcast control" ideas, as presented in works by Segall and Bruckstein (2016), Dovrat and Bruckstein (2017), and Barel et al (2018); where the broadcast signal is often assumed to be acquired by only a random set of the swarm's agents.…”

“…Yet the fundamental capabilities and limitations of swarms of such agents are rather difficult to analyze theoretically, so novel mathematical approaches are often needed to prove task accomplishment and termination, to evaluate the time span necessary to do the work, and to assess the effects of random or byzantine failures of agents. As examples of our team's efforts we refer the reader to papers by Bruckstein et al (1991), Bruckstein (1993), Bruckstein (2011), Oggier andBruckstein (2012), Elor and Bruckstein (2012b), Segall and Bruckstein (2016), Barel et al (2016), and Barel et al (2021).…”