Multi-featured collective perception with Evidence Theory: tackling spatial correlations

Abstract

Collective perception allows sparsely distributed agents to form a global view on a common spatially distributed problem without any direct access to global knowledge and only based on a combination of locally perceived information. However, the evidence gathered from the environment is often subject to spatial correlations and depends on the movements of the agents. The latter is not always easy to control and the main question is how to share and to combine the estimated information to achieve the most precise global estimate in the least possible time. The current article aims at answering this question with the help of evidence theory, also known as Dempster–Shafer theory, applied to the collective perception scenario as a collective decision-making problem. We study eight most common belief combination operators to address the arising conflict between different sources of evidence in a highly dynamic multi-agent setting, driven by modulation of positive feedback. In comparison with existing approaches, such as voter models, the presented framework operates on quantitative belief assignments of the agents based on the observation time of the options according to the agents’ opinions. The evaluated results on an extended benchmark set for multiple options ($$n>2$$ n > 2 ) indicate that the proportional conflict redistribution (PCR) principle allows a collective of small size ($$N=20$$ N = 20 ), occupying $$3.5\%$$ 3.5 % of the surface, to successfully resolve the conflict between clustered areas of features and reach a consensus with almost $$100\%$$ 100 % certainty up to $$n=5$$ n = 5 .

The Hidden Benefits of Limited Communication and Slow Sensing in Collective Monitoring of Dynamic Environments

“…While individual estimates are noisy, the swarm collectively filters noise and converges to an accurate collective decision [33]. Individual estimation errors can be caused, for example, by simple error-prone sensing devices (readings distant from the ground truth, e.g., [13,11]), spatial correlations (clustered information in localised areas rather than uniformly in the environment, e.g., [3,4,29]), and limited sensing range. Our simulations allow us to control sources and levels of sampling errors as well as to disentangle the impact of sampling errors from other system dynamics of interest (e.g., recruitment time).…”

“…We consider tiles with n = 2 colours: blue and yellow (see Fig. 1a) 4 . The difficulty of the perception problem κ ∈ [0, 1] is determined by the ratio between the concentration of tiles in the two colours: κ = q b /q y where q b and q y are (a)…”

“…Compared with previous work that investigated decentralised consensus decision making in the collective perception scenario, our algorithm has the fewest requirements, in line with our quest for minimalism. Different from previous work that required the storage of all avail-able alternatives and all received messages [23,24,4], here the robots only store the information about a single opinion (i.e., the colour considered predominant and its estimated concentration), the last received message from a neighbour, and a temporary variable to estimate possible environmental changes. Different from previous work requiring more advanced computation based on Bayesian inference [23,24,8,7] or fusion operators from epistemic logic [4], our robot behaviour is defined by a small finite state machine with reactive transitions.…”

“…All our experimental work is based on simulations using the ARGoS simulator extended with a simulator of the smart environment for the Kilobot robot called Kilogrid. 4 The current geopolitical situation motivated our choice of tile colour .…”

Robot Swarms Break Decision Deadlocks in Collective Perception Through Cross-Inhibition

“…We also plan to validate our results through real-robot experiments on the real Kilogrid. Moreover, most of the collective decision-making research in swarm robotics is concentrated on binary best-of-n problems, with only a few studies exploring n > 2 [25,13,4,30]. Therefore, as future work, we also aim to expand our analyses and experiments to n > 2 scenarios and investigate if the robustness of the cross-inhibition model extends to non-binary environments, as theory predicts [22].…”