Lloyd-based Approach for Robots Navigation in Human-shared environments

Abstract

We present a Lloyd-based navigation solution for robots that are required to move in a dynamic environment, where static obstacles (e.g, furnitures, parked cars) and unpredicted moving obstacles (e.g., humans, other robots) have to be detected and avoided on the fly. The algorithm can be computed in real-time and falls in the category of the reactive methods. The simplicity, the small amount of information required for the control inputs synthesis, and the low number of parameters to be tuned are the highlights of this method. Moreover, the multiagent flocking is considered in the same framework and opens to several interesting applications. particular, we show how a team of robots can flocks The goodness of the method is proved through extensive simulations and, for the single agent navigation in human-shared environment, also with experiments on a unicycle-like robot.

Time-Inverted Kuramoto Model Meets Lissajous Curves: Multi-Robot Persistent Monitoring and Target Detection

“…In accordance with the simulation results, we claim that the deadlocks in cases a., b., and c. cannot occur by applying the rules ( 8) and (9). Notice that, the introduction of these rules is the main reason why RBL over perform LB [53] in success rate. Let us analyze the three scenarios: Case a (Fig.…”

“…Firstly, we synthesize what we called the rule-based Lloyd algorithm (RBL), a solution that can guarantee safety and convergence towards the goal region. With respect to our previous work [53], here we focused on the interaction between robots and: 1) We drastically improve the multi-robot coordination performance, that is, we increased the success rate from 0.00 to 1.00 in scenarios with more that 20 robots; 2) We provide, not only safety, but also convergence guarantees; and 3) We extend the algorithm to account for non-holonomic constraints and control inputs saturation by leveraging on a model predictive controller (MPC). Secondly, we show how the basic Lloyd-based algorithm (without rules) can be used effectively as a safety layer in learning-based methods.…”

“…Our claim is that, by modifying the geometry of the cell V i and by properly designing a weighting function φ i (q), we can obtain an efficient and effective algorithm for multirobot motion planning and control. Notice that, while the cell geometry to provide safety was introduced by the basic LB algorithm [53], [57]- [60], one of the novelty introduced in this work, besides the use of the algorithm as a safety layer for learning-based algorithm and the introduction of an MPC formulation to deal with dynamical constraint, is the addition of rules in the shaping of the weighting function φ i (q), which are crucial in order to increase the performance and provide convergence guarantees to the goal positions.…”

“…In the following, we provide a comparison with some state of the art algorithms. Firstly, we considered the non-holonomic (unicycle) case, we compared the performance of our rule-based Lloyd algorithm (RBL) with LB [53] and RL-RVO [41], which demonstrates superior performance and success rate compared to other algorithms such as SARL [48], GA3C-CADRL [49], NH-ORCA [6]. We considered the crossing circle scenario R c = 4 (m) with N = [6,20,30], δ i = 0.2 (m), a maximum forward velocity v i,max = 1.5 (m/s) and limits on the accelerations of a i,max = 1 (m/s 2 ).…”

A unified Lloyd-based framework for multi-agent collective behaviours

“…robots with memory of past positions and exchanging information. Unlike the frontier-based methods [14], our solution does not require the support of planning algorithms to reach the frontiers; (3) A novel motion control algorithm for connectivity maintenance; (4) A group navigation technique that switches between a flocking behaviour, i.e., the robots maintain a loose formation [48,49], and a constrained formation behaviour, where each robot has to keep a fixed distance from its neighbours; (5) A rendezvous algorithm that operates in generic non-convex environments (e.g., a room cluttered with obstacles).…”