Reactive Exploration of Unknown Environments for Redundant Robots using Virtual Model Control

Alessio Canzolino; Omar Faris; Alessandro De Blasi; Fulvio Forni

cs.ROOct 6, 2026

Reactive Exploration of Unknown Environments for Redundant Robots using Virtual Model Control

Authors: Alessio Canzolino, Omar Faris, Alessandro De Blasi, Fulvio Forni

Organizations: Department of Electrical, Electronic, and Information Engineering “Guglielmo Marconi”, University of Bologna, Italy · Department of Engineering, University of Cambridge, UK.

Abstract

The exploration of confined, occluded, and partially known spaces poses significant challenges in robotic manipulation. The overall pose of the robotic arm must be carefully controlled to respect tight geometric constraints while avoiding newly discovered obstacles. We address this problem by proposing an active exploration approach for redundant robotic arms with an eye-in-hand camera configuration. Our approach navigates and acquires information in real-time based on a novel scoring method that directly selects a target voxel from the unexplored space using the robot's current state and expected information gain. To move the robot safely toward the target voxel, we utilize Virtual Model Control, which guarantees compliance and enables whole-body reactive obstacle avoidance without the need for path replanning. Simulated and real-robot experiments in both confined and open environments demonstrate the effectiveness of our approach, achieving over $90\%$ mapping coverage across all tested environments in under $120$ seconds without colliding with obstacles.

Figures & tables

Fig. 1 : The shelving unit for testing autonomous exploration in partially known environments.

Panel of Figure 2 — Fig. 2 : 2D visualization of the three scores used to select the next target voxel. $R(v)$ counts unknown voxels in the neighbourhood of $v$ , $F(v)$ rewards voxels closer to the robot base, and $D(v)$ rewards voxels closer to the camera position.

Panel of Figure 3 — Fig. 3 : Camera nose mechanism: (a) configuration when a new target is selected (b) $\textbf{F}_{ns}$ pulls the nose in the direction of the target, orienting the camera accordingly.

Fig. 4 : The green spring and damper pull the camera nose (orange arrow) towards the target (yellow box). Repulsive springs from obstacles (red) and base plane (blue) regulate the robot posture, enabling whole-body obstacle avoidance.

Panel of Figure 5 — Fig. 5 : (a) Real vs mapped shelving unit from one experimental run. Red voxels have occupied status ( $V_{occ}$ ) and gray voxels are unexplored ( $V_{unk}$ ). (b) Exploration curve $R(t)$ for all experimental runs. (c) Curves of the normalized discovery rate $\gamma_{d}(t)$ accumulated over a rolling time window of $5$ s for all experimental runs.

Parameter	Value	Parameter	Value
$w_{R},w_{F},w_{D}$	$2,1,4$	$l_{n}$	$25$ cm
$V_{min},V_{max}$	$0,350$	$k_{ns}$	$300$ N/m
$V_{free},V_{unk},V_{occ}$	$20,55,90$	$k_{r,i}$	$330$ N/m
$\lambda_{vel},\lambda_{torq}$	$0.15$ m/s, $0.02$ Nm	$\bar{F}_{ns}$	$18$ N
$r_{ign}$	$5$	$c$	$8.5$ Ns/m
$N_{R(v)}$	$3$	$\sigma_{r,i}$	$5$ cm

TABLE I : Parameters used in experiments and simulations.

Metric	Mean $\pm$ SD	Metric	Mean $\pm$ SD
$R_{\infty}$ (%)	$92.5\pm 1.3$	$T_{e}$ (s)	$91.4\pm 7.3$
$L$ (m)	$13.4\pm 1.0$	$N_{\mathrm{ts}}$	$47.4\pm 4.8$
$p_{\mathrm{stag}}$ (%)	$40.9\pm 6.1$	$N_{ts:TR}$	$28.8\pm 3.4$
$N_{\mathrm{dl}}$	$29.8\pm 2.1$	$N_{ts:\gamma}\,,\,N_{ts:\lambda}$	$12.0\pm 1.5$ , $6.6\pm 4.6$

TABLE II : Experimental results for the exploration of three-tier shelving unit environment.

Panel of Figure 8 — Fig. 6 : Simulation tests with increased levels of clutter. Scenarios A-C shows increased number of smaller obstacles. Scenario D highlights challenging occlusions, as shown in the rear view.

Panel of Figure 9 — Fig. 7 : Simulation results for the Scenarios A, B, C, and D, with $R(t)$ shown in (a), the remaining metrics shown in (b), and the rear view of a mapped Scenario D environment shown in (c).

Panel of Figure 10 — Fig. 8 : Real and mapped examples from the strawberry clusters experiment showing a front view at the top and a side view at the bottom. As the voxel resolution decreases from (b) $4$ cm to (c) $3$ cm to (d) $2$ cm, the robot becomes capable of extracting finer details of the map at the expense of slower exploration.

Panel of Figure 11 — Fig. 9 : Real vs mapped open-top boxes environment from one experimental run.

Fig. 10 : Number of sampled next-best-views required to reach more than $95\%$ of workspace exploration for longer sensor range in Scenarios A, B, C, and D and the shelving unit environments.

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