Addressing the problem of enclosing and tracking a target requires multiple agents with adequate motion strategies. We consider a team of unicycle robots with a standard camera on board. The robots must maintain the desired enclosing formation while dealing with their nonholonomic motion constraints. The reference formation trajectories must also guarantee permanent visibility of the target by overcoming the limited field of view of the cameras. In this article, we present a novel approach to characterize the conditions on the robots’ trajectories taking into account the motion and visual constraints. We also propose online and offline motion planning strategies to address the constraints involved in the task of enclosing and tracking the target. These strategies are based on maintaining the formation shape with variable size or, alternatively, on maintaining the size of the formation with flexible shape.

Shape servoing is the problem of controlling the shape of a deformable object via robotic manipulation, exploiting sensory feedback. In many shape servoing scenarios of practical interest, the object has a natural tendency to preserve local rigidity. Motivated by this observation, we propose to use the As-Rigid-As-Possible deformation model for shape servoing. Specifically, we propose a novel control scheme based on a deformation Jacobian computed from this model. The proposed scheme drives the object to a desired 3D shape using as feedback the object’s measured current 3D shape. The scheme is simple to implement, and it avoids some typical requirements in existing work: specifically, (i) we do not need to know the object’s mechanical deformation parameters, and (ii) we do not use a Jacobian computed from data collected over a time window while the robots move. We test the proposed scheme in bi-arm shape servoing experiments with a variety of deformable objects of different material (paper, rubber, plastic). Tracking of the deformable object’s surface in 3D is performed in our experiments via an algorithm based on monocular vision. The experimental results validate the practicality of our scheme.

In this article, we propose a general unified tracking-servoing approach for controlling the shape of elastic deformable objects using robotic arms. Our approach works by forming a lattice around the object, binding the object to the lattice, and tracking and servoing the lattice instead of the object. This makes our approach have full control over the deformation of elastic deformable objects of any general form (linear, thin-shell, and volumetric) in 3-D space. Furthermore, it decouples the runtime complexity of the approach from the objects' geometric complexity. Our approach is based on the as-rigid-as-possible deformation model. It requires no mechanical parameter of the object to be known and can drive the object toward desired shapes through large deformations. The inputs to our approach are the point cloud of the object's surface in its rest shape and the point cloud captured by a 3-D camera in each frame. Overall, our approach is more broadly applicable than existing approaches. We validate the efficiency of our approach through numerous experiments with elastic deformable objects of various shapes and materials (paper, rubber, plastic, and foam).

Robotic manipulation of deformable linear objects (DLOs) has great potential for applications in diverse fields such as agriculture or industry. However, a major challenge lies in acquiring accurate deformation models that describe the relationship between robot motion and DLO deformations. Such models are difficult to calculate analytically and vary among DLOs. Consequently, manipulating DLOs poses significant challenges, particularly in achieving large deformations that require highly accurate global models. To address these challenges, this letter presents MultiAC6: a new multi Actor-Critic framework for robot action space decomposition to control large 3D deformations of DLOs. In our approach, two deep reinforcement learning (DRL) agents orient and position a robot gripper to deform a DLO into the desired shape. Unlike previous DRL-based studies, MultiAC6 is able to solve the sim-to-real gap, achieving large 3D deformations up to 40 cm in real-world settings. Experimental results also show that MultiAC6 has a 66% higher success rate than a single-agent approach. Further experimental studies demonstrate that MultiAC6 generalizes well, without retraining, to DLOs with different lengths or materials.

An emerging application of multirobot systems is the monitoring of a dynamic event. Here, the goal is to enclose and track a moving target by attaining a desired geometric formation around it. By considering a circular pattern configuration for the target enclosing, the multirobot system is able to perform full perception of the target along its motion. In the proposed system, the robots rely only on their onboard vision sensor without external input to complete the task. The key problem resides in overcoming the motion and visual constraints of the agents. In particular, differential-drive robots with limited sensing, that must maintain visibility of the moving target as it navigates in the environment, are considered. A novel approach to characterize the motion of the robots in the formation that allows to enclose and track the target while overcoming their limited field of view (FOV) is presented. The proposed approach is illustrated through simulations.