Marco Cognetti

3-D mutual localization with anonymous bearing measurements

We present a decentralized algorithm for estimating mutual 3-D poses in a group of mobile robots, such as a team of UAVs. Our algorithm uses bearing measurements reconstructed, e.g., by a visual sensor, and inertial measurements coming from the robot IMU. Since identification of a specific robot in a group would require visual tagging and may be cumbersome in practice, we simply assume that the bearing measurements are anonymous. The proposed localization method is a non-trivial extension of our previous algorithm for the 2-D case [1], and exhibits similar performance and robustness. An experimental validation of the algorithm has been performed using quadrotor UAVs.

Whole-body motion planning for humanoids based on CoM movement primitives

This work addresses the problem of whole-body motion planning for a humanoid robot that must execute a certain task in an environment containing obstacles. A randomized planner is proposed that builds a solution by concatenating whole-body motions. Each whole-body motion is generated so as to realize a center of mass (CoM) movement selected from a set of primitives and simultaneously accomplish a portion of the task. The CoM primitives are representative of typical humanoid actions such as walking gaits (static and dynamic), and can in principle include more sophisticated movements (e.g., jumping, crouching, etc). Implementation on the NAO humanoid proves that the proposed method generates sensible plans for a variety of composite tasks requiring a combination of navigation and manipulation.

Task-Oriented Whole-Body Planning for Humanoids based on Hybrid Motion Generation

This paper considers the problem of planning the motion of a humanoid robot that must execute a manipulation task, possibly requiring stepping, in environments cluttered by obstacles. The proposed method explores the submanifold of the configuration space that is admissible with respect to the assigned task and at the same time satisfies other constraints, including humanoid equilibrium. The exploration tree is expanded using a hybrid scheme that simultaneously generates footsteps and whole-body motions. The algorithm has been implemented for the humanoid robot NAO and validated through planning experiments and dynamic playback in V-REP.

Relative localization and identification in a heterogeneous multi-robot system

We develop a localization method for a single-UAV/multi-UGV heterogeneous system of robots. Considering the natural supervisory role of the UAV and the challenging (but realistic) assumption that the UAV-to-UGV measurements do not include the identities of the UGVs, we have adopted the PHD filter as a multi-target tracking technique. However, the standard version of this filter does not take into account odometric information coming from the targets, nor does it solve the problem of estimating their identities. Hence, we design ID-PHD, a modification of the PHD filter that is able to reconstruct the identities of the targets by incorporating odometric data. The proposed localization method has been successfully validated through experiments. Some preliminary results of a localization-based control scheme for the multi-robot system are also presented.

Real-time planning and execution of evasive motions for a humanoid robot

We present a method for performing evasive motions with a humanoid robot. In the considered scenario, the robot is standing in a workspace, when a moving obstacle (e.g., a human, or another robot) enters its safety area and heads towards it; the humanoid must plan and execute in real-time a maneuver that avoids the collision. The proposed method goes through several conceptual steps. Once the entrance of the moving obstacle in the safety area is detected, its approach direction relative to the robot is determined. On the basis of this information, a suitable evasion maneuver represented by footsteps is generated. From these, an appropriate trajectory is computed for the Center of Mass of the humanoid. Finally, joint motion commands are generated so as to track such trajectory. All computations make use of closed-form expressions and are therefore suitable for real-time implementation. The proposed approach is validated via simulations and experiments on a NAO humanoid. The possibility of adapting the basic method so as to be used in a replanning framework is also investigated.

Rearrangement planning using object-centric and robot-centric action spaces

This paper addresses the problem of rearrangement planning, i.e. to find a feasible trajectory for a robot that must interact with multiple objects in order to achieve a goal. We propose a planner to solve the rearrangement planning problem by considering two different types of actions: robot-centric and object-centric. Object-centric actions guide the planner to perform specific actions on specific objects. Robot-centric actions move the robot without object relevant intent, easily allowing simultaneous object contact and whole arm interaction. We formulate a hybrid planner that uses both action types. We evaluate the planner on tasks for a mobile robot and a household manipulator.

Cooperative control of a heterogeneous multi-robot system based on relative localization

We propose a cooperative control scheme for a heterogeneous multi-robot system, consisting of an Unmanned Aerial Vehicle (UAV) equipped with a camera and multiple identical Unmanned Ground Vehicles (UGVs). Our control scheme takes advantage of the different capabilities of the robots. Since the system is highly redundant, the execution of multiple different tasks is possible. The primary task is aimed at keeping the UGVs well inside the camera field of view, so as to allow our localization system to reconstruct the identity and relative pose of each UGV with respect to the UAV. Additional tasks include formation control, navigation and obstacle avoidance. We thoroughly discuss the feasibility of each task, proving convergence when possible. Simulation results are presented to validate the proposed method.

Whole-body planning for humanoids along deformable tasks

This paper addresses the problem of generating whole-body motions for a humanoid robot that must execute a certain task in an environment containing obstacles. The assigned task trajectory is deformable, and the planner may exploit this feature for finding a solution. Our framework consists of two main components: a constrained motion planner and a deformation mechanism. The basic idea is that the constrained motion planner attempts to solve the problem for the original task. If this proves to be too difficult, the deformation mechanism modifies the task using appropriate heuristic functions. Then, the constrained motion planner is invoked again on the deformed task. If needed, this procedure is iterated. The proposed algorithm has been successfully implemented for the NAO humanoid in V-REP.

Intrinsically stable MPC for humanoid gait generation

We present a novel MPC method for humanoid gait generation that is guaranteed to produce stable CoM trajectories. This is obtained by using a dynamic extension of the LIP as motion model, with the ZMP velocity as a control variable, and embedding an explicit stability constraint in the formulation. Such constraint turns out to be linear in the control variables, leading to a standard QP problem with equality and inequality constraints. The intrinsically stable MPC framework is developed into a full-fledged gait generation scheme by including automatic footstep placement. Simulations show that the proposed method is very effective and performs robustly in the presence of changes in the prediction horizon.

Ground and Aerial Mutual Localization Using Anonymous Relative-Bearing Measurements

We present a decentralized algorithm for estimating mutual poses (relative positions and orientations) in a group of mobile robots. The algorithm uses relative-bearing measurements, which, for example, can be obtained from onboard cameras, and information about the motion of the robots, such as inertial measurements. It is assumed that all relative-bearing measurements are anonymous; i.e., each specifies a direction along which another robot is located but not its identity. This situation, which is often ignored in the literature, frequently arises in practice and remarkably increases the complexity of the problem. The proposed solution is based on a two-step approach: in the first step, the most likely unscaled relative configurations with identities are computed from anonymous measurements by using geometric arguments, while in the second step, the scale is determined by numeric Bayesian filtering based on the motion model. The solution is first developed for ground robots in SE (2) and then for aerial robots in SE (3). Experiments using Khepera III ground mobile robots and quadrotor aerial robots confirm that the proposed method is effective and robust w.r.t. false positives and negatives of the relative-bearing measuring process.