Maximum Likelihood Uncertainty Estimation: Robustness to Outliers

We benchmark the robustness of maximum likelihood based uncertainty estimation methods to outliers in training data for regression tasks. Outliers or noisy labels in training data results in degraded performances as well as incorrect estimation of uncertainty. We propose the use of a heavy-tailed distribution (Laplace distribution) to improve the robustness to outliers. This property is evaluated using standard regression benchmarks and on a high-dimensional regression task of monocular depth estimation, both containing outliers. In particular, heavy-tailed distribution based maximum likelihood provides better uncertainty estimates, better separation in uncertainty for out-of-distribution data, as well as better detection of adversarial attacks in the presence of outliers

Implementation and validation of an event-based real-time nonlinear model predictive control framework with ROS interface for single and multi-robot systems.

This paper presents the implementation and experimental validation of a central control framework. The presented framework addresses the need for a controller, which provides high performance combined with a low-computational load while being on-line adaptable to changes in the control scenario. Examples for such scenarios are cooperative control, task-based control and fault-tolerant control, where the system's topology, dynamics, objectives and constraints are changing. The framework combines a fast Nonlinear Model Predictive Control (NMPC), a communication interface with the Robot Operating System (ROS) as well as a modularization that allows an event-based change of the NMPC scenario. To experimentally validate performance and event-based adaptability of the framework, this paper is using a cooperative control scenario of Unmanned Aerial Vehicles (UAVs)

Model predictive control for spacecraft rendezvous.

The current paper addresses the problem of Spacecraft Rendezvous using Model Predictive Control (MPC). The Clohessy-Wiltshire-Hill equations are used to model the spacecraft relative motion. Here the rendezvous problem is discussed by trajectory control using MPC method. Two different scenarios are addressed in trajectory control. The first scenario consist of position control with fuel constraint, secondly the position control is performed in the presence of obstacles. Here the problem of fuel consumption and obstacle avoidance is addressed directly in the cost function. The proposed methods are successfully analysed through simulations

Operational space control of a lightweight robotic arm actuated by shape memory alloy wires: a comparative study.

This article presents the design and control of a two-link lightweight robotic arm using shape memory alloy wires as actuators. Both a single-wire actuated system and an antagonistic configuration system are tested in open and closed loops. The mathematical model of the shape memory alloy wire, as well as the kinematics and dynamics of the robotic arm, are presented. The operational space control of the robotic arm is performed using a joint space control in the inner loop and closed-loop inverse kinematics in the outer loop. In order to choose the best joint space control approach, a comparative study of four different control approaches (proportional derivative, sliding mode, adaptive, and adaptive sliding mode control) is carried out for the proposed model. From this comparative analysis, the adaptive controller was chosen to perform operational space control. This control helps us to perform accurate positioning of the end-effector of shape memory alloy wire–based robotic arm. The complete operational space control was successfully tested through simulation studies performing position reference tracking in the end-effector space. Through simulation studies, the proposed control solution is successfully verified to control the hysteretic robotic arm

Collision avoidance effects on the mobility of a UAV swarm using chaotic ant colony with model predictive control.

The recent development of compact and economic small Unmanned Aerial Vehicles (UAVs) permits the development of new UAV swarm applications. In order to enhance the area coverage of such UAV swarms, a novel mobility model has been presented in previous work, combining an Ant Colony algorithm with chaotic dynamics (CACOC). This work is extending CACOC by a Collision Avoidance (CA) mechanism and testing its efficiency in terms of area coverage by the UAV swarm. For this purpose, CACOC is used to compute UAV target waypoints which are tracked by model predictively controlled UAVs. The UAVs are represented by realistic motion models within the virtual robot experimentation platform (V-Rep). This environment is used to evaluate the performance of the proposed CACOC with CA algorithm in an area exploration scenario with 3 UAVs. Finally, its performance is analyzed using metrics

Trajectory optimization and control of multipod robots in on-orbit servicing operations

This paper presents a trajectory optimization method applied to multipod robots performing extravehicular activities. The presented approach automatically determines the leg motion required to achieve a desired location on the exterior of the target spacecraft. A 3D camera is located at the robot body, and a 3D map of the target spacecraft is generated from the point cloud. A trajectory optimization is obtained given the system's initial and desired state, the manoeuvre's total duration, and the number of steps for each leg. From this information, the trajectory optimization approach generates the legs trajectories and contact forces required to guide the multipod robot. Numerical simulations assess the applicability of the proposed strategy in typical operations that can potentially be performed in an extravehicular activity scenario

Towards an autonomous vision-based unmanned aerial system against wildlife poachers.

Poaching is an illegal activity that remains out of control in many countries. Based on the 2014 report of the United Nations and Interpol, the illegal trade of global wildlife and natural resources amounts to nearly $ 213 billion every year, which is even helping to fund armed conflicts. Poaching activities around the world are further pushing many animal species on the brink of extinction. Unfortunately, the traditional methods to fight against poachers are not enough, hence the new demands for more efficient approaches. In this context, the use of new technologies on sensors and algorithms, as well as aerial platforms is crucial to face the high increase of poaching activities in the last few years. Our work is focused on the use of vision sensors on UAVs for the detection and tracking of animals and poachers, as well as the use of such sensors to control quadrotors during autonomous vehicle following and autonomous landing

Mobility Strategy of Multi-Limbed Climbing Robots for Asteroid Exploration

Mobility on asteroids by multi-limbed climbing robots is expected to achieve our exploration goals in such challenging environments. We propose a mobility strategy to improve the locomotion safety of climbing robots in such harsh environments that picture extremely low gravity and highly uneven terrain. Our method plans the gait by decoupling the base and limbs' movements and adjusting the main body pose to avoid ground collisions. The proposed approach includes a motion planning that reduces the reactions generated by the robot's movement by optimizing the swinging trajectory and distributing the momentum. Lower motion reactions decrease the pulling forces on the grippers, avoiding the slippage and flotation of the robot. Dynamic simulations and experiments demonstrate that the proposed method could improve the robot's mobility on the surface of asteroids.Comment: Submitted version of paper accepted for presentation at the CLAWAR 2023 (26th International Conference on Climbing and Walking Robots and the Support Technologies for Mobile Machines