Towards the Implementation of a MPC-based Planner on an Autonomous All-Terrain Vehicle

Planning and control for a wheeled mobile robot are challenging problems when poorly traversable terrains, including dynamic obstacles, are considered. To accomplish a mission, the control system should firstly guarantee the vehicle integrity, for example with respect to possible roll-over/tip-over phenomena. A fundamental contribution to achieve this goal, however, comes from the planner as well. In fact, computing a path that takes into account the terrain traversability, the kinematic and dynamic vehicle constraints, and the presence of dynamic obstacles, is a first and crucial step towards ensuring the vehicle integrity. The present paper addresses some of the aforementioned issues, describing the hardware/software architecture of the planning and control system of an autonomous All-Terrain Mobile Robot and the implementation of a real-time path planner

Robust and Safe Autonomous Navigation for Systems with Learned SE(3) Hamiltonian Dynamics

Stability and safety are critical properties for successful deployment of automatic control systems. As a motivating example, consider autonomous mobile robot navigation in a complex environment. A control design that generalizes to different operational conditions requires a model of the system dynamics, robustness to modeling errors, and satisfaction of safety \NEWZL{constraints}, such as collision avoidance. This paper develops a neural ordinary differential equation network to learn the dynamics of a Hamiltonian system from trajectory data. The learned Hamiltonian model is used to synthesize an energy-shaping passivity-based controller and analyze its \emph{robustness} to uncertainty in the learned model and its \emph{safety} with respect to constraints imposed by the environment. Given a desired reference path for the system, we extend our design using a virtual reference governor to achieve tracking control. The governor state serves as a regulation point that moves along the reference path adaptively, balancing the system energy level, model uncertainty bounds, and distance to safety violation to guarantee robustness and safety. Our Hamiltonian dynamics learning and tracking control techniques are demonstrated on \Revised{simulated hexarotor and quadrotor robots} navigating in cluttered 3D environments

A framework for safe human-humanoid coexistence

This work is focused on the development of a safety framework for Human-Humanoid coexistence, with emphasis on humanoid locomotion. After a brief introduction to the fundamental concepts of humanoid locomotion, the two most common approaches for gait generation are presented, and are extended with the inclusion of a stability condition to guarantee the boundedness of the generated trajectories. Then the safety framework is presented, with the introduction of different safety behaviors. These behaviors are meant to enhance the overall level of safety during any robot operation. Proactive behaviors will enhance or adapt the current robot operations to reduce the risk of danger, while override behaviors will stop the current robot activity in order to take action against a particularly dangerous situation. A state machine is defined to control the transitions between the behaviors. The behaviors that are strictly related to locomotion are subsequently detailed, and an implementation is proposed and validated. A possible implementation of the remaining behaviors is proposed through the review of related works that can be found in literature

A Survey on Aerial Swarm Robotics

The use of aerial swarms to solve real-world problems has been increasing steadily, accompanied by falling prices and improving performance of communication, sensing, and processing hardware. The commoditization of hardware has reduced unit costs, thereby lowering the barriers to entry to the field of aerial swarm robotics. A key enabling technology for swarms is the family of algorithms that allow the individual members of the swarm to communicate and allocate tasks amongst themselves, plan their trajectories, and coordinate their flight in such a way that the overall objectives of the swarm are achieved efficiently. These algorithms, often organized in a hierarchical fashion, endow the swarm with autonomy at every level, and the role of a human operator can be reduced, in principle, to interactions at a higher level without direct intervention. This technology depends on the clever and innovative application of theoretical tools from control and estimation. This paper reviews the state of the art of these theoretical tools, specifically focusing on how they have been developed for, and applied to, aerial swarms. Aerial swarms differ from swarms of ground-based vehicles in two respects: they operate in a three-dimensional space and the dynamics of individual vehicles adds an extra layer of complexity. We review dynamic modeling and conditions for stability and controllability that are essential in order to achieve cooperative flight and distributed sensing. The main sections of this paper focus on major results covering trajectory generation, task allocation, adversarial control, distributed sensing, monitoring, and mapping. Wherever possible, we indicate how the physics and subsystem technologies of aerial robots are brought to bear on these individual areas

Performance Analysis of Constant Speed Local Abstacle Avoidance Controller Using a MPC Algorithym on Granular Terrain

A Model Predictive Control (MPC) LIDAR-based constant speed local obstacle avoidance algorithm has been implemented on rigid terrain and granular terrain in Chrono to examine the robustness of this control method. Provided LIDAR data as well as a target location, a vehicle can route itself around obstacles as it encounters them and arrive at an end goal via an optimal route. This research is one important step towards eventual implementation of autonomous vehicles capable of navigating on all terrains. Using Chrono, a multibody physics API, this controller has been tested on a complex multibody physics HMMWV model representing the plant in this study. A penalty-based DEM approach is used to model contacts on both rigid ground and granular terrain. Conclusions are drawn regarding the MPC algorithm performance based on its ability to navigate the Chrono HMMWV on rigid and granular terrain. A novel simulation framework has been developed to efficiently simulate granular terrain for this application. Two experiments were conducted to analyze the performance of the MPC LIDAR-based constant speed local obstacle avoidance controller. In the first, two separate controllers were developed, one using a 2-DOF analytical model to predict the HMMWV behavior, and the second using a higher fidelity 14-DOF vehicle model. In this first experiment, two controllers were compared as they controlled the HMMWV on two obstacle fields on rigid ground and granular terrain to understand the influence of model fidelity and terrain on controller performance. From these results, an improved lateral force model was developed for use in the 2-DOF vehicle model to better model the tire ground interaction using terramechanics relations. A second experiment was performed to compare two developed controllers. One used the 2-DOF vehicle model using the Pacejka Magic Formula to estimate tire forces while the second used a 2-DOF vehicle model with the newly developed force model to estimate lateral tire forces. As a result of this research, a smarter controller was developed that uses friction angle, cohesion, and interparticle friction coefficient to more accurately predict vehicle trajectories on granular terrain and allow a vehicle to navigate autonomously on granular terrain

Optimized state feedback regulation of 3DOF helicopter system via extremum seeking

In this paper, an optimized state feedback regulation of a 3 degree of freedom (DOF) helicopter is designed via extremum seeking (ES) technique. Multi-parameter ES is applied to optimize the tracking performance via tuning State Vector Feedback with Integration of the Control Error (SVFBICE). Discrete multivariable version of ES is developed to minimize a cost function that measures the performance of the controller. The cost function is a function of the error between the actual and desired axis positions. The controller parameters are updated online as the optimization takes place. This method significantly decreases the time in obtaining optimal controller parameters. Simulations were conducted for the online optimization under both fixed and varying operating conditions. The results demonstrate the usefulness of using ES for preserving the maximum attainable performance

ROBI’: A prototype mobile manipulator for agricultural applications

The design of ROBI', a prototype mobile manipulator for agricultural applications devised following low-cost, low-weight, simplicity, flexibility and modularity requirements, is presented in this work. The mechanical design and the selection of the main components of the motion control system, including sensors and in-wheel motors, is described. The kinematic and dynamic models of the robot are also derived, with the aim to support the design of a trajectory tracking system and to make a preliminary assessment of the design choices, as well. Finally, two simulations, one~specifically related to a realistic trajectory in an agricultural field, show the validity of these choices

Trust-Based Control of (Semi)Autonomous Mobile Robotic Systems

Despite great achievements made in (semi)autonomous robotic systems, human participa-tion is still an essential part, especially for decision-making about the autonomy allocation of robots in complex and uncertain environments. However, human decisions may not be optimal due to limited cognitive capacities and subjective human factors. In human-robot interaction (HRI), trust is a major factor that determines humans use of autonomy. Over/under trust may lead to dispro-portionate autonomy allocation, resulting in decreased task performance and/or increased human workload. In this work, we develop automated decision-making aids utilizing computational trust models to help human operators achieve a more effective and unbiased allocation. Our proposed decision aids resemble the way that humans make an autonomy allocation decision, however, are unbiased and aim to reduce human workload, improve the overall performance, and result in higher acceptance by a human. We consider two types of autonomy control schemes for (semi)autonomous mobile robotic systems. The first type is a two-level control scheme which includes switches between either manual or autonomous control modes. For this type, we propose automated decision aids via a computational trust and self-confidence model. We provide analytical tools to investigate the steady-state effects of the proposed autonomy allocation scheme on robot performance and human workload. We also develop an autonomous decision pattern correction algorithm using a nonlinear model predictive control to help the human gradually adapt to a better allocation pattern. The second type is a mixed-initiative bilateral teleoperation control scheme which requires mixing of autonomous and manual control. For this type, we utilize computational two-way trust models. Here, mixed-initiative is enabled by scaling the manual and autonomous control inputs with a function of computational human-to-robot trust. The haptic force feedback cue sent by the robot is dynamically scaled with a function of computational robot-to-human trust to reduce humans physical workload. Using the proposed control schemes, our human-in-the-loop tests show that the trust-based automated decision aids generally improve the overall robot performance and reduce the operator workload compared to a manual allocation scheme. The proposed decision aids are also generally preferred and trusted by the participants. Finally, the trust-based control schemes are extended to the single-operator-multi-robot applications. A theoretical control framework is developed for these applications and the stability and convergence issues under the switching scheme between different robots are addressed via passivity based measures