Sub-Optimal Control of Autonomous Wheel Loader With Approximate Dynamic Programming

Optimal control of wheel loaders in short loading cycles is studied in this paper. For modeling the wheel loader, the data from a validated diesel engine model is used to find a control oriented mean value engine model. The driveline is modeled as a switched system with three constant gear ratios (modes) of −60 for backwarding, 60 for forwarding, and zero for stopping. With these three modes, the sequence of active modes in a short loading cycle is fixed as backwarding, stopping, forwarding, and stopping. For the control part, it is assumed that the optimal path is known a priori. Given the mode sequence, the control objective is finding the optimal switching time instants between the modes while the wheel loader tracks the optimal path. To solve the optimal control problem, approximate dynamic programming is used. Simulation results are provided to show the effectiveness of the solution.

Hierarchical Nonlinear Moving Horizon Estimation of Vehicle Lateral Speed and Road Friction Coefficient

Publisher’s Note: This paper was selected for publication in ASME Letters in Dynamic Systems and Control. https://www.asmedigitalcollection.asme.org/lettersdynsys/article/doi/10.1115/1.4046395/1074688/Hierarchical-Nonlinear-Moving-Horizon-Estimation

The Restricted Newton Method for Fast Nonlinear Model Predictive Control

We introduce the Restricted Newton’s Method (RNM), a basic optimization method, to accelerate model predictive control turnaround times. RNM is a hybrid of Newton’s method (NM) and gradient descent (GD) that can be used as a building block in nonlinear programming. The two parameters of RNM are the subspace on which we restrict the Newton steps and the maximal size of the GD step. We present a convergence analysis of RNM and demonstrate how these parameters can be selected for MPC applications using simple machine learning methods. This leads to two parameter selection strategies with different convergence behaviour. Lastly, we demonstrate the utility of RNM on a sample autonomous vehicle problem with promising results.

Completing Complex Contact Tasks Using Integrated Active and Passive Compliant Control Methodologies

In this paper we introduce the Generalized Contact Control Framework (GCCF) implemented on a compliant robotic manipulator. We demonstrate that the combined joint compliance and GCCF-based compliance control enable the completion of complex contact tasks in uncertain environments, where complex refers to the need to meet different contact force requirements involving multiple steps and output axes. Operating in uncertain environments means limited knowledge of the location or material properties of contact objects. The demonstrated tasks include opening a pill bottle and rigidly connecting to a purely mechanical tool changer. The GCCF simplifies the definition and modification of contact control parameters and allows for on-the-fly definition and completion of new tasks. Unlike hybrid force/impedance controllers, we do not need to define large damping and stiffness matrices, and we decouple the joint level control gains from the compliance control. The result is a robotic manipulator that can dynamically switch between unconstrained motion and contact tasks and provides a lot of versatility to perform a wide variety of tasks.

Safe, Aggressive Quadrotor Flight via Reachability-Based Trajectory Design

Quadrotors can provide services such as infrastructure inspection and search-and-rescue, which require operating autonomously in cluttered environments. Autonomy is typically achieved with receding-horizon planning, where a short plan is executed while a new one is computed, because sensors receive limited information at any time. To ensure safety and prevent robot loss, plans must be verified as collision free despite uncertainty (e.g, tracking error). Existing spline-based planners dilate obstacles uniformly to compensate for uncertainty, which can be conservative. On the other hand, reachability-based planners can include trajectory-dependent uncertainty as a function of the planned trajectory. This work applies Reachability-based Trajectory Design (RTD) to plan quadrotor trajectories that are safe despite trajectory-dependent tracking error. This is achieved by using zonotopes in a novel way for online planning. Simulations show aggressive flight up to 5 m/s with zero crashes in 500 cluttered, randomized environments.

Compressive Sensing-Based Reconstruction of Lissajous-Like Nodding Lidar Data

Publisher’s Note: This paper was selected for publication in ASME Letters in Dynamic Systems and Control. https://www.asmedigitalcollection.asme.org/lettersdynsys/article/doi/10.1115/1.4046574/1075674/Compressive-Sensing-Based-Reconstruction-of

Analysis of a Novel Command Governor-Based Adaptive Cruise Controller for Non-Cooperative Vehicle Following

This paper presents a novel adaptive cruise control (ACC) strategy that utilizes a command governor (CG) to enforce vehicle following constraints. The CG formulation relies on knowledge of the maximum possible braking deceleration of the lead vehicle and a tunable assumption regarding the lead vehicle velocity profile (offering different levels of conservatism) to modify wheel torque commands to ensure safe following. In particular, a safe following distance is defined as one in which the ego vehicle can avoid collision with the lead vehicle and maintain a sufficient following distance in the event that the lead vehicle exerts maximum braking deceleration. The CG seeks to adjust the wheel torque command such that the aforementioned constraint is satisfied at every step in a prediction horizon (i.e., at every step, if the lead vehicle exerts maximum braking deceleration, the ego vehicle can brake and remain outside of the aforementioned buffer zone), which requires an estimate of future lead vehicle behavior. In this work, we explore different levels of conservatism with regard to this assumption. Simulations are presented for a heavy-duty truck, using a stochastic lead vehicle model that has been calibrated with actual traffic data. Even for the most conservative lead vehicle prediction models, results show that this CG-based ACC strategy can reduce braking energy expended (used as a surrogate for fuel wasted) by up to 78%, while improving drivability and reducing total trip time.

A Passive Jumping Mechanism

In this paper we present a novel unactuated mechanism that utilizes gravity to jump. The passive jumper is a hoop whose center of mass does not coincide with its geometric center. When the hoop rolls down an inclined plane, the center of mass of the hoop moves along a cycloid. As the hoop gains speed moving down the inclined plane, the normal reaction between the hoop and the plane becomes insufficient to ensure contact between the hoop and the plane. This allows the hoop to ‘jump’. Experiments and analysis show that such a jump can be significant, with the jump height from the plane being as high as one body length (diameter) of the hoop. The mechanics of the passive jumping hoop powered by gravity investigated in this paper can inspire the design of actuated jumping robots that can both roll and jump.

Human Driver Modeling Based on Analytical Optimal Solutions: Stopping Behaviors at the Intersections

Publisher’s Note: This paper was selected for publication in ASME Letters in Dynamic Systems and Control. https://www.asmedigitalcollection.asme.org/lettersdynsys/article/doi/10.1115/1.4046575/1075675/Human-Driver-Modeling-Based-on-Analytical-Optimal

Toward Magneto-Electroactive Endoluminal Soft (MEESo) Robots

This paper introduces a magneto-electroactive endoluminal soft (MEESo) robot concept, which could enable new classes of catheters, tethered capsule endoscopes, and other mesoscale soft robots designed to navigate the natural lumens of the human body for a variety of medical applications. The MEESo locomotion mechanism combines magnetic propulsion with body deformation created by an ionic polymer-metal composite (IPMC) electroactive polymer. A detailed explanation of the MEESo concept is provided, including experimentally validated models and simulated magneto-electroactive actuation results demonstrating the locomotive benefits of incorporating an IPMC compared to magnetic actuation alone.