Sterling J. Anderson

An optimal-control-based framework for trajectory planning, threat assessment, and semi-autonomous control of passenger vehicles in hazard avoidance scenarios

This paper formulates the vehicle navigation task as a constrained optimal control problem with constraints bounding a traversable region of the environment. A model predictive controller iteratively plans an optimal vehicle trajectory through the constrained corridor and uses this trajectory to establish the minimum threat posed to the vehicle given its current state and driver inputs. Based on this threat assessment, the level of controller intervention required to prevent departure from the traversable corridor is calculated and driver/controller inputs are scaled accordingly. Simulated and experimental results are presented to demonstrate multiple threat metrics and configurable intervention laws.

Constraint-based planning and control for safe, semi-autonomous operation of vehicles

This paper presents a new approach to semi-autonomous vehicle hazard avoidance and stability control, based on the design and selective enforcement of constraints. This differs from traditional approaches that rely on the planning and tracking of paths. This emphasis on constraints facilitates “minimally-invasive” control for human-machine systems; instead of forcing a human operator to follow an automation-determined path, the constraint-based approach identifies safe homotopies, and allows the operator to navigate freely within them, introducing control action only as necessary to ensure that the vehicle does not violate safety constraints. The method evaluates candidate homotopies based on “restrictiveness”, rather than traditional measures of path goodness, and designs and enforces requisite constraints on the humans control commands to ensure that the vehicle never leaves the controllable subset of a desired homotopy. Identification of these homotopic classes in off-road environments is performed using geometric constructs. The goodness of competing homotopies and their associated constraints is then characterized using geometric heuristics. Finally, input limits satisfying homotopy and vehicle dynamic constraints are enforced using threat-based feedback mechanisms to ensure that the vehicle avoids collisions and instability while preserving the human operators situational awareness and mental models. The methods developed in this work are shown in simulation and experimentally demonstrated in safe, high-speed teleoperation of an unmanned ground vehicle.

Experimental Performance Analysis of a Homotopy-Based Shared Autonomy Framework

This paper describes and experimentally demonstrates a new approach to shared-adaptive control of human-machine systems. Motivated by observed human proclivity toward fields of safe travel rather than specific trajectories, our approach is rooted in the planning and enforcement of constraints rather than the more traditional reference paths. This approach identifies path homotopies, bounds a desired homotopy with constraints, and allocates control as necessary to ensure that these constraints remain satisfied without unduly restricting the human operator. We present a summary of this frameworks technical background and analyze its effect both with and without driver feedback on the performance and confidence of 20 different drivers teleoperating an unmanned (teleoperated) vehicle through an outdoor obstacle course. In 1200 trials, constraint-based semiautonomy was shown to increase the operator speed by 26% while reducing the occurrence of collisions by 78%, and improving overall user confidence and sense of control by 44% and 12%, respectively-all the while assuming less than 43% control of the vehicle.

Design and Development of an Optimal-Control-Based Framework for Trajectory Planning, Threat Assessment, and Semi-autonomous Control of Passenger Vehicles in Hazard Avoidance Scenarios

This paper describes the design of an optimal-control-based active safety framework that performs trajectory planning, threat assessment, and semi-autonomous control of passenger vehicles in hazard avoidance scenarios. This framework allows for multiple actuation modes, diverse trajectory-planning objectives, and varying levels of autonomy. A model predictive controller iteratively plans a best-case vehicle trajectory through a navigable corridor as a constrained optimal control problem. The framework then uses this trajectory to assess the threat posed to the vehicle and intervenes in proportion to this threat. This approach minimizes controller intervention while ensuring that the vehicle does not depart from a navigable corridor of travel. Simulation and experimental results are presented here to demonstrate the framework’s ability to incorporate configurable intervention laws while sharing control with a human driver.

Viscoelastic Damping of Ortho-Planar Springs

This paper presents the design of a damped ortho-planar spring that uses viscoelastic constrained-layer damping to reduce the free response oscillations of the spring and suppress modal resonances in that response. Background, theory, and applications surrounding fully-compliant ortho-planar springs and viscoelastic damping treatments are first discussed. Next, the effect of various constrained layer thickness on the spring constant, damping ratio, equivalent viscous damping ratio, modal frequencies, and modal damping ratios are compared, and trends discussed. The results show that the equivalent viscous damping co-efficient of the viscoelastically-damped spring can be increased to nearly 2.5 times that of the reference configuration without significantly changing the size of the constraining layer or the spring constant of the ortho-planar spring. Viscoelastically-damped ortho-planar springs are also shown to successfully remove mechanical noise from a contact resistance test stand.Copyright

Semi-autonomous Avoidance of Moving Hazards for Passenger Vehicles

This paper presents a method for semi-autonomous hazard avoidance in the presence of unknown moving obstacles and unpredictable driver inputs. This method iteratively predicts the motion and anticipated intersection of the host vehicle with both static and dynamic hazards and excludes projected collision states from a traversable corridor. A model predictive controller iteratively replans a stability-optimal trajectory through the navigable region of the environment while a threat assessor and semi-autonomous control law modulate driver and controller inputs to maintain stability, preserve controllability, and ensure safe hazard avoidance. The efficacy of this approach is demonstrated through both simulated and experimental results using a semi-autonomously controlled Jaguar S-Type.Copyright

Experimental Study of an Optimal-Control- Based Framework for Trajectory Planning, Threat Assessment, and Semi-Autonomous Control of Passenger Vehicles in Hazard Avoidance Scenarios

This paper describes the design of an optimal-control-based active safety framework that performs trajectory planning, threat assessment, and semiautonomous control of passenger vehicles in hazard avoidance scenarios. The vehicle navigation problem is formulated as a constrained optimal control problem with constraints bounding a navigable region of the road surface. A model predictive controller iteratively plans an optimal vehicle trajectory through the constrained corridor. Metrics from this “best-case” scenario establish the minimum threat posed to the vehicle given its current state. Based on this threat assessment, the level of controller intervention required to prevent departure from the navigable corridor is calculated and driver/controller inputs are scaled accordingly. This approach minimizes controller intervention while ensuring that the vehicle does not depart from a navigable corridor of travel. It also allows for multiple actuation modes, diverse trajectory-planning objectives, and varying levels of autonomy. Experimental results are presented here to demonstrate the framework’s semiautonomous performance in hazard avoidance scenarios.