Emmanuel G. Collins

Robust Automatic Parallel Parking in Tight Spaces via Fuzzy Logic

This paper develops and experimentally demonstrates a robust automatic parallel parking algorithm for parking in tight spaces. Novel fuzzy logic controllers are designed for each step of the maneuvering process. The controllers are flrst demonstrated by simulation using the kinematic model of a skid steering autonomous ground vehicle (AGV). They are then demonstrated experimentally on a skid steering AGV and it is shown that the developed algorithm has the ability to parallel park AGVs in tight spaces under both vehicle localization errors and parking space detection errors. This paper also presents a genetic fuzzy system which uses a genetic algorithm’s learning ability to determine efiective parameters for the developed fuzzy logic controllers. The genetic fuzzy system is used to tune the fuzzy logic algorithm for both a skid steering AGV and a front-wheel steering AGV.

Vibration-based terrain classification using surface profile input frequency responses

Terrain variations can greatly influence autonomous ground vehicle (AGV) performance. However, if the terrain is properly identified, the AGV control systems can be adjusted to better suit the terrain. Current terrain classification techniques are largely based on vision and terrain dependent vehicle reactions. But at this time both methods have limitations. Visual methods can easily be fooled by fallen leaves or other superficial ground coverings, whereas response-based methods yield poor results when the AGV speed is significantly different from the training speeds. In addition, current response-based methods are not capable of identifying the multiple terrains that a vehicle may simultaneously traverse, e.g., when one subset of wheels is on-road and one is off-road. This paper proposes a response-based method called terrain input classification to solve the issue of speed dependency and multiple terrain traversal. This method is dependent upon knowing the vibration transfer function of the AGV. The method has been verified through terrain-based simulation results and is ready for experimental testing.

Human-aware robot motion planning with velocity constraints

This paper addresses the issue of how high-speed robots may move among humans such that the robots complete their tasks efficiently while the humans in the environment feel safe and comfortable. It describes the Segway robotic platform used for this research and then discusses the three primary research areas needed to develop the human-aware motion planner. First, it is necessary to conduct experiments with humans to develop human aware velocity constraints as a function of the distance of the robot from a human. Next, these velocity constraints must be used to plan the robot motion in real time. Finally, practical implementation of this motion planner requires the ability to robustly detect humans using the available vision sensors. The approach taken to each of these problems is described in this paper along with preliminary results.

Implementation of Multi-valued Fuzzy Behavior Control for Robot Navigation in Cluttered Environments

Navigation in environments that are densely cluttered with obstacles is still a challenge for Autonomous Ground Vehicles (AGVs), especially when the configuration of obstacles is not known a priori. Reactive local navigation schemes that tightly couple the robot actions to the sensor information have proved to be effective in these environments, and because of the environmental uncertainties, fuzzy behavior systems have been proposed. The most difficult problem in applying these systems is that of arbitrating or fusing the reactions of the individual behaviors. This paper presents an architectural design of a fuzzy behavior based system for navigation control of robotic vehicles using multivalued reactive fuzzy behaviors. This design allows the robot to thoroughly use the available sensor information when choosing the control action to be taken. Experimental results show that the proposed method can smoothly and effectively guide a robot through cluttered environments such as dense forests.

Optimal Popov controller analysis and synthesis for systems with real parameter uncertainties

Robust performance analysis plays an important role in the design of controllers for uncertain multivariable systems. Recent research has investigated the use of absolute stability criteria to develop less conservative analysis tests for systems with linear and nonlinear real parameter uncertainties. This note extends previous work on optimal /spl Hscr/2 performance analysis using the Popov criterion by presenting a numerical homotopy algorithm that can be used to analyze systems with less restrictive assumptions on the structure of the uncertainty block. The technique is used to compare relative robustness capabilities of the various control algorithms that have been designed for the Middeck active control experiment (MACE). The analysis is combined with the previously presented Popov controller synthesis to yield compensators that guarantee robust performance for systems with real parameter uncertainty.

An efficient numerically robust homotopy algorithm for H2 model reduction using the optimal projecton equations

Homotopy approaches have previously been developed for synthesizing #2 optimal reduced-order models. Some of the previous homotopy algorithms were based on directly solving the optimal projection equations, a set of two Lyapunov equations mutually coupled by a nonlinear term involving a projection matrix r, that characterize the optimal reduced-order model. These algorithms are numerically robust but suffer from the curse of large dimensionality. Subsequently, gradient-based homotopy algorithms were developed. To make these algorithms efficient and to eliminate singularities along the homotopy path, the basis of the reduced-order model was constrained to a minimal parameterization. However, the resultant homotopy algorithms sometimes experienced numerical ill-conditioning or failure due to the minimal parameterization constraint. This paper presents a new homotopy approach to solve the optimal projection equations for #2 model reduction. The current algorithm avoids the large dimensionality of the previou...

Sampling-Based Direct Trajectory Generation Using the Minimum Time Cost Function

This paper presents a methodology for computationally efficient, direct trajectory generation using sampling with the minimum time cost function, where only the initial and final positions and velocities of the trajectory are specified. The approach is based on a randomized A* algorithm called Sampling-Based Model Predictive Optimization (SBMPO) that exclusively samples in the input space and integrates a dynamic model of the system. The paper introduces an extended kinematic model, consisting of the standard kinematic model preceded by two integrators. This model is mathematically a dynamic model and enables SBMPO to sample the acceleration and provide the acceleration, velocity, and position as functions of time that are needed by a typical trajectory tracking controller. A primary contribution of this paper is the development of an appropriate “optimistic A* heuristic” (i.e, a rigorous lower bound on the chosen cost) based on the solution of a minimum time control problem for the system q = u; this heuristic is a key enabler to fast computation of trajectories that end in zero velocity. Another contribution of this paper is the use of the extended kinematic model to develop a trajectory generation methodology that takes into account torque constraints associated with the regular dynamic model without having to integrate this more complex model as has been done previously. This development uses the known form of the trajectory following control law. The results are initially illustrated experimentally using a 1 degree of freedom (DOF) manipulator lifting heavy loads, which necessitates the development of trajectories with appropriate momentum characteristics. Further simulation results are for a 2 DOF manipulator.

Motion planning for steep hill climbing

The motors or engines of an autonomous ground vehicles (AGV) have torque and power limitations, which limit their abilities to climb steep hills, which are defined to be hills that have high grade sections in which the vehicle is forced to decelerate. Traversal of a steep hill requires the vehicle to have sufficient momentum before entering the hill. This problem is part of a larger class of momentum-based motion planning problems such as the problem of lifting heavy objects with manipulators. Hence, solutions to the steep hill climbing problem have much wider applicability. The motion planning here is accomplished using a dynamic model of the skid-steered AGV used in the experiments along with Sampling Based Model Predictive Control (SBMPC), a recently developed input sampling planning algorithm that may be viewed as a generalization of LPA* to the direct use of kinodynamic models. The motion planning is demonstrated experimentally using two scenarios, one in which the robot starts at rest at the bottom of a hill and one in which the robot starts at rest a distance from the hill. The first scenario requires the AGV to first reverse direction so that the vehicle can gather enough momentum before reaching the hill. This corresponds to having the vehicle begin at a local minimum, which results in a problem that many traditional model predictive control methods cannot solve. It is seen that, whereas open loop trajectories can lead to vehicle immobilization, SBMPC successfully uses the information provided by the dynamic model to ensure that the AGV has the requisite momentum.

Efficient and accurate subpixel path based stereo matching

This paper presents an efficient algorithm to achieve accurate subpixel matchings for calculating correspondences between stereo images based on a path-based matching algorithm. Compared to point-by-point stereo matching algorithms, path-based algorithms resolve local ambiguities by maximizing the cross correlation (or other measurements) along a path, which can be implemented efficiently using dynamic programming. An effect of the global matching criterion is that the cross correlation at all pixels can contribute to the criterion; since cross correlation can change significantly even with subpixel changes, to achieve subpixel accuracy, it is no longer sufficient to first find the path that maximizes the criterion and then refine to subpixel accuracy. In this paper, by writing bilinear interpolation using integral images, we show that cross correlations at all subpixel locations can be computed efficiently and thus lead to a subpixel accuracy path based matching algorithm. Our results show the feasibility of the method and illustrate the significant improvements over the original path-based matching method.

Fuzzy behavior navigation for an unmanned helicopter in unknown environments

Aerial missions that require unmanned aerial vehicles (UAVs) to fly autonomously in unknown and hostile environments are inevitable. These UAVs must be equipped with a fully autonomous navigation system. Many methods that have been proposed for navigation of autonomous systems either lack the necessary intelligence or are not responsive enough to cope with the flying speeds of UAVs. This paper presents a new method for autonomous navigation of UAVs using reactive fuzzy behaviors. It extends a 2D fuzzy behavior navigation system used in unmanned ground vehicles to a 3D navigation system suitable for UAVs. The research is based on a range finding sensor system by judicious use of a 2D range finder. A novel defuzzification method for 3D navigation systems is developed to generate the most desired flying orientation. Simulation results carried out using MATLABs Virtual Reality Toolbox show that the proposed system works very well to avoid static obstacles.