Emmanuel Collins
Florida State University, Mechanical Engineering, Faculty Member
Running legged robots present several challenges when motion planning, these challenges often stem from the difficulty in predicting motion due to the innate complexity of the mechanical system, persistent effects of terrain and... more
Running legged robots present several challenges when motion planning, these challenges often stem from the difficulty in predicting motion due to the innate complexity of the mechanical system, persistent effects of terrain and foot-ground interactions. While reasonable approximations of the inherent motion models of the Ghost Robotics Minitaur platform can be learned through data-driven approaches, system mechanical robustness and the requisite experimental time discourages running a full battery of experiments to determine a unique model for each considered terrain. This paper discusses the development of turning maneuvers on the quadruped robot Minitaur and the approach taken to adapt a learned model for new terrains. The prediction model was generated through data collected in indoor experiments using a VICON motion capture system and adapted through use of a correction factor for new terrains. Resulting motion planning differences show that the addition of this factor improves...
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Real world motion planning often suffers from the need to replan during execution of the trajectory. This replanning can be triggered as the robot fails to properly track the trajectory or new sensory information is provided that... more
Real world motion planning often suffers from the need to replan during execution of the trajectory. This replanning can be triggered as the robot fails to properly track the trajectory or new sensory information is provided that invalidates the planned trajectory. Particularly in the case of many occluded obstacles or in unstructured terrain, replanning is a frequent occurrence. Developing methods to allow the robots to replan efficiently allows for greater operation time and can ensure robot mission success. This paper presents a novel approach that updates heuristic weights of a sampling based A* planning algorithm. This approach utilizes parallel instances of this planner to quickly search through multiple heuristic weights within its allotted replanning time. These weights are employed upon triggered replanning to speed up computation time. The concept is tested on a simulated quadrupedal robot LLAMA with realistic constraints on computation time imposed.
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Unmanned Underwater Vehicles (UUVs) can be utilized to perform difficult tasks in cluttered environments such as harbor and port protection. However, since UUVs have nonlinear and highly coupled dynamics, motion planning and control can... more
Unmanned Underwater Vehicles (UUVs) can be utilized to perform difficult tasks in cluttered environments such as harbor and port protection. However, since UUVs have nonlinear and highly coupled dynamics, motion planning and control can be difficult when completing complex tasks. Introducing models into the motion planning process can produce paths the vehicle can feasibly traverse. As a result, Sampling-Based Model Predictive Control (SBMPC) is proposed to simultaneously generate control inputs and system trajectories for an autonomous underwater vehicle (AUV). The algorithm combines the benefits of sampling-based motion planning with model predictive control (MPC) while avoiding some of the major pitfalls facing both traditional sampling-based planning algorithms and traditional MPC. The method is based on sampling (i.e., discretizing) the input space at each sample period and implementing a goal-directed optimization (e.g., A*) in place of standard numerical optimization. This fo...
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Simplicity, flexibility, and responsiveness are among the reasons that behavior fuzzy control in robotic systems is popular. One of the major problems faced in the design of behavior based fuzzy control systems for mobile robots is that... more
Simplicity, flexibility, and responsiveness are among the reasons that behavior fuzzy control in robotic systems is popular. One of the major problems faced in the design of behavior based fuzzy control systems for mobile robots is that of resolving conflicts between different behaviors when the behaviors are given full autonomy, i.e., the control is decentralized. Under this framework the behaviors are forced to compete for robot control whereby some of the behaviors must lose the competition and hence be ignored by the robot. By ignoring the behaviors that lost in the competition, the control system becomes less robust and can lead the robot to undesired reactions. This paper presents a new method of designing fuzzy behavior control systems for robotic navigation that honors all behaviors. The control system is centralized and it takes into consideration the interests of all behaviors. Each behavior is designed to fire several proposals on how to react to a given situation. There is a central command unit that takes the proposals from all behaviors in the system and selects a single proposal that best meets the demands of all behaviors in the system. This structure allows the behavioral fusion to be robust in the sense that under all circumstances, each behavior will be honored and the robot reactions will satisfy the requirements of each behavior.
Research Interests: Engineering, Computer Science, Fuzzy Logic, Fuzzy Control, Control system, and 12 moreNavigation, Mobile Robots, ROBOT, Mobile Robot, Robot Control, Flexibility in engineering design, Fire Severity, Control Management, Boolean Satisfiability, Behavioral Control, Robot Navigation, and Fuzzy Control System
ABSTRACT Solutions for a system of nonlinear equations are needed quite frequently in many applications. For example, selection of design parameters in most control design problems can be posed in terms of the solution of nonlinear... more
ABSTRACT Solutions for a system of nonlinear equations are needed quite frequently in many applications. For example, selection of design parameters in most control design problems can be posed in terms of the solution of nonlinear equations. The available methods for such solutions are based on explicit and determinate systems. This paper presents a method of solving inexplicit and/or underdetermined systems using fuzzy logic. The method is formulated using the theory of interval arithmetic and subsequently transformed into fuzzy set rules. This method is seen to be effective although it is slower than Newton's method for explicit determinate problems.
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One of the well known deficiencies of most modern control methods is that they attempt to represent multiple criteria using scalar cost functions. Hence, in practice the cost function weights (static or dynamic) must be chosen by trial... more
One of the well known deficiencies of most modern control methods is that they attempt to represent multiple criteria using scalar cost functions. Hence, in practice the cost function weights (static or dynamic) must be chosen by trial and error in order to satisfy the multiple objectives. This paper develops a fuzzy algorithm for selecting the weights in an linear
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ABSTRACT Constraints on the variances of the system inputs and selected system outputs have been shown to have practical applications. One way of designing control laws that achieve these objectives is by designing an LQG (or optimal... more
ABSTRACT Constraints on the variances of the system inputs and selected system outputs have been shown to have practical applications. One way of designing control laws that achieve these objectives is by designing an LQG (or optimal ℋ2) controller with an appropriate choice of the input and output weighting matrices. However, one of the well known limitations of practical implementation of an LQG controller is that it is of the same order as the plant model. The two primary methods of designing reduced order controllers are the truncation (or indirect) methods and parameter optimization (or direct methods). These two methods are often used simultaneously since the truncated controllers are often used to initialize the direct methods. This paper considers the design of ℋ2 optimal reduced order controllers to meet a set of variance constraints. As with full order control, this problem involves the proper choice of the weighting matrices in the cost function. Although the behavior of the variances with weight variations in reduced order control design are experimentally seen to be more erratic than in optimal full order design, a fuzzy algorithm previously developed for the full order variance constrained problem is shown to be applicable to the reduced order variance constrained problem. Three reduced order schemes are developed and compared. The first two schemes involve direct reduced order design while the third scheme involves reduced order design using modified balanced truncation. The first two schemes differ only in how they are initialized with the first approach using the weights from the full order variance constrained problem and the second approach using unity weights for initialization. The three schemes are compared using numerical experiments. The results clearly demonstrate the feasibility of reduced order variance constrained control design
Research Interests: Mechanical Engineering, Mathematics, Computer Science, Optimal Control, Fuzzy Logic, and 12 moreFuzzy set theory, Automatic Control, Fuzzy Control, Control Design, Design Methodology, Constrained control, Mathematical Optimization, Design optimization, Parameter Optimization, Cost Function, Direct Method, and weighting
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Research Interests: Engineering, Mechanical Engineering, Chemical Engineering, Computer Science, Estimation Theory, and 11 moreParameter estimation, Kalman Filter, Nonlinear Systems, Steady state, Mathematical Model, Nonlinear system, Continuous stirred tank reactor, Extended Kalman Filter, Optimization of Chemical Processes, Equilibrium point, and equilibrium points
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Applications of autonomous ground vehicles (AGVs) in field operations have expanded from simple transportation tasks to complicated tasks such as military and rescue missions. The complexity in controlling these vehicles increases with... more
Applications of autonomous ground vehicles (AGVs) in field operations have expanded from simple transportation tasks to complicated tasks such as military and rescue missions. The complexity in controlling these vehicles increases with the complexity of the tasks that the vehicles are intended for and the environment in which they are to operate. The behavior robotics approach has been adopted as a paradigm for controlling these systems. Due the uncertainty that surrounds the vehicle dynamics and their environments, fuzzy logic control approaches for navigation control have been developed, hence resulting in fuzzy behavior control systems. Two types of behavior structures have been proposed: the univalued and multivalued behaviors. This paper1 presents a qualitative and quantitative comparison of the structure and performance of these behavior systems. The quantitative performance comparison is performed by using nu- merical simulation results for the motions of two identical AGVs e...
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We present the input-normal Riccati parameterization, a generalization of Kabamba's input-normal form, which allows the continuous parameterization of the set of minimal linear systems of a given order that have distinct... more
We present the input-normal Riccati parameterization, a generalization of Kabamba's input-normal form, which allows the continuous parameterization of the set of minimal linear systems of a given order that have distinct singular values; the input-normal Riccati form has no requirement that the underlying system be stable. We also present formulas for the use of the input-normal Riccati parameterization for the
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ABSTRACT This paper presents a fuzzy logic approach for determining a numerical solution to a consistent system of algebraic equations F(x)=0 in which the function F(·) is not explicitly defined and may be underdetermined. Such systems... more
ABSTRACT This paper presents a fuzzy logic approach for determining a numerical solution to a consistent system of algebraic equations F(x)=0 in which the function F(·) is not explicitly defined and may be underdetermined. Such systems arise frequently in many engineering design problems where design parameters must be chosen using qualitative information by the designer to meet a set of desired performance constraints. The proposed method also can be used for a consistent system of nonlinear equations in which F(·) is explicitly defined and may have fewer independent equations than the number of unknowns. However, this method is very computationally demanding; hence, it is not advisable to apply it to problems involving explicit functions that can be solved using other existing numerical methods. It is seen that this method works quite well and numerical solutions for such problems can be obtained, although it is much slower than Newton's method when employed to consistent, explicit nonlinear equations.
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Input constraints are active in robot trajectory planning when a mobile robot traverses mobility challenges such as steep hills that limit the acceleration of the robot due to the torque constraints of the motor or engine or in... more
Input constraints are active in robot trajectory planning when a mobile robot traverses mobility challenges such as steep hills that limit the acceleration of the robot due to the torque constraints of the motor or engine or in manipulator lifting tasks when the load is sufficiently heavy that the torque constraints of the robot's motor prevent it from statically supporting the load in regions of the robot's workspace. This paper presents a general methodology for solving these planning tasks using a minimum-time cost function and applies it to the problem of a multiple degrees-of-freedom (DOF) manipulator lifting a heavy load. Planning for these types of problems requires use of the robot's dynamic model. Here, we plan using sampling-based model predictive optimization (SBMPO), which is only practical if the planning can be done quickly. Hence, substantial attention is given to efficient computations by: (1) using the dynamic model without integrating it, (2) using opti...
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Abstract: The ability to recognize the encountered terrain is an essential part of any terrain-dependent control system designed for mobile robots. Terrains such as sand and gravel make vehicle mobility more difficult and thus reduce... more
Abstract: The ability to recognize the encountered terrain is an essential part of any terrain-dependent control system designed for mobile robots. Terrains such as sand and gravel make vehicle mobility more difficult and thus reduce vehicle performance. To alleviate this ...
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