Miloš Žefran

On mechanical control systems with nonholonomic constraints and symmetries

Abstract This paper presents a computationally efficient method for deriving coordinate representations for the equations of motion and the affine connection describing a class of Lagrangian systems. We consider mechanical systems endowed with symmetries and subject to nonholonomic constraints and external forces. The method is demonstrated on two robotic locomotion mechanisms known as the snakeboard and the roller racer. The resulting coordinate representations are compact and lead to straightforward proofs of various controllability results.

Monitorability of stochastic dynamical systems

Monitoring is an important run time correctness checking mechanism. This paper introduces the notions of monitorability and strong monitorability for partially observable stochastic systems, and gives necessary and sufficient conditions characterizing them. It also presents important decidability and complexity results for checking these properties for finite state systems. Furthermore, it presents general monitoring techniques for the case when systems are modeled as quantized probabilistic hybrid automata, and the properties are specified as safety or liveness automata. Experimental results showing the effectiveness of the methods are given.

Using monocular images to estimate interaction forces during minimally invasive surgery

The lack of haptic feedback during minimally invasive surgery can cause significant tissue damage and increase morbidity. Estimating the applied force from endoscopic images is a promising approach, especially using binocular images. However, many existing operation rooms are only equipped with monocular endoscopes, making force estimation more problematic. In this paper a new method for estimating the applied force from monocular endoscope images is proposed. The main contribution is the concept of virtual template that enables modeling of surface deformation without the knowledge of the undeformed shape. Results of the in vitro experiment with the lamb liver support the practicality and effectiveness of the proposed method.

A comparison of the embedding method with multiparametric programming, mixed-integer programming, gradient-descent, and hybrid minimum principle-based methods

In recent years, the embedding approach for solving switched optimal control problems has been developed in a series of papers. However, the embedding approach, which advantageously converts the hybrid optimal control problem to a classical nonlinear optimization, has not been extensively compared with alternative solution approaches. The goal of this paper is thus to compare the embedding approach with multiparametric programming, mixed-integer programming [mixed integer programming (MIP), commercial (CPLEX)], and gradient-descent-based methods in the context of five recently published examples: 1) a spring-mass system; 2) moving-target tracking for a mobile robot; 3) two-tank filling; dc-dc boost converter; and 5) skid-steered vehicle. A sixth example, an autonomous switched 11-region linear system, is used to compare a hybrid minimum principle method and traditional numerical programming. For a given performance index (PI) for each case, cost and solution times are presented. It is shown that there are numerical advantages of the embedding approach: lower PI cost (except in some instances when autonomous switches are present), generally faster solution time, and convergence to a solution when other methods may fail. In addition, the embedding method requires no ad hoc assumptions (e.g., predetermined mode sequences) or specialized control models. Theoretical advantages of the embedding approach over the other methods are also described; guaranteed existence of a solution under mild conditions, convexity of the embedded hybrid optimization problem (under the customary conditions on the PI), solvability with traditional techniques (e.g., sequential quadratic programming) avoiding the combinatorial complexity in the number of modes/discrete variables of MIP, applicability to affine nonlinear systems, and no need to explicitly assign discrete/mode variables to autonomous switches. Finally, common misconceptions regarding the embedding approach are addressed, including whether it uses an average value control model (no), whether it is necessary to tweak the algorithm to obtain bang-bang solutions (no), whether it requires infinite switching to implement embedded solution (no), and whether it has real-time capability (yes).

Runtime monitoring of stochastic cyber-physical systems with hybrid state

Correct functioning of cyber-physical systems is of critical importance. This is more so in the case of safety critical systems such as in medical, automotive and many other applications. Since verification of correctness, in general, is infeasible and testing is not exhaustive, it is of critical importance to monitor such system during their operation and detect erroneous behaviors to be acted on. A distinguishing property of cyber-physical systems is that they are described by a mixture of integer-valued and real-valued variables. As a result, approaches that assume countable number of states are not applicable for runtime monitoring of such systems. This paper proposes a formalism, called Extended Hidden Markov systems, for specifying behavior of systems with such hybrid state. Using measure theory, it exactly characterizes when such systems are monitorable with respect to a given property. It also presents monitoring algorithms and experimental results showing their effectiveness.

Generalized penetration depth for penalty-based six-degree-of-freedom haptic rendering

Existing penalty-based haptic rendering approaches are based on the penetration depth estimation in strictly translational sense and cannot properly take object rotation into account. We propose a new six-degree-of-freedom (6-DOF) haptic rendering algorithm which is based on determining the closest-point projection of the inadmissible configuration onto the set of admissible configurations. Energy is used to define a metric on the configuration space. Once the projection is found the 6-DOF wrench can be computed from the generalized penetration depth. The space is locally represented with exponential coordinates to make the algorithm more efficient. Examples compare the proposed algorithm with the existing approaches and show its advantages.

The roles and recognition of Haptic-Ostensive actions in collaborative multimodal human-human dialogues

The RoboHelper project has the goal of developing assistive robots for the elderly. One crucial component of such a robot is a multimodal dialogue architecture, since collaborative task-oriented human-human dialogue is inherently multimodal. In this paper, we focus on a specific type of interaction, Haptic-Ostensive (H-O) actions, that are pervasive in collaborative dialogue. H-O actions manipulate objects, but they also often perform a referring function.We collected 20 collaborative task-oriented human-human dialogues between a helper and an elderly person in a realistic setting. To collect the haptic signals, we developed an unobtrusive sensory glove with pressure sensors. Multiple annotations were then conducted to build the Find corpus. Supervised machine learning was applied to these annotations in order to develop reference resolution and dialogue act classification modules. Both corpus analysis, and these two modules show that H-O actions play a crucial role in interaction: models that include H-O actions, and other extra-linguistic information such as pointing gestures, perform better.For true human-robot interaction, all communicative intentions must of course be recognized in real time, not on the basis of annotated categories. To demonstrate that our corpus analysis is not an end in itself, but can inform actual human-robot interaction, the last part of our paper presents additional experiments on recognizing H-O actions from the haptic signals measured through the sensory glove. We show that even though pressure sensors are relatively imprecise and the data provided by the glove is noisy, the classification algorithms can successfully identify actions of interest within subjects.

Decision-Theoretic Monitoring of Cyber-Physical Systems

Runtime monitoring has been proposed as an alternative to formal verification for safety critical systems. This paper introduces a decision-theoretic view of runtime monitoring. We formulate the monitoring problem as a Partially Observable Markov Decision Process (POMDP). Furthermore, we adopt a Partially Observable Monte-Carlo Planning (POMCP) to compute an approximate optimal policy of the monitoring POMDP. We show how to construct the POMCP for the monitoring problem and demonstrate experimentally that it can be effectively applied even when some of the state-space variables are continuous, the case where many other POMDP solvers fail. Experimental results on a mobile robot system show the effectiveness of the proposed POMDP-monitor.

Notch filter and MPC for powered wheelchair operation under Parkinson's tremor

This paper considers a model predictive control (MPC) strategy for mitigating the effects of Parkinson tremors on a movement-sensing, joystick controlled battery powered wheelchair with regenerative braking to extend its range between charges. Regenerative braking transforms the wheelchair model into a (switched) hybrid system. The wheelchair is represented as a joystick controlled wheeled mobile robot (WMR) having four modes of operation, propelling and regenerative braking for each wheel. The joystick is presumed to provide velocity, orientation, and position commands. To enhance safety, velocity and acceleration saturation limits are imposed as constraints on the control activation. The paper delineates a notch filter to remove the main Parkinsons tremor followed by a model predictive control strategy to track velocity, orientation, and distance to a wall commands from the joystick. Results show significant feasible advantages for safe wheelchair operation by Parkinsons patients with tremor.

Counting and rendezvous: Two applications of distributed consensus in robotics

Consensus protocols have been widely studied in recent years in the control community. We discuss two applications of consensus protocols in robotics: counting and rendezvous. For counting, the main issue is how each agent can estimate the total number of robots in a network by using limited communications with its neighbors. For rendezvous, the aim is to make the agents converge to a common meeting point, again by only allowing them to communicate with immediate neighbors. We present a formal analysis of the proposed algorithms and prove their convergence properties by relying on the theory of consensus protocols.