Marcelo H. Ang

Specifying and achieving passive compliance based on manipulator structure

We explore the possibility of achieving passive compliance through the structure of the manipulator itself. The emphasis is on passive compliance because a minimum of passive compliance to prevent jamming will always be required even when active stiffness control is employed. Particular attention is given to the large class of robots with nonbackdrivable actuators, where the actuator must be commanded to move, and in which actuator forces or torques are not easily interpreted as end-effector forces and torques. We present a novel framework for specifying the desired end-effector compliance for several tasks in terms of stiffness matrices. We explore whether the desired stiffness matrix of a manipulator can be achieved by using the natural or designed stiffness of the manipulator limbs themselves. Several techniques for adjusting the manipulator stiffness matrix are proposed. Achieving this variable passive compliance allows the attainment of high stiffnesses for fast and accurate movements and low stiffness values for force control. Furthermore, achieving nondiagonal stiffness properties wherein there are force and motion coupling in different directions is shown to be useful to prevent jamming and contact induced vibrations. >

Active compliance control of a PUMA 560 robot

Constrained motion tasks involve interaction forces between the tool and the work-piece and demand certain amounts of compliance at either the tool or at the work-piece. We present a scheme to actively control the necessary compliance of the tool attached to the robot. This scheme allows the operator to specify the center of compliance and the three lateral and three angular compliances. The scheme uses a wrist-mounted, 6 axis force-torque sensor to measure the interaction forces. The necessary compliance is achieved by modifying the motion of the manipulator. This is achieved by programming a force-deflection relationship at the tool tip. This paper discusses the details of the algorithm, development of the control architecture for implementing the proposed scheme on an industrial robot, and real-time experimental results.

Intention-Aware Pedestrian Avoidance

A critical component of autonomous driving in urban environment is the vehicle’s ability to interact safely and intelligently with the human drivers and on-road pedestrians. This requires identifying the human intentions in real time based on a limited observation history and reacting accordingly. In the context of pedestrian avoidance, traditional approaches like proximity based reactive avoidance, or taking the most likely behavior of the pedestrian into account, often fail to generate a safe and successful avoidance strategy. This is mainly because they fail to take into account the human intention and the inherent uncertainty resulting in identifying such intentions from direct observations.

A general framework for road marking detection and analysis

Road markings are paintings on road surface to provide traffic guidance information for vehicles and pedestrians. In this paper, we propose a general framework for road marking detection and analysis, which is able to support various types of markings. Marking contours of different types are extracted indiscriminately from a image processing procedure, and sent to respective modules for independent classification and analysis. Four common types of markings are studied as examples in this paper, including lanes, arrows, zebra-crossings, and words. Our proposed method is tested through experiments, and shows good performance.

A Spatial-Temporal Approach for Moving Object Recognition with 2D LIDAR

Moving object recognition is one of the most fundamental functions for autonomous vehicles, which occupy an environment shared by other dynamic agents. We propose a spatial-temporal (ST) approach for moving object recognition using a 2D LIDAR. Our experiments show reliable performance. The contributions of this paper include: (i) the design of ST features for accumulated 2D LIDAR data; (ii) a real-time implementation for moving object recognition using the ST features.

Reinforcement learning of cooperative behaviors for multi-robot tracking of multiple moving targets

Traditional reinforcement learning algorithms learn based on discrete/finite states and actions, thus limit the learned behaviors to discrete/finite space. To address this problem, this paper introduces a distributed reinforcement learning controller that integrates reinforcement learning with behavior based control networks. This learning controller can enable the robot to generate appropriate control policy which combines different elementary behaviors. In addition, to address the problems in concurrent learning, a distributed learning control algorithm is proposed to coordinate concurrent learning processes. The distributed reinforcement learning controller and learning control algorithm are applied to multi-robot tracking of multiple moving targets. The efficacy is demonstrated by simulations.

Spatio-temporal motion features for laser-based moving objects detection and tracking.

This paper proposes a spatio-temporal motion feature detection and tracking method using range sensors working on a moving platform. The proposed spatio-temporal motion features are similar to optical flow but are extended on a moving platform with fusion of odometry and show much better classification accuracy with consideration of different uncertainties. In the proposal, the ego motion is compensated by odometry sensors and the laser scan points are accumulated and represented as space-time point clouds, from which the velocities and moving directions can be extracted. Based on these spatio-temporal features, a supervised learning technique is applied to classify the points as static or moving and Kalman filters are implemented to track the moving objects. A real experiment is performed during day and night on an autonomous vehicle platform and shows promising results in a crowded and dynamic environment.

Reactive, distributed layered architecture for resource-bounded multi-robot cooperation: application to mobile sensor network coverage

This paper describes a reactive, distributed layered architecture for cooperation of multiple resource-bounded robots, which is utilized in mobile sensor network coverage. In the upper layer, a dynamic task allocation scheme self-organizes the robot coalitions to track efficiently in separate regions. It uses the concepts of ant behavior to self-regulate the regional distributions of robots in proportion to that of the targets to be tracked in the changing environment. As a result, the adverse effects of task interference between robots are minimized and sensor network coverage is improved. In the lower layer, the robots use self-organizing neural networks to coordinate their target tracking within a region. Quantitative comparisons with other tracking strategies such as static sensor placements, potential fields, and auction-based negotiation show that our approach can provide better coverage and greater flexibility in responding to environmental changes.

Enhancing the reactive capabilities of integrated planning and control with Cooperative Extended Kohonen Maps

Despite the many significant advances made in robot motion research, few works have focused on the tight integration of high-level deliberative planning with reactive control at the lowest level. In particular, the real-time performance of existing integrated planning and control architectures is still not optimal because the reactive control capabilities have not been fully realized. This paper aims to enhance the low-level reactive capabilities of integrated planning and control with Cooperative Extended Kohonen Maps for handling complex, unpredictable environments so that the work-load of the high-level planner can be consequently eased. The enhancements include fine, smooth motion control, execution of more complex motion tasks such as overcoming unforeseen concave obstacles and traversing between closely spaced obstacles, and asynchronous execution of behaviors.

Flexible virtual fixture interface for path specification in tele-manipulation

We present the design and implementation of a flexible force-vision-based interface; allowing local operators to visually specify a path constraint to a remote robot manipulator in an on-line fashion during the teleoperation. Using bilateral and unilateral configurations, we compare our system to direct teleoperation through user studies. Three performance metrics (smoothness, error and execution time) and a subjective evaluation (NASA TLX) were used to quantify user performance. The trials show that our system outperforms direct teleoperation and reduces cognitive load. Our findings show that the performance of a unilateral teleop configuration with visual-force constraints surpass a bilateral teleop configuration in terms of displacement error and variance, as well as allowing users to complete tasks faster and with a smoother trajectory.