Robot Collision Avoidance with a Guaranteed Safety Zone and Randomized Symmetry Breaking

Collision avoidance of moving systems is a wellstudiedproblem. The use of an Artificial Potential Field functionis a popular approach to compute in real time a path that avoidscollision between agents. It involves the minimization of aweighted sum of an attractive force and a repulsive force.Previous studies consider these weights to be fixed designparameters, to be determined experimentally. In particular, theseparameters do not change during the run of the algorithm. Ourmain result is based on the observation that by dynamicallychanging these parameters one can obtain a guarantee on aminimum safety distance between the agents. Specifically, if theagents compute their path by minimizing the potential field withproperly chosen weights, there will always be a guaranteed safetydistance between each pair of agents. Our earlier studies showpromising experimental results and we extended the studies onavoiding trajectory symmetry.Our simulation validates ourmodel and demonstrated its effectiveness for a group of noncooperativeagents moving in a small area

Real-time probabilistic collision avoidance for autonomous vehicles, using order reductive conflict metrics

Thesis (Ph. D.)--Massachusetts Institute of Technology, Dept. of Aeronautics and Astronautics, 2003.Includes bibliographical references (p. 131-137).Contemporary collision avoidance systems such as the Traffic Alert and Collision Avoidance System (TCAS) have proven their effectiveness in the Commercial Aviation (CA) industry within the last decade. Yet, TCAS and many systems like it represent attempts at collision avoidance that do not fully recognize the uncertain nature of a conflict event. Most systems circumvent probabilistic representation through simplifying approximations and pre-compiled notions of hazard space, since probabilistic representation of collision in three dimensions is considered to be an intractable problem. Recent developments by Kuchar and Yang[70] and Paielli and Erzberger[50] have shown that collision avoidance may be cast as a probabilistic state-space problem. Innovative solution approaches may then allow systems of this nature to probe collision risk in real-time, based on real-time state estimates. The research documented in this thesis further develops the probabilistic approach for the non-cooperative, two-vehicle problem as applied in real-time to autonomous aircraft. The research is kept in a general form, thereby warranting application to a wide variety of multi-dimensional collision avoidance applications and scenario geometries. The work primarily improves the state of the art through the creation of order reductive collision metrics in order to simplify the intractable problem of multi-dimensional collision risk calculation. As a result, a tractable, real-time, probabilistic algorithm is developed for the calculation of collision risk as a function of time.(cont.) The collision avoidance problem is contextualized not only within the realm of recent research within the CA industry, but is also likened to such concepts as the first passage time problem encountered in physics, and the field of reliability theory often encountered in civil and mechanical engineering problems. Yang's method of solution, a piece-wise straight-line Monte-Carlo approach to state propagation, is extended with a model-predictive, finite horizon risk accumulation algorithm. Through this extension we are capable of modelling collision risk for linear(-ized), time-variant, dynamic vehicle models and control strategies. A strategy is developed whereby the advantage of delayed collision avoidance action is calculated and it is framed as an extension of the notion of system operating characteristics (SOCs). The complexity of the probabilistic representation is reduced by application of quadratic conflict metrics. The numerical complexity can be reduced from [Omicron](N2n) to [Omicron](Nlog2(N)) at each time step within a finite horizon time interval. Risk calculation errors due to numerical and stochastic approximations are quantified. An applicability test is also devised whereby a vehicle's dynamic model and control characteristics may be used to calculate risk error estimates before implementing the bulk of the algorithmic solution. Various other applications of the work, outside the scope of collision avoidance, are also identified.by Thomas Jones.Ph.D

Cooperative Material Handling by Human and Robotic Agents:Module Development and System Synthesis

In this paper we present the results of a collaborative effort to design and implement a system for cooperative material handling by a small team of human and robotic agents in an unstructured indoor environment. Our approach makes fundamental use of human agents\u27 expertise for aspects of task planning, task monitoring, and error recovery. Our system is neither fully autonomous nor fully teleoperated. It is designed to make effective use of human abilities within the present state of the art of autonomous systems. It is designed to allow for and promote cooperative interaction between distributed agents with various capabilities and resources. Our robotic agents refer to systems which are each equipped with at least one sensing modality and which possess some capability for self-orientation and/or mobility. Our robotic agents are not required to be homogeneous with respect to either capabilities or function. Our research stresses both paradigms and testbed experimentation. Theory issues include the requisite coordination principles and techniques which are fundamental to the basic functioning of such a cooperative multi-agent system. We have constructed a testbed facility for experimenting with distributed multi-agent architectures. The required modular components of this testbed are currently operational and have been tested individually. Our current research focuses on the integration of agents in a scenario for cooperative material handling

Uncertainty and social considerations for mobile assistive robot navigation

An increased interest in mobile robots has been seen over the past years. The wide range of possible applications, from vacuum cleaners to assistant robots, makes such robots an interesting solution to many everyday problems. A key requirement for the mass deployment of such robots is to ensure they can safely navigate around our daily living environments. A robot colliding with or bumping into a person may be, in some contexts, unacceptable. For example, if a robot working around elderly people collides with one of them, it may cause serious injuries. This thesis explores four major components required for effective robot navigation: sensing the static environment, detection and tracking of moving people, obstacle and people avoidance with uncertainty measurement, and basic social navigation considerations. First, to guarantee adherence to basic safety constraints, sensors and algorithms required to measure the complex structure of our daily living environments are explored. Not only do the static components of the environment have to be measured, but so do any people present. A people detection and tracking algorithm, aimed for a crowded environment is proposed, thus enhancing the robot's perception capabilities. Our daily living environments present many inherent sources of uncertainty for robots, one of them arising due to the robot's inability to know people's intentions as they move. To solve this problem, a motion model that assumes unknown long-term intentions is proposed. This is used in conjunction with a novel uncertainty aware local planner to create feasible trajectories. In social situations, the presence of groups of people cannot be neglected when navigating. To avoid the robot interrupting groups of people, it first needs to be able to detect such groups. A group detector is proposed which relies on a set of gaze- and geometric-based features. Avoiding group disruption is finally incorporated into the navigation algorithm by means of taking into account the probability of disrupting a group's activities. The effectiveness of the four different components is evaluated using real world and simulated data, demonstrating the benefits for mobile robot navigation.Open Acces

Preference-Based Trajectory Generation

Peer Reviewedhttp://deepblue.lib.umich.edu/bitstream/2027.42/76820/1/AIAA-36214-892.pd