Computer and Robot Vision Vol. 1, by R.M. Haralick and Linda G. Shapiro, Addison-Wesley, 1992, ISBN 0-201-10887-1.

Computer and Robot Vision

https://hal.archives-ouvertes.fr/hal-01684295/document

Human-aware robot navigation: A survey

We review a range of techniques related to navigation of unmanned vehicles through unknown environments with obstacles, especially those that rigorously ensure collision avoidance (given certain assumptions about the system). This topic continues to be an active area of research, and we highlight some directions in which available approaches may be improved. The paper discusses models of the sensors and vehicle kinematics, assumptions about the environment, and performance criteria. Methods applicable to stationary obstacles, moving obstacles and multiple vehicles scenarios are all reviewed. In preference to global approaches based on full knowledge of the environment, particular attention is given to reactive methods based on local sensory data, with a special focus on recently proposed navigation laws based on model predictive and sliding mode control.

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Algorithms for collision-free navigation of mobile robots in complex cluttered environments: a survey

This paper describes a concept for a collision avoidance system for ships, which is based on model predictive control. A finite set of alternative control behaviors are generated by varying two parameters: offsets to the guidance course angle commanded to the autopilot and changes to the propulsion command ranging from nominal speed to full reverse. Using simulated predictions of the trajectories of the obstacles and ship, compliance with the Convention on the International Regulations for Preventing Collisions at Sea and collision hazards associated with each of the alternative control behaviors are evaluated on a finite prediction horizon, and the optimal control behavior is selected. Robustness to sensing error, predicted obstacle behavior, and environmental conditions can be ensured by evaluating multiple scenarios for each control behavior. The method is conceptually and computationally simple and yet quite versatile as it can account for the dynamics of the ship, the dynamics of the steering and propulsion system, forces due to wind and ocean current, and any number of obstacles. Simulations show that the method is effective and can manage complex scenarios with multiple dynamic obstacles and uncertainty associated with sensors and predictions.

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Ship Collision Avoidance and COLREGS Compliance Using Simulation-Based Control Behavior Selection With Predictive Hazard Assessment

This paper introduces the COMPANION framework: a Constraint-Optimizing Method for Person-Acceptable NavigatION. In this framework, human social conventions, such as personal space and tending to one side of hallways, are represented as constraints on the robot's navigation. These constraints are accounted for at the global planning level. In this paper, we present the rationale for, and implementation of, this framework, and we describe the experiments we have run in simulation to verify that the method produces human-like behavior in a mobile robot. Our approach is novel in that it can express an arbitrary number of social conventions and explicitly accounts for these conventions in the planning phase.

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COMPANION: A Constraint-Optimizing Method for Person-Acceptable Navigation

Robot navigation in very cluttered environments by preference-based fuzzy behaviors

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.

Human-aware robot motion planning with velocity constraints

The virtual wall approach to limit cycle avoidance for unmanned ground vehicles

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 formulation of MPC readily applies to nonlinear systems and avoids the local minima which can cause a vehicle to become immobilized behind obstacles. The SBMPC algorithm is applied to an AUV in a cluttered environment and an AUV in a common local minima problem.

Motion planning for an autonomous Underwater Vehicle via Sampling Based Model Predictive Control

Path planning is a method that determines a path, consecutive states, between a start state and goal state, LaValle (2006). However, in motion planning that path must be parameterized by time to create a trajectory. Consequently, not only is the path determined, but the time the vehicle moves along the path. To be successful at motion planning, a vehicle model must be incorporated into the trajectory computation. The motivation in utilizing a vehicle model is to provide the opportunity to predict the vehicle’s motion resulting from a variety of system inputs. The kinematic model enforces the vehicle kinematic constraints (i.e. turn radius, etc.), on the vehicle that limit the output space (state space). However, the kinematic model is limited because it does not take into account the forces acting on the vehicle. The dynamic model incorporates more useful information about the vehicle’s motion than the kinematic model. It describes the feasible control inputs, velocities, acceleration and vehicle/terrain interaction phenomena. Motion planning that will require the vehicle to perform close to its limits (i.e. extreme terrains, frequent acceleration, etc.) will need the dynamic model. Examples of missions that would benefit from using a dynamic model in the planning are time optimal motion planning, energy efficient motion planning and planning in the presence of faults, Yu et al. (2010). Sampling-based methods represent a type of model based motion planning algorithm. These methods incorporate the system model. There are current sampling-based planners that should be discussed: The Rapidly-Exploring Random Tree (RRT) Planner, Randomized A (RA) algorithm, and the Synergistic Combination of Layers of Planning (SyCLoP) multi-layered planning framework. The Rapidly-Exploring Random Tree Planner was one of the first single-query sampling based planners and serves as a foundation upon which many current algorithms are developed. The RRT Planner is very efficient and has been used in many applications including manipulator path planning, Kuffner & LaValle. (2000), and robot trajectory planning, LaValle & Kuffner (2001). However, the RRT Planner has the major drawback of lacking any sort of optimization other than a bias towards exploring the search space. The RA algorithm, which was designed based on the RRT Planner, addresses this drawback by combining the RRT Planner with an A algorithm. The SyCLoP framework is 11

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Damion D. Dunlap

Papers

Robot navigation in very cluttered environments by preference-based fuzzy behaviors

Human-aware robot motion planning with velocity constraints

The virtual wall approach to limit cycle avoidance for unmanned ground vehicles

Motion planning for an autonomous Underwater Vehicle via Sampling Based Model Predictive Control

Motion Planning for Mobile Robots Via Sampling-Based Model Predictive Optimization