Proactive And Smooth Maneuvering For Navigation Around Pedestrians

Abstract

Navigation in close proximity with pedestrians is a challenge on the way to fully automated vehicles. Pedestrianfriendly navigation requires an understanding of pedestrian reaction and intention. Merely safety based reactive systems can lead to sub-optimal navigation solutions resulting in the freezing of the vehicle in many scenarios. Moreover, a strictly reactive method can produce unnatural driving patterns which cannot guarantee the legibility or social acceptance of the automated vehicle. This work presents a proactive maneuvering method adapted to navigation in close interaction with pedestrians using a dynamic channel approach. The method allows to proactively explore the navigation options based on anticipating pedestrians cooperation. The navigation is tested in frontal and lateral crossing scenarios with variable space density. The system is implemented under ROS, and compared with the probabilistic Risk-RRT planning method. The results are evaluated based on the safety and comfort of the pedestrians, and the quality of the vehicle's trajectory.

Evolution of Socially-Aware Robot Navigation

“…The trajectory optimization uses an elastic band approach and a least-squares problem is mapped into a hyper-graph representation to adjust the position and orientation of nodes and minimize the imposed constraints. [69] Predictive navigation performed in a global manner with the use of a POMDP Polynomial Neural Network (PNN) Future motion prediction Robot Navigation-HPOMDP (RN-HPOMDP) Rios-Martinez et al [73] RISK-RRT algorithm navigation Learned Gaussian Processes Personal Space Model of o-space in F-formations Park and Kuipers [71] The formulation of the kinematic control law The pose-following algorithm for smooth and comfortable motion of unicycle -type robots Park et al [72] Model Predictive Equilibrium Point Control (MPEPC) framework Du Toit and Burdick [42] Motion Planning Ferrer et al [52] SFM and prediction information Ferrer and Sanfeliu [74] Bayesian Human Motion Intentionality Prediction Sliding Window BHMIP (BHMIP) Two variants: the Sliding Window BHMIP and the Time Decay BHMIP Expectation-Maximization method Palm et al [75] Recognize the human intention with relative speeds Collision avoidance by extrapolation of human intentions and heading angle Compass dial Fuzzy rules for Human-Robot interactions Ferrer et al [76] Socially-aware navigation framework for allowing a robot to navigate accompanying the person Khambhaita and Alami [77] Cooperative navigation planner Trajectory Optimization: Elastic band Expectation-Maximization method Optimization framework Graph-based optimal solver Time-to-collision and directional constraints during optimization Kabtoul et al [78] Cooperative navigation planner…”

“…In the same way that we describe in the previous subsection, ignoring the cooperation between the mobile agents in the path planning step can lead to the freezing of the robot. In the proposal by Kabtoul et al [78], proactive and natural maneuvering is suggested for navigation around people. The approach consists of two main steps.…”

Uncertainty-aware Navigation in Crowded Environment

“…The safety distance measures the distance between the boundary of the robot's body and the pedestrians. The collision rate is the ratio of the number of collisions to the number of attempts (opposite of the success rate in [5]). The trajectory length and the time taken corresponds to the total displacement and time it took the robot to complete the task.…”

“…However, results from a long-term study of the workflow in some public environment also show that the actions of state-of-the-art service robots often fail to fulfil people's expectations [4]. The state of the art approaches that have been employed in path planning with a focus on social awareness, uncertainty and solving the Freezing Robot Problem -FRP, are usually an extension of multiagent collision avoidance and learning-based techniques with additional parameters that makes the algorithms socially compliant [5]. Classical motion planning algorithms generally employ the deterministic model by ignoring uncertainty, which may be sufficient in static environments or when an accurate information about future locations of the humans are available (an example is using motion capture cameras).…”

Towards Autonomous Robot Navigation in Human Populated Environments Using an Universal SFM and Parametrized MPC

“…All these extensions consider interactions with Humans (H2H), Obstacles (H2O), Robots (H2R), and, more generally, everything (H2X). Based on the H2X Social Force model, the SPACiSS simulator [7] was developed and recently used to simulate pedestrian behavior in the presence of an autonomous vehicle operating in a shared and open space in a proactive manner [8].…”