Learning a Group-Aware Policy for Robot Navigation

Human-aware robot navigation promises a range of applications in which mobile robots bring versatile assistance to people in common human environments. While prior research has mostly focused on modeling pedestrians as independent, intentional individuals, people move in groups; consequently, it is imperative for mobile robots to respect human groups when navigating around people. This paper explores learning group-aware navigation policies based on dynamic group formation using deep reinforcement learning. Through simulation experiments, we show that group-aware policies, compared to baseline policies that neglect human groups, achieve greater robot navigation performance (e.g., fewer collisions), minimize violation of social norms and discomfort, and reduce the robot's movement impact on pedestrians. Our results contribute to the development of social navigation and the integration of mobile robots into human environments.Comment: 8 pages, 4 figure

A reactive collision avoidance approach for mobile robot in dynamic environments

This paper describes a novel reactive obstacle avoidance approach for mobile robot navigation in unknown and dynamic environment. This approach is developed based on the “situated-activity paradigm” and a “divide and conquer” strategy which steers the robot to move among unknown obstacles and towards a target without collision. The proposed approach entitled the Virtual Semi-Circles(VSC). The VSC approach lies in integration of 4 modules: division, evaluation, decision and motion generation. The VSC proposes a comprehensive obstacle avoidance approach for robust and reliable mobile robot navigation in cluttered, dense and complex unknown environments. The simulation result shows the feasibility and effectiveness of the proposed approach

Real-Time Navigation for Bipedal Robots in Dynamic Environments

The popularity of mobile robots has been steadily growing, with these robots being increasingly utilized to execute tasks previously completed by human workers. Bipedal robots, a subset of mobile robots, have been a popular field of research due to the large range of tasks for which they can be utilized. For bipedal robots to see a similarly successful integration into society, robust autonomous navigation systems need to be designed. These autonomous navigation systems can generally be divided into three components: perception, planning, and control. A holistic navigation system for bipedal robots must successfully integrate all three components of the autonomous navigation problem to enable robust real-world navigation. Many works expand on fundamental planning algorithms such as A*, RRT, and PRM to address the unique problems of bipedal motion planning. However, many of these works lack several components required for autonomous navigation systems such as real-time perception, mapping, and localization processes. Thus, the goal of this research is to develop a real-time navigation system for bipedal robots in dynamic environments which addresses all components of the navigation problem. To achieve this: a depth-based sensor suite was used for obstacle segmentation, mapping, and localization. Additionally, a two-stage planning system generates collision-free and kinematically feasible trajectories robust to unknown and dynamic environments. Finally, the Digit bipedal robot's default low-level controller is used to execute these feasible trajectories. The navigation system was first validated on a differential drive robot in simulation. Next, the system was adapted for bipedal robots and validated in hardware on the Digit bipedal robot. In both simulation and in hardware experiments, the implemented navigation system facilitated successful navigation in unknown environments and in environments with both static and dynamic obstacles.Undergraduate Honors Committee in the College of EngineeringNo embargoAcademic Major: Computer Science and Engineerin

Topomap: Topological Mapping and Navigation Based on Visual SLAM Maps

Visual robot navigation within large-scale, semi-structured environments deals with various challenges such as computation intensive path planning algorithms or insufficient knowledge about traversable spaces. Moreover, many state-of-the-art navigation approaches only operate locally instead of gaining a more conceptual understanding of the planning objective. This limits the complexity of tasks a robot can accomplish and makes it harder to deal with uncertainties that are present in the context of real-time robotics applications. In this work, we present Topomap, a framework which simplifies the navigation task by providing a map to the robot which is tailored for path planning use. This novel approach transforms a sparse feature-based map from a visual Simultaneous Localization And Mapping (SLAM) system into a three-dimensional topological map. This is done in two steps. First, we extract occupancy information directly from the noisy sparse point cloud. Then, we create a set of convex free-space clusters, which are the vertices of the topological map. We show that this representation improves the efficiency of global planning, and we provide a complete derivation of our algorithm. Planning experiments on real world datasets demonstrate that we achieve similar performance as RRT* with significantly lower computation times and storage requirements. Finally, we test our algorithm on a mobile robotic platform to prove its advantages.Comment: 8 page

Symbiotic Navigation in Multi-Robot Systems with Remote Obstacle Knowledge Sharing

Large scale operational areas often require multiple service robots for coverage and task parallelism. In such scenarios, each robot keeps its individual map of the environment and serves specific areas of the map at different times. We propose a knowledge sharing mechanism for multiple robots in which one robot can inform other robots about the changes in map, like path blockage, or new static obstacles, encountered at specific areas of the map. This symbiotic information sharing allows the robots to update remote areas of the map without having to explicitly navigate those areas, and plan efficient paths. A node representation of paths is presented for seamless sharing of blocked path information. The transience of obstacles is modeled to track obstacles which might have been removed. A lazy information update scheme is presented in which only relevant information affecting the current task is updated for efficiency. The advantages of the proposed method for path planning are discussed against traditional method with experimental results in both simulation and real environments

A layered fuzzy logic controller for nonholonomic car-like robot

A system for real time navigation of a nonholonomic car-like robot in a dynamic environment consists of two layers is described: a Sugeno-type fuzzy motion planner; and a modified proportional navigation based fuzzy controller. The system philosophy is inspired by human routing when moving between obstacles based on visual information including right and left views to identify the next step to the goal. A Sugeno-type fuzzy motion planner of four inputs one output is introduced to give a clear direction to the robot controller. The second stage is a modified proportional navigation based fuzzy controller based on the proportional navigation guidance law and able to optimize the robot's behavior in real time, i.e. to avoid stationary and moving obstacles in its local environment obeying kinematics constraints. The system has an intelligent combination of two behaviors to cope with obstacle avoidance as well as approaching a target using a proportional navigation path. The system was simulated and tested on different environments with various obstacle distributions. The simulation reveals that the system gives good results for various simple environments

Navite: A Neural Network System For Sensory-Based Robot Navigation

A neural network system, NAVITE, for incremental trajectory generation and obstacle avoidance is presented. Unlike other approaches, the system is effective in unstructured environments. Multimodal inforrnation from visual and range data is used for obstacle detection and to eliminate uncertainty in the measurements. Optimal paths are computed without explicitly optimizing cost functions, therefore reducing computational expenses. Simulations of a planar mobile robot (including the dynamic characteristics of the plant) in obstacle-free and object avoidance trajectories are presented. The system can be extended to incorporate global map information into the local decision-making process.Defense Advanced Research Projects Agency (AFOSR 90-0083); Office of Naval Research (N00014-92-J-l309); Consejo Nacional de Ciencia y Tecnología (63l462