Sampling-based Motion Planning with 3D Simultaneous Localization and Mapping for Social Aware Service Robotics Applications

In the present work, an socially aware autonomous navigation framework and motion planning algorithm for indoor service robot are proposed. The entirety of our work is based on Robot Operating System (ROS), which serves as the Linux-based middle-ware on which all robotics applications execute and communicate with each other based on an unified formats of messages, and allows sharing of information within a cluster of devices. Within ROS, our navigational framework consists of integrating layered planning scheme, layered costmap update (a ROS concept based on [2]), mapping and localization, as well as base control into a viable navigation system. The social aware functionality is implemented via an establishment of dynamic costmap registering the proxemics of each perceived individual by the robot’s onboard Laser Scan as well as RGB-D sensors. With the aid of sensorial fusion perception of humans within highly dynamic configuration space poses high demand for more agile and flexible motion planning algorithms as well as faster people tracking techniques to ensure safer interaction. For such purpose, this work presents a novel biased sampling-based planning approach which displays both the Anytime Dynamic planning characteristics of search-based path planning algorithms with the computational simplicity of single-query sampling-based approach. We evaluate our motion planning algorithm with 3 major benchmarks: one simulated environment and two real-world scenarios involving partially explored and fully explored maps of the same maze-like indoor space. We compare our proposed algorithm ADRRT* with several major search-based and sampling-based algorithms in terms of the spent cost and computation time on each iteration. And on average, our algorithm not only consumes cost which ranges 8.5% to 16.7% less than its counterparts, but also occupies les than comvi pared to as much as 58.17% to RRT and 95% to RRT* in non-convex environement such as Room 302 of our laboratory. While our technique proves to yield faster and less costly trajectories, the layered costmaps should also be effectively updated to ensure the collision avoidance both with static obstacles as well as people at the robot’s surroundings on a real-time basis. The dynamic costmap layers registers the perceived people’s poses in terms of Gaussian pose estimate, representing the proxemics of each individual. With the notion of proxemics, the navigation framework respects the personal space of others and maneuver according to social norms, an important feature to regulate robotic behavior in order to integrate robots into our society. Such study also serves as springboard toward future works related not limited to human-robot-interaction, multi-robot exploration, and further integration of robotics with the Internet of Things (IoT) framework.