RCAMP: A resilient communication-aware motion planner for mobile robots with autonomous repair of wireless connectivity

Abstract

Abstract-Mobile robots, be it autonomous or teleoperated, require stable communication with the base station to exchange valuable information. Given the stochastic elements in radio signal propagation, such as shadowing and fading, and the possibilities of unpredictable events or hardware failures, communication loss often presents a significant mission risk, both in terms of probability and impact, especially in Urban Search and Rescue (USAR) operations. Depending on the circumstances, disconnected robots are either abandoned, or attempt to autonomously back-trace their way to the base station. Although recent results in Communication-Aware Motion Planning can be used to effectively manage connectivity with robots, there are no results focusing on autonomously re-establishing the wireless connectivity of a mobile robot without back-tracing or using detailed a priori information of the network.In this paper, we present a robust and online radio signal mapping method using Gaussian Random Fields, and propose a Resilient Communication-Aware Motion Planner (RCAMP) that integrates the above signal mapping framework with a motion planner. RCAMP considers both the environment and the physical constraints of the robot, based on the available sensory information. We also propose a self-repair strategy using RCMAP, that takes both connectivity and the goal position into account when driving to a connection-safe position in the event of a communication loss. We demonstrate the proposed planner in a set of realistic simulations of an exploration task in single or multi-channel communication scenarios.

3D multi-robot patrolling with a two-level coordination strategy

“…Update() 1 update idlenesses I (h) (t) and travel costs in G // asynchronous update through received messages and path planner feedback 2 update metric map M (h) and traversability map // asynchronous update through sensor callbacks 3 update team model T (h) // asynchronous update through received messages and path planner feedback 4 is node conf lict ← check if another robot in T (h) has the same goal node 5 is goal reached ← check if current goal has been reached by this robot 6 is goal visited ← check if current goal is visited by another robot // check by using received visited messages 7 is path planning f ailure ← check if path planner failed to compute a path to goal // check continuous replanning 8 is critical path planning f ailure ← check if path planning failure is lasting more than T pcr 9 is critical node conf lict ← check if robot is experiencing node conflicts for more than a time interval T ncr 10 is node visited ← check if a non-goal node is visited by this robot while reaching the current goal 11 if is node visited then 12 node ← get node visited along the way 13 broadcast node is visited // inform teammates about the non-goal node visit 14 end 15 broadcast idleness message with pre-fixed frequency 1/T idln On the other hand, if the robot is still reaching the current goal node, lines 11-20 are executed. If a path planner failure, a node conflict (see Section 6.2), or a node visit (see Section 6.1) occurs on the selected goal (line 11), the patrolling agent first sends a goal abort to the path planner, next broadcasts its decision and then triggers a new node selection (lines 12-16).…”

“…Once an initial solution is found, the robot starts moving toward its goal (line 14) along the computed path. In this process, the path planner continuously replans a new path by using the most updated information (lines [11][12][13][14][15][16][17][18][19][20]. Since the environment is assumed to be dynamic and populated by moving robots, a path planning failure can be verified by the local path planner during its continuous replanning, even after an initial solution is found by the global path planner.…”

“…In order to use this representation for navigation and patrolling, a 'clamping policy' is adopted by setting a lower and upper bound on the log-likelihood of the occupancy estimate in the OctoMap. The final decision about occupancy is made by thresholding this bounded estimate which ensures that the 3D map representation can quickly adapt to changes in the environment 13 .…”

An Auto-Adaptive Multi-Objective Strategy for Multi-Robot Exploration of Constrained-Communication Environments

“…Unfortunately, due to uncertain and incomplete knowledge, the Γ i function only can either confirm the absence of connectivity or deliver an optimistic estimation of connectivity level instead. Although this model represents a valuable improvement in relation to others (e.g., the classic disk or line of sight models [20]), for the sake of simplicity other impairments also common in communication (e.g., bandwidth, information losses, fading, and multi-path propagation phenomenon [39,40]) are not considered in this work.…”

Self-optimization of resilient topologies for fallible multi-robots

“…Despite the real-world experiment and encouraging performance, this problem still turns out to be NP-Hard. In another alternative approach to a similar problem, Caccamo et al [43] proposed a communication-aware motion planner "with autonomous repair of wireless connectivity". Yet, this work relies on the existence of fixed-location access points.…”