Scalable information-theoretic path planning for a rover-helicopter team in uncertain environments

Abstract

Mission-critical exploration of uncertain environments requires reliable and robust mechanisms for achieving information gain. Typical measures of information gain such as Shannon entropy and KL divergence are unable to distinguish between different bimodal probability distributions or introduce bias toward one mode of a bimodal probability distribution. The use of a standard deviation (SD) metric reduces bias while retaining the ability to distinguish between higher and lower risk distributions. Areas of high SD can be safely explored through observation with an autonomous Mars Helicopter allowing safer and faster path plans for ground-based rovers. First, this study presents a single-agent information-theoretic utility-based path planning method for a highly correlated uncertain environment. Then, an information-theoretic two-stage multiagent rapidly exploring random tree framework is presented, which guides Mars helicopter through regions of high SD to reduce uncertainty for the rover. In a Monte Carlo simulation, we compare our information-theoretic framework with a rover-only approach and a naive approach, in which the helicopter scouts ahead of the rover along its planned path. Finally, the model is demonstrated in a case study on the Jezero region of Mars. Results show that the information-theoretic helicopter improves the travel time for the rover on average when compared with the rover alone or with the helicopter scouting ahead along the rover’s initially planned route.

AlphaMobileSensing: A virtual testbed for mobile environmental monitoring

“…Navigation algorithm informs where the sensing agent needs to move to for the next measurements. Ideally, the navigation algorithm should lead the sensing agent to take the measurements that can generate the largest information gain about the environmental field (Folsom et al 2021). The field reconstruction algorithm needs to reconstruct the whole environment field from temporally and spatially discrete measurements (Unnikrishnan and Vetterli 2012).…”

Resiliency in Space Autonomy: a Review

“…In order to realize the concept of hyper-modality robots [47], risk-aware path planning [48] is necessary to be considered. Autonomous path planning algorithms for ground-based robotic manoeuvres on planetary exploration are documented in [49,50] and for ground and aerial robots in [51]. Autonomy for re-configurable robots is evolving as a revolutionary aspect of space robotics for a wide variety of locomotion [52,53], while the geometry-morphing aerial platforms focus on the design of the platform and the low-level control scheme to maintain its stability when shape reformation occurs during the flight [54,55].…”

Adaptive personalized routing for vulnerable road users

“…Rather than finding the shortest path, other routing studies have focused on algorithms that improve the satisfaction of users by learning and accommodating the users' preferences [26,27]. Other efforts include the information-theoretic version for navigation in highly correlated uncertain environments [28] and the social-force version that considers the influence of other people in navigation (e.g., evacuation in emergencies) [29]. A significant improvement to the shortest path algorithm is the adaptive routing algorithm.…”

“…Unfortunately, it can be challenging to properly handle transient obstacles (e.g., construction, wet surface), even those reported, due to their relatively short-term nature. However, current improved sidewalk monitoring tools (e.g., drones) can be tasked to specifically observe locations of reported transient obstacles to provide regular updates of their status [28,[48][49][50][51]. Also, transient obstacles can be automatically deleted from the database after a specific period (depending on the type and estimated duration) unless a user requests their deletion.…”