Scheduled perception for energy-efficient path following

Abstract

This paper explores the idea of reducing a robot's energy consumption while following a trajectory by turning off the main localisation subsystem and switching to a lowerpowered, less accurate odometry source at appropriate times. This applies to scenarios where the robot is permitted to deviate from the original trajectory, which allows for energy savings. Sensor scheduling is formulated as a probabilistic belief planning problem. Two algorithms are presented which generate feasible perception schedules: the first is based upon a simple heuristic; the second leverages dynamic programming to obtain optimal plans. Both simulations and real-world experiments on a planetary rover prototype demonstrate over 50% savings in perception-related energy, which translates into a 12% reduction in total energy consumption. I. INTRODUCTIONRobots require energy to operate. Yet they only have access to limited energy storage during missions. As we extend the reach of autonomous systems to operate in remote locations, over long distances and for long periods of time, energy considerations are becoming increasingly important. To date, these considerations are often brought to bear in schemes where trajectories or speed profiles are optimised to minimise the energy required for actuation (see, for example, [1], [2], [3]). Here we take a different, yet complementary, approach in considering the energy expenditure for sensing (and, implicitly, computation) associated with navigation. In particular, our goal is to activate the perception system only as required to maintain the vehicle within a given margin around a predetermined path. As the main navigation sensors are switched off and the robot reverts to a lower-powered, less accurate odometry source for parts of the trajectory, any associated computation will also be reduced, leading to further savings in energy.Naively, such perception schedules could be constructed by switching sensors on and off randomly or according to, for example, a fixed frequency. This does, however, suffer the drawback that no heed is paid to drift in the robot's position with respect to the original trajectory: it may not be desirable to deviate by more than an allowed margin from the predetermined route. This arises, for example, in a planetary exploration scenario when conducting long traverses over featureless terrain. Other possible considerations include traversability, obstacles, and the robustness of the localisation system to deviations from the original path. Such naive approaches would also need to be tuned to individual trajectories as savings would depend significantly on trajectory shape. In this work we present two approaches which explicitly account for drift and trajectory shape (though the

Dec-MCTS: Decentralized planning for multi-robot active perception

“…An interesting avenue for future work would be to develop a communication-planning algorithm that selects when to communicate and who to communicate to while running Dec-MCTS in scenarios with limited communication bandwidth. A possible approach could be a dynamic programming formulation for planning communication to maintain coordination in a similar way to how Ondruska et al (2015) planned the use of navigation hardware to maintain localization accuracy. Other communication-planning formulations that may be useful here include those of Kassir et al (2015) and Lindhé and Johansson (2013).…”

Planning-Aware Communication for Decentralised Multi-Robot Coordination

“…Our approach is motivated by the belief-space planner for scheduling localisation hardware usage [30], which aims to conserve energy during a path-following scenario. Our belief graph is generated as a directed acyclic graph (DAG) in a similar way, except we use a particle filter relevant to our application rather than a linear Gaussian model.…”

From Bits of Data to Bits of Knowledge—An On-Board Classification Framework for Wearable Sensing Systems

“…The concept of hierarchical classification itself has been proposed for sound classification in the context of smartphones [10] and smart vehicles [11]. In turn, intelligent duty cycling of high-power sensing elements, such as gyroscopes, GPS receivers and cameras, has been proposed as a means to extend the battery lifetime of smartphones [12,13] and mobile robots [14]. Different to these works, we target severely resource-constrained devices [15] (20kB RAM, 128kB Flash, and energy budgets of approximately 100 mAh).…”