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Abstract—In many scenarios for informative path planning done by ground robots or drones, certain types of information are significantly more valuable than others. For example, in the precision agriculture context, detecting plant disease outbreaks can prevent costly crop losses. Quite often, there is a limit on the exploration budget, which does not allow for a detailed investigation of every location. In this paper, we propose Learned Adaptive Inspection Paths (LAIP), a methodology to learn policies that handle such scenarios by combining uniform sampling with close inspection of areas where high-value information is likely to be found. LAIP combines Q-learning in an offline reinforcement learning setting, careful engineering of the state representation and reward system, and a training regime inspired by the teacher-student curriculum learning model. We found that a policy learned with LAIP outperforms traditional approaches in low-budget scenarios. 

Communication is arguably the most important way to enable cooperation among multiple robots. In numerous such settings, robots exchange local sensor measurements to form a global perception of the environment. One example of this setting is adaptive multi-robot informative path planning, where robots’ local measurements are “fused” using probabilistic techniques (e.g., Gaussian process models) for more accurate prediction of the underlying ambient phenomena. In an adversarial setting, in which we assume a malicious entity–-the adversary-–can modify data exchanged during inter-robot communications, these cooperating robots become vulnerable to data integrity attacks. Such attacks on a multi-robot informative path planning system may, for example, replace the original sensor measurements with fake measurements to negatively affect achievable prediction accuracy. In this paper, we study how such an adversary may design data integrity attacks using a Generative Adversarial Network (GAN). Results show the GAN-based techniques learning spatial patterns in training data to produce fake measurements that are relatively undetectable yet significantly degrade prediction accuracy. 

In this work, we propose a blockchain-based solution for securing robot-to-robot communication for a task with a high socioeconomic impact—information gathering. The objective of the robots is to gather maximal information about an unknown ambient phenomenon such as soil humidity distribution in a field. More specifically, we use the proof-of-work (PoW) consensus protocol for the robots to securely coordinate while rejecting tampered data injected by a malicious entity. As the blockchain-based PoW protocol has a large energy footprint, we next employ an algorithmically-engineered energy-efficient version of PoW. Results show that our proposed energy-efficient PoW-based protocol can reduce energy consumption by 14% while easily scaling up to 10 robots.